Correlation between parameters of volumetric capnography and spirometry during a submaximal exercise protocol on a treadmill in patients with cystic fibrosis and healthy controls

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"apellidos" => "Lima" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2531043718300369?idApp=UINPBA00004E" "url" => "/25310437/0000002500000001/v1_201902260629/S2531043718300369/v1_201902260629/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "Original article" "titulo" => "Correlation between parameters of volumetric capnography and spirometry during a submaximal exercise protocol on a treadmill in patients with cystic fibrosis and healthy controls" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "21" "paginaFinal" => "31" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "P.L.F. Parazzi, F.A.L. Marson, M.A.G.O. Ribeiro, C.I.S. Schivinski, J.D. Ribeiro" "autores" => array:5 [ 0 => array:4 [ "nombre" => "P.L.F." "apellidos" => "Parazzi" "email" => array:1 [ 0 => "palomaparazzi@yahoo.com.br" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:4 [ "nombre" => "F.A.L." "apellidos" => "Marson" "email" => array:1 [ 0 => "fernandolimamarson@hotmail.com" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 2 => array:3 [ "nombre" => "M.A.G.O." "apellidos" => "Ribeiro" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "C.I.S." "apellidos" => "Schivinski" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 4 => array:3 [ "nombre" => "J.D." "apellidos" => "Ribeiro" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Department of Pediatrics, Faculty of Medical Sciences, University of Campinas, Tessália Vieira de Camargo, 126, Cidade Universitária “Zeferino Vaz”, Postal Code: 13083-887 Campinas, São Paulo, Brazil" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Medical Genetics, Faculty of Medical Sciences, University of Campinas, Tessália Vieira de Camargo, 126, Cidade Universitária “Zeferino Vaz”, Postal Code: 13083-887 Campinas, São Paulo, Brazil" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Center of Physical Education and Sports, State University of Santa Catarina, Postal Code: 88080-350 Coqueiros, Florianópolis, Santa Catarina, Brazil" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding authors." ] ] ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1282 "Ancho" => 3000 "Tamanyo" => 475775 ] ] "descripcion" => array:1 [ "en" => "Correlation between slope 3/PetCO2 and spirometry markers. (A) FEV1: [All subjects] (Moment 1) CC=−0.384**, p-value<0.001; (Moment 2) CC=−0.525**, p-value<0.001; (Moment 3) CC=−0.512**, p-value<0.001; (Moment 4) CC=−0.463**, p-value<0.001; (Moment 5) CC=−0.431**, p-value<0.001; [CFG] (Moment 1) CC=−0.351**, p-value=0.005; (Moment 2) CC=−0.499**, p-value<0.001; (Moment 3) CC=−0.503, p-value<0.001; (Moment 4) CC=−0.280, p-value=0.025; (Moment 5) CC=−0.353**, p-value=0.004; [CG] (Moment 1) CC=−0.093, p-value=0.467; (Moment 2) CC=−0.313*, p-value=0.012; (Moment 3) CC=−0.173, p-value=0.172; (Moment 4) CC=−0.239, p-value=0.058; (Moment 5) CC=−0.156, p-value=0.219. B. FVC: [All subjects] (Moment 1) CC=−0.336**, p-value<0.001; (Moment 2) CC=−0.435**, p-value<0.001; (Moment 3) CC=−0.409**, p-value<0.001; (Moment 4) CC=−0.361**, p-value<0.001; (Moment 5) CC=−0.342**, p-value<0.001; [CFG] (Moment 1) CC=0.341**, p-value=0.006; (Moment 2) CC=−0.457**, p-value<0.001; (Moment 3) CC=−0.438, p-value<0.001; (Moment 4) CC=−0.238, p-value=0.058; (Moment 5) CC=−0.327**, p-value=0.008; [CG] (Moment 1) CC=0.036, p-value=0.780; (Moment 2) CC=−0.131, p-value=0.302; (Moment 3) CC=0.066, p-value=0.603; (Moment 4) CC=−0.044, p-value=0.730; (Moment 5) CC=0.044, p-value=0.727. C. FEV1/FVC: [All subjects] (Moment 1) CC=−0.292**, p-value=0.001; (Moment 2) CC=−0.298**, p-value=0.001; (Moment 3) CC=−0.289**, p-value=0.001; (Moment 4) CC=−0.275**, p-value=0.002; (Moment 5) CC=−0.252**, p-value=0.004; [CFG] (Moment 1) CC=−0.177, p-value=0.162; (Moment 2) CC=−0.346**, p-value=0.005; (Moment 3) CC=−0.228, p-value=0.070; (Moment 4) CC=−0.230, p-value=0.068; (Moment 5) CC=−0.134, p-value=0.290; [CG] (Moment 1) CC=−0.119, p-value=0.349; (Moment 2) CC=0.077, p-value=0.543; (Moment 3) CC=0.092, p-value=0.471; (Moment 4) CC=0.278*, p-value=0.026; (Moment 5) CC=0.133, p-value=0.293. CC, correlation coefficient; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; slope 3/PetCO2, slope of phase 3 normalized by end-tidal carbon dioxide tension; CFG, group with cystic fibrosis (N=64); CG, control group (N=64). All subjects, patients with cystic fibrosis+control subjects (N=128). ** The correlation is significant at the 0.01 level (2 ends). * The correlation is significant at the 0.05 level (2 ends). Alpha=0.05. Statistical analysis was performed using Spearman correlation (Rho). In the legend is presented the correlation coefficient of Spearman's rank. Data marked with * and ** showed statistically significant p-value." ] ] ] "textoCompleto" => "IntroductionPatients with cystic fibrosis (CF) (OMIM: #<a id="intr0005" class="elsevierStyleInterRef" href="omim:219700">219700</a>) from birth, show a progressive deterioration of lung structures,<a class="elsevierStyleCrossRefs" href="#bib0175">1,2</a> with functional changes that are perpetuated throughout life. These include decreased: peripheral capillary oxygen saturation (SpO2), the ratio of anatomic dead space and expired tidal volume (VD/VT), forced expiratory volume in one second (FEV1) and the ability to perform exercise. Moreover, increased respiratory rate (RR), heart rate (HR) and partial pressure of exhaled carbon dioxide (PetCO2) in arterial blood have been shown.<a class="elsevierStyleCrossRefs" href="#bib0180">2–4</a>Challenges in the management of lung disease in CF include the early detection of lung damage, monitoring of disease progression and the identification and validation of parameters to evaluate response to treatment.<a class="elsevierStyleCrossRefs" href="#bib0195">5–7</a> In this context, studies have proposed the use of new markers and instruments, such as clinical scores,<a class="elsevierStyleCrossRef" href="#bib0210">8</a> computed tomography,<a class="elsevierStyleCrossRef" href="#bib0200">6</a> sputum culture or endotracheal secretion,<a class="elsevierStyleCrossRef" href="#bib0215">9</a> lung clearance index,<a class="elsevierStyleCrossRef" href="#bib0220">10</a> functional tests [six-minute walk test (6-MWT)<a class="elsevierStyleCrossRef" href="#bib0225">11</a> and treadmill tests<a class="elsevierStyleCrossRef" href="#bib0190">4</a>], transepithelial potential difference,<a class="elsevierStyleCrossRef" href="#bib0230">12</a> exhaled nitric oxide,<a class="elsevierStyleCrossRef" href="#bib0235">13</a> impulse oscillometry,<a class="elsevierStyleCrossRef" href="#bib0240">14</a> condensed exhaled<a class="elsevierStyleCrossRef" href="#bib0245">15</a> and volumetric capnography (VCap),<a class="elsevierStyleCrossRefs" href="#bib0190">4,5,7,16</a> with the latter recently being considered a promising useful tool in the assessment of functional impairment of the lungs.<a class="elsevierStyleCrossRefs" href="#bib0190">4,5,7,16</a> Also, until now, spirometry has been considered the best lung function test to monitor lung disease progression in CF for more than two decades, but the VCap has emerged as a feasible new tool.<a class="elsevierStyleCrossRefs" href="#bib0195">5–7,16</a>The VCap may reflect the increased alveolar heterogeneity with the evolution of lung disease.<a class="elsevierStyleCrossRefs" href="#bib0190">4,5,7,16</a> The VCap presents a curve from the measurement of the concentration of end tidal CO2, compared to values measured simultaneously in expired volumes during a single respiratory cycle, representing an alternative in the search for links between structural damage and functional changes.<a class="elsevierStyleCrossRefs" href="#bib0195">5,16</a> VCap is a noninvasive, easy to use method, with simple maintenance and low cost.<a class="elsevierStyleCrossRef" href="#bib0190">4</a> Also, recently, alterations of the small airways in patients with CF and normal spirometry were detected by VCap showing the feasibly of performing this test early, before severe pulmonary structure changes occur.<a class="elsevierStyleCrossRef" href="#bib0195">5</a>The VCap allows calculations of several indices among them the slope of phase 3 (slope 3) and little is known about its behavior during the physical exercise in individuals with CF. Slope 3 is a measure of the gas elimination curve and contains information about the transport of gases in the alveolar airways of the pulmonary periphery, hence the alveolar plateau. In addition, the slope of phase 2 (slope 2) describes the removal of CO2 from the alveoli, representing the mixture of alveolar gas and VD gas. Thus, slope 2 is determined by the abrupt rise of CO2 in the exhaled air from the VD.<a class="elsevierStyleCrossRef" href="#bib0255">17</a> Thus, the VCap reflects disturbances of lung function and may be useful as a non-invasive method of estimating the change in function in many lung diseases.<a class="elsevierStyleCrossRefs" href="#bib0190">4,5,7,16</a>The combination of VCap and exercise and the comparison with an already validated technique, such as spirometry, can provide us with valuable information about gas exchange during exercise, as well as, help us understand the limitation of these individuals in activities of daily life.The scientific community has turned its attention to methods and parameters that may be more sensitive in the early detection of these kinds of lung damage so that they can be combated early. Measurements of gas exchange of various forms during rest have already been obtained. However, the role of physical exercise, as an indicator of prognosis and/or therapeutic instrument, is an area of research interest in several diseases, particularly respiratory diseases. Physical exercise in CF has been studied as a marker of prognosis or as a tool to improve quality of life, and respiratory morbidity.<a class="elsevierStyleCrossRefs" href="#bib0175">1,3,4</a>In this context, the objective of the study was to verify the correlation between VCap parameters during submaximal treadmill exercise test with validated markers of spirometry in patients with CF and healthy controls.Material and methodsWe conducted a cross-sectional and controlled study, which included patients with CF (CFG) from a university hospital and healthy volunteers as a control group (CG). All patients with CF who agreed to participate were included. Those over 18 years of age, and caregivers of minors, signed the consent form, and the study followed the ethical standards of the Helsinki agreement and was approved by the Ethics Committee of the institution (#1182/2009).Group of patients with cystic fibrosis (CFG)Inclusion criteria was age between six and 25 years (inclusive). The CF was diagnosed according to international consensus criteria for sweat test (chloride values above 60mEq/L) and/or the presence of two CFTR mutations identified (cystic fibrosis transmembrane regulator).<a class="elsevierStyleCrossRef" href="#bib0260">18</a>The clinical stability was controlled by the use of two clinical scores: Cystic Fibrosis Foundation Clinical Score and Cystic Fibrosis Clinical Score<a class="elsevierStyleCrossRefs" href="#bib0210">8,19,20</a> and the severity of the disease was classified per the Shwachman–Kulczycki score.<a class="elsevierStyleCrossRef" href="#bib0275">21</a> Patients with lung exacerbation were not included. Also, we did not include patients on oxygen therapy. All patients with CF performed the submaximal treadmill exercise test without interruption.Control group (CG)The CG included children, adolescents and young healthy adults aged between six to 25 years, randomly selected among students from public and private schools of the same university district. All participants answered a questionnaire about health and none had acute respiratory disease at the time of data collection by clinical evaluation and spirometry markers.Evaluation proceduresAll participants performed sequentially.SpirometryBefore starting the submaximal treadmill exercise test, the follow anthropometric measurements were conducted: weight (kg), height (cm) and calculation of body mass index (BMI). Next, all subjects underwent spirometry according to the guidelines of the American Thoracic Society.<a class="elsevierStyleCrossRefs" href="#bib0280">22,23</a> Spirometry was performed using a CPFS/D spirometer (MedGraphics, Saint Paul, Minnesota, USA; PF BREEZE Software version 3.8 B for Windows 95/98/NT), and the percentage of predicted values of FEV1 (FEV1% predicted), forced vital capacity (FVC% predicted) and the ratio of FEV1/FVC were analyzed. Spirometry data are shown in percentage of the predicted value according to the Polgar and Promadhat (1971), Pereira et al. (2007) and Duarte et al. (2007) equations.<a class="elsevierStyleCrossRefs" href="#bib0285">23–25</a>Volumetric capnography (VCap)For VCap, a Co2smo Plus® DX 8100 Monitor (Novametrix Medical Systems, Wallingford, CT, USA) was used, which monitored the participants during the submaximal treadmill exercise test. This is a noninvasive monitor with capnography, pulse oximetry and pneumotachography function. The capnography and pneumotachography measures were obtained in real time by analysis of expired gases. The Co2smo Plus® was connected to a computer equipped with software to record the flow, volume, pressure, pressure-volume, flow-volume and capnography measurements and curves.The sensor monitor was connected to a mouthpiece, and a nose clip was used to prevent air escaping. At the end of the collection, an offline sequence of subjects’ respiratory cycles was selected for admission to a coefficient of variation for the lowest expired VT of 25% of the current volume of the mean, and the PetCO2 was admitted to a coefficient of variation less than 10% in millimeters of mercury (mmHg). Respiratory cycles presenting a value of zero in slope 3 were eliminated. The result obtained was the average of the variables during four minutes of monitoring.<a class="elsevierStyleCrossRefs" href="#bib0190">4,5</a>The following measures were analyzed:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005">i.Slope 3 was calculated using a linear regression curve of CO2/exhaled volume, measured in millimeters of mercury per liter (mmHg/L).</li><li class="elsevierStyleListItem" id="lsti0010">ii.VT: volume of air exhaled in one breath, measured in milliliters (mL).</li><li class="elsevierStyleListItem" id="lsti0015">iii.Slope 2 was calculated using a linear regression curve of CO2/exhaled volume, measured in mmHg/L.</li><li class="elsevierStyleListItem" id="lsti0020">iv.RR: measured in breaths per minute.</li><li class="elsevierStyleListItem" id="lsti0025">v.Volume carbon dioxide (VCO2): volume of carbon dioxide exhaled in one breath in mL.</li><li class="elsevierStyleListItem" id="lsti0030">vi.PetCO2, measured in mmHg.</li><li class="elsevierStyleListItem" id="lsti0035">vii.VD: volume of air that remains in the conducting airways, measured in mL.</li><li class="elsevierStyleListItem" id="lsti0040">viii.Alveolar tidal volume (VTalv): VT minus the VD in mL.</li><li class="elsevierStyleListItem" id="lsti0045">ix.VD/VT ratio.</li></ul>As the Co2smo Plus® monitor has pulse oximetry capability we also collected: (i) HR measured in beats per minute and (ii) SpO2 measured as a percentage (%).<a class="elsevierStyleCrossRefs" href="#bib0190">4,5</a>Submaximal treadmill exercise testTo determine how these variables are affected by exercise and lung disease, the participants walked on a Caloi Electronics® Pro CL 5004 treadmill for a period of six minutes; with increasing velocity, according to the tolerance of each individual. In this context, aiming at a submaximal test, the participants did not reach full capacity of exercise, choosing the proper exercise intensity at a submaximal level of effort.<a class="elsevierStyleCrossRefs" href="#bib0190">4,26,27</a>Just before the start of the submaximal treadmill exercise test, the participant remained at rest for five minutes for the measurement of cardiorespiratory variables, VCap and modified Borg scale (the classification of the subjective perception of effort). Then the submaximal treadmill exercise test began and cardiorespiratory data (RR, HR, SpO2 and VCap) were measured throughout the submaximal treadmill exercise test by the Co2smo Plus®.<a class="elsevierStyleCrossRef" href="#bib0190">4</a>The exercise was accompanied by verbal stimulus (“you are doing very well; can you go a little faster?”) in the first minute and at the end of each minute. Throughout the activity participants used a nozzle connected to the capnography sensor.The exercise was based on the 6-MWT and protocols developed for treadmills.<a class="elsevierStyleCrossRefs" href="#bib0190">4,27–31</a>The modified Borg scale is an instrument that evaluates the subjective perception of the effort measured by a questionnaire, 0 being no fatigue, up to the value of 10 that indicates exhaustive exhaustion. The scale was applied at rest (before) and immediately after the submaximal exertion test. The Borg scale was applied, only giving the explanation of the association with the perception of effort and that higher numbers represented a worst status. The Borg scale is shown in <a class="elsevierStyleCrossRef" href="#sec0090">Supplementary Material 1</a>.Statistical analysisStatistical analysis was performed for the CG and CFG at five different times in relation to the exercise: (1) basal; (2) one and two minutes into the test; (3) three and four minutes into the test; (4) five and six minutes into the test and (5) immediately post exercise. To calculate the values from the markers achieved in VCap we used the mean from all the data obtained during the period of the analysis (five different periods of time). We also include the cardiorespiratory adaptation period to promote a complete understanding of the response to two tools used in the submaximal treadmill exercise test performed. The association of the FVC, FEV1 and FEV1/FVC between patients with CF and healthy control subjects was performed by the Mann–Whitney test. Data were processed by the Statistical Package for Social Sciences software version 21.0 (SPSS Inc., Chicago, IL, USA). Correlations between spirometry and VCap were assessed by Spearman correlation test. In the data, the value of the Rho correlation coefficient is indicated. In all analyses, we adopted an alpha value of 0.05.ResultsA total of 128 subjects were evaluated, 64 in each group. There was no statistical difference in age and gender between the groups. Anthropometric variables were lower in CFG for weight, height and BMI compared to CG (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). Among CFG, the Shwachman–Kulczycki score ranged from 60 to 90 (77.93±6.94) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>) and the profile of the CFTR mutations identified is shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. In the modified Borg scale, subjects showed higher values after submaximal treadmill exercise test. The greatest sensation of dyspnea occurred in the CFG (p-value<0.05).<elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia>CFG had lower spirometric variables, as a percentage of the predicted value, compared to CG (FVC%: 78.61±20.68 versus 95.78±11.56; FEV1%: 68.98±22.45 versus 91.94±16.96 and FEV1/FVC%: 85.66±11.56 versus 98.23±8.95) (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>) (p-value<0.05).<elsevierMultimedia ident="tbl0015"></elsevierMultimedia>Slope 3 showed a low inverse correlation with FEV1 in both groups and at all moments of the submaximal treadmill exercise test. The FVC and FEV1/FVC ratio showed a low inverse correlation with slope 3 only for CFG (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). Values of slope 3 corrected by the spontaneous VT and PetCO2 showed results like slope 3 analyzed separately (<a class="elsevierStyleCrossRefs" href="#fig0010">Figs. 2 and 3</a>).<elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia>The complete data for correlations considering the VCap and spirometric markers is shown in <a class="elsevierStyleCrossRef" href="#sec0090">Supplementary Material 2</a>. Moreover, the <a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a> shows the descriptive analysis of all correlations performed.<elsevierMultimedia ident="fig0020"></elsevierMultimedia>DiscussionThe search for markers with higher sensitivity and specificity to assess the damage and consequences deriving from progression of lung disease in patients with CF has been increasingly encouraged and enhanced. In this context, this seems to be the first study that correlates the prognostic indices of spirometry with promising rates of VCap.It was noted that all indices analyzing VCap such as slope 3, slope 3/VT and slope 3/PetCO2 correlated with validated spirometric variables such as FEV1, FVC and FEV1/FVC demonstrating that VCap can identify early minor changes in airways, both at rest and mainly during exercise.The CF causes progressive deterioration in the ability to perform physical exercise. With the progression of lung disease, the increased VD requires changes in ventilation to maintain adequate alveolar ventilation during exercise, so changes in lung function over time are correlated with changes in exercise capacity.<a class="elsevierStyleCrossRefs" href="#bib0185">3,4,16</a>On the other hand, regardless of severity, both children and adults increase the ability to tolerate and benefit from exercise in the long term.<a class="elsevierStyleCrossRefs" href="#bib0310">28,30</a> Exercise has been used as a tool to assess improvement or worsening of markers of cardiac function.<a class="elsevierStyleCrossRefs" href="#bib0315">29–31</a>In this sense, VCap, although widely used in clinical practice, is underutilized in relation to HR, probably from ignorance of the results of this clinical tool and its possibilities for practical applications.<a class="elsevierStyleCrossRefs" href="#bib0320">30,31</a>Patients with CF are constantly subjected to numerous procedures, some invasive and others which are noninvasive but require patient effort, such as spirometry. VCap comes as a counterproposal requiring minimal patient effort.<a class="elsevierStyleCrossRefs" href="#bib0190">4,5</a>Slope 3 is caused by sequential emptying of lung units with different CO2 concentrations reflecting the heterogeneity of less ventilated areas such as the lung periphery, while FEV1 reflects the most proximal airway disease.<a class="elsevierStyleCrossRef" href="#bib0195">5</a>Ribeiro et al. (2012) by comparing the values of spirometry and VCap noted that patients with CF had higher slope 3/VT ratios, regardless of the lung disease stage, even in those with normal spirometry results, showing that VCap can identify heterogeneity of ventilation distribution in the peripheral airways of patients with CF even with normal spirometry.<a class="elsevierStyleCrossRef" href="#bib0195">5</a> In this context, patients with CF require ventilatory increase during exercise caused by poor ventilation of the lungs resulting in heterogeneity between ventilation and perfusion, since certain regions are hypoventilated while others are hyperventilated.<a class="elsevierStyleCrossRefs" href="#bib0330">32–34,7</a>Finally, the spirometry and VCap represents different aspects/elements related to lung function [(spirometry) mainly airway obstruction; (VCap) alveolar ventilation heterogeneity and ventilation/perfusion)] and this fact can be visualized by the low correlation coefficients observed in our sample between the two tools. The lung is a complex organ and numerous tools should be used, at the same time, to perform a wide diagnostic of the lung disease – one tool is a complement to another tool.As a study limitation, VCap reference values were not available in the literature, so we developed a curve of healthy subject values for VCap, as well as, the association regarding age and sex (e.g., these data can be done to spirometry, but the same is not true to VCap). Also, we are not able to perform multiple linear regression procedure regarding the low number of patients with CF and healthy control subjects enrolled and the correlation analysis providing a number that summarizes the degree of linear relationship between the two variables (spirometry and VCap). Also, the regression analysis provides an equation that describes the behavior of one of the variables as a function of the behavior of the other variable, which is not known in VCap. Thus, to date, knowledge about the cause and effect factor between both tools is limited and the markers of each tool appear to reflect on different phenotypic anomalies of the disease. Thus, in this first step, we believe that the interpretation, even with caution, of the correlation, and later on the regression, is more relevant. In addition, the interpretation of the results with low coefficient correlations and the limits of linear regression method that informs on the intensity of the liaison between two variables is limited, and new studies should be done to prove our findings. But, VCap tracking and monitoring of lung function in patients with CF may be a promising parameter. Also, follow-up studies may show more evidence of the effectiveness of the management of VCap in CF and other diseases. In addition, the VCap shows some limitations in discriminating the severity of the small airways alteration and it may be worth exploring another index to describe these alterations. In this way, studies should be performed including several indexes achieved from VCap, as well, the comparison between it and some lung function methods.Although VCap is a tool used mainly for monitoring critically ill and mechanically ventilated subjects, it has been proven to be a valuable tool in the evaluation of patients with CF before and during the exercise. The VCap differences and advantages include the lack of need for forced breathing maneuvers and, unlike spirometry, the possible elimination of bias such as patient effort, technical expertise and reference equations.ConclusionThe VCap indices had low correlation with parameter already validated in spirometry during submaximal treadmill exercise test such as FEV1, FVC and FEV1/FVC demonstrating that VCap is a useful tool for evaluating the pulmonary status of children and adolescents with CF being a complementary tool – spirometry (airway obstruction) and VCap (alveolar ventilation heterogeneity and ventilation/perfusion) methods do not assess the same element. The use of VCap and application associated with physical exercise provides information about respiratory behavior during this activity in these subjects, and the presence of obstruction, even in patients with unchanged FEV1.Ethical approvalThe study followed the ethical standards of the Helsinki agreement and was approved by the Ethics Committee of the institution (#1182/2009).Funding informationPaloma L.F. Parazzi: Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico (CNPq); Fernando A.L. Marson: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for the studies #2011/12939-4 and #2015/12858-5; Fundo de Apoio à Pesquisa ao Ensino e à Extensão da Universidade Estadual de Campinas for the study #0648/2015; José D. Ribeiro: FAPESP for the study #2011/18845-1 and #2015/12183-8.Authors’ contributionPLFP, FALM, MAGOR, CISS, JDR – made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; involved in drafting the manuscript and revising it critically for important intellectual content; gave final approval of the manuscript version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work were appropriately investigated and resolved.Conflicts of interestThe authors declare that there is no conflict of interest." "textoCompletoSecciones" => array:1 [ "secciones" => array:14 [ 0 => array:3 [ "identificador" => "xres1155729" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1083121" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec1083120" "titulo" => "Abbreviations" ] 3 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 4 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:7 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Group of patients with cystic fibrosis (CFG)" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Control group (CG)" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Evaluation procedures" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Spirometry" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Volumetric capnography (VCap)" ] 5 => array:2 [ "identificador" => "sec0040" "titulo" => "Submaximal treadmill exercise test" ] 6 => array:2 [ "identificador" => "sec0045" "titulo" => "Statistical analysis" ] ] ] 5 => array:2 [ "identificador" => "sec0050" "titulo" => "Results" ] 6 => array:2 [ "identificador" => "sec0055" "titulo" => "Discussion" ] 7 => array:2 [ "identificador" => "sec0060" "titulo" => "Conclusion" ] 8 => array:2 [ "identificador" => "sec0065" "titulo" => "Ethical approval" ] 9 => array:2 [ "identificador" => "sec0070" "titulo" => "Funding information" ] 10 => array:2 [ "identificador" => "sec0075" "titulo" => "Authors’ contribution" ] 11 => array:2 [ "identificador" => "sec0080" "titulo" => "Conflicts of interest" ] 12 => array:2 [ "identificador" => "xack394416" "titulo" => "Acknowledgments" ] 13 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2017-06-01" "fechaAceptado" => "2018-04-07" "PalabrasClave" => array:1 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1083121" "palabras" => array:4 [ 0 => "Capnography" 1 => "Exercise test" 2 => "Lung function" 3 => "Spirometry" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec1083120" "palabras" => array:27 [ 0 => "BMI" 1 => "CF" 2 => "CFG" 3 => "CFTR" 4 => "CG" 5 => "CO2" 6 => "ET" 7 => "EV" 8 => "FEV1" 9 => "FVC" 10 => "HR" 11 => "IT" 12 => "PetCO2" 13 => "RR" 14 => "slope 2" 15 => "slope 2/VT" 16 => "slope 3" 17 => "slope 3/PetCO2" 18 => "slope 3/VT" 19 => "SpO2" 20 => "6-MWT" 21 => "VCap" 22 => "VCO2" 23 => "VD" 24 => "VD/VT" 25 => "VT" 26 => "VTalv" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "IntroductionSpirometry is the most frequently used test to evaluate the progression of lung damage in cystic fibrosis (CF). However, there has been low sensitivity in detecting early lung changes. In this context, our objective was to identify the correlation between parameters of volumetric capnography (VCap) and spirometric parameters during a submaximal treadmill exercise test. MethodsA cross-sectional and controlled study which included 64 patients with CF (CFG) and 64 healthy control subjects (CG) was performed. The CFG was from a university hospital and the CG from local schools. All participants underwent spirometry and VCap before, during and after the submaximal treadmill exercise test. The main variable analyzed by VCap was the slope of phase 3 (slope 3), which indicates the [exhaled carbon dioxide] at the end of expiration, and expresses the heterogeneity of gas emptying in pulmonary periphery. The correlation analysis between spirometry and VCap was conducted using the Spearman correlation test, considering α=0.05. ResultsThe indices analyzed by VCap showed correlation with parameters of VCap. Slope 3 showed an inverse correlation with forced expiratory volume in the first second of forced vital capacity (FEV1) in both groups and at all moments of the submaximal treadmill exercise test. Forced vital capacity (FVC) and FEV1/FVC ratio showed an inverse correlation with slope 3 only for CFG. Values of slope 3 corrected by the spontaneous tidal volume (VT) and end-tidal carbon dioxide tension (PetCO2) showed results similar to slope 3 analyzed separately. ConclusionParameters of VCap such as slope 3, slope 3/VT and slope 3/PetCO2 correlated with sensitive variables of spirometry such as FEV1, FVC and FEV1/FVC ratio. For the evaluated variables, there was consistency in the correlation between the two tests, which may indicate the impact of CF on pulmonary physiology." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "The following are the supplementary data to this article:<elsevierMultimedia ident="upi0005"></elsevierMultimedia><elsevierMultimedia ident="upi0010"></elsevierMultimedia>" "etiqueta" => "Appendix A" "titulo" => "Supplementary material" "identificador" => "sec0090" ] ] ] ] "multimedia" => array:9 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1127 "Ancho" => 3167 "Tamanyo" => 561829 ] ] "descripcion" => array:1 [ "en" => "Correlation between slope 3 and spirometry markers. (A) FEV1: [All subjects] (Moment 1) CC=−0.468**, p-value<0.001; (Moment 2) CC=−0.501**, p-value<0.001; (Moment 3) CC=−0.455**, p-value<0.001; (Moment 4) CC=−0.447**, p-value<0.001; (Moment 5) CC=−0.445**, p-value<0.001; [CFG] (Moment 1) CC=−0.338**, p-value=0.006; (Moment 2) CC=−0.471**, p-value<0.001; (Moment 3) CC=−0.462**, p-value<0.001; (Moment 4) CC=−0.388**, p-value=0.002; (Moment 5) CC=−0.293*, p-value=0.019; [CG] (Moment 1) CC=−0.277*, p-value=0.027; (Moment 2) CC=−0.305*, p-value=0.014; (Moment 3) CC=−0.124, p-value=-0.328; (Moment 4) CC=−0.267*, p-value=0.033; (Moment 5) CC=−0.245, p-value=0.051. (B) FVC: [All subjects] (Moment 1) CC=−0.376**, p-value<0.001; (Moment 2) CC=−0.394**, p-value<0.001; (Moment 3) CC=−0.365**, p-value<0.001; (Moment 4) CC=−0.359**, p-value<0.001; (Moment 5) CC=−0.330, p-value<0.001; [CFG] (Moment 1) CC=−0.315**, p-value=0.011; (Moment 2) CC=−0.411**, p-value=0.001; (Moment 3) CC=−0.407**, p-value=0.001; (Moment 4) CC=−0.354**, p-value=0.004; (Moment 5) CC=−0.236, p-value=0.061; [CG] (Moment 1) CC=−0.117, p-value=0.359; (Moment 2) CC=−0.098, p-value=0.442; (Moment 3) CC=0.098, p-value=0.439; (Moment 4) CC=−0.022, p-value=0.861; (Moment 5) CC=−0.026, p-value=0.838. (C) FEV1/FVC: [All subjects] (Moment 1) CC=−0.237**, p-value=0.007; (Moment 2) CC=−0.282, p-value=0.001; (Moment 3) CC=−0.294, p-value=0.001; (Moment 4) CC=−0.284, p-value=0.001; (Moment 5) CC=−0.249, p-value=0.005; [CFG] (Moment 1) CC=−0.138, p-value=0.276; (Moment 2) CC=−0.349**, p-value=0.005; (Moment 3) CC=−0.330**, p-value=0.008; (Moment 4) CC=−0.239, p-value=0.057; (Moment 5) CC=−0.220, p-value=0.081; [CG] (Moment 1) CC=0.064, p-value=0.616; (Moment 2) CC=0.039, p-value=0.758; (Moment 3) CC=0.152, p-value=0.231; (Moment 4) CC=0.063, p-value=0.624; (Moment 5) CC=0.255*, p-value=0.042. CC, correlation coefficient; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; slope 3, slope of phase 3 of capnography; CFG, group with cystic fibrosis (N=64); CG, control group (N=64). All subjects, patients with cystic fibrosis+control subjects (N=128). ** The correlation is significant at the 0.01 level (2 ends). * The correlation is significant at the 0.05 level (2 ends). Alpha=0.05. Statistical analysis was performed using Spearman correlation (Rho). In the legend is presented the correlation coefficient of Spearman's rank. Data marked with * and ** showed statistically significant p-value." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1362 "Ancho" => 3199 "Tamanyo" => 499413 ] ] "descripcion" => array:1 [ "en" => "Correlation between slope 3/VT and spirometry markers. (A) FEV1: [All subjects] (Moment 1) CC=−0.437**, p-value<0.001; (Moment 2) CC=−0.454**, p-value<0.001; (Moment 3) CC=−0.422**, p-value<0.001; (Moment 4) CC=−0.431**, p-value<0.001; (Moment 5) CC=−0.409**, p-value<0.001; [CFG] (Moment 1) CC=−0.273, p-value=0.029; (Moment 2) CC=−0.428**, p-value<0.001; (Moment 3) CC=−0.406**, p-value=0.001; (Moment 4) CC=−0.351**, p-value=0.004; (Moment 5) CC=−0.213, p-value=0.091; [CG] (Moment 1) CC=−0.300*, p-value=0.016; (Moment 2) CC=−0.292*, p-value=0.019; (Moment 3) CC=−0.164, p-value=0.194; (Moment 4) CC=−0.286*, p-value=0.022; (Moment 5) CC=−0.254*, p-value=0.042. (B) FVC: [All subjects] (Moment 1) CC=−0.327**, p-value<0.001; (Moment 2) CC=−0.349**, p-value<0.001; (Moment 3) CC=−0.328**, p-value<0.001; (Moment 4) CC=−0.340**, p-value<0.001; (Moment 5) CC=−0.291**, p-value=0.001; [CFG] (Moment 1) CC=−0.256*, p-value=0.041; (Moment 2) CC=−0.378**, p-value=0.002; (Moment 3) CC=−0.362**, p-value=0.003; (Moment 4) CC=−0.327**, p-value=0.008; (Moment 5) CC=−0.146, p-value=0.250; [CG] (Moment 1) CC=−0.053, p-value=0.675; (Moment 2) CC=−0.068, p-value=0.592; (Moment 3) CC=0.083, p-value=0.512; (Moment 4) CC=−0.030, p-value=0.811; (Moment 5) CC=−0.032, p-value=0.803. (C) FEV1/FVC: [All subjects] (Moment 1) CC=−0.201*, p-value=0.023; (Moment 2) CC=0.206*, p-value=0.020; (Moment 3) CC=−0.232**, p-value=0.008; (Moment 4) CC=−0.213*, p-value=0.016; (Moment 5) CC=−0.209*, p-value=0.018; [CFG] (Moment 1) CC=−0.064, p-value=0.616; (Moment 2) CC=−0.263*, p-value=0.036; (Moment 3) CC=−0.246, p-value=0.051; (Moment 4) CC=−0.168, p-value=0.185; (Moment 5) CC=−0.150, p-value=0.236; [CG] (Moment 1) CC=0.049, p-value=0.699; (Moment 2) CC=0.110, p-value=0.385; (Moment 3) CC=0.199, p-value=0.115; (Moment 4) CC=0.131, p-value=0.304; (Moment 5) CC=0.259*, p-value=0.039. CC, correlation coefficient; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; slope 3/VT, slope of phase 3 normalized by expired tidal volume; CFG, group with cystic fibrosis (N=64); CG, control group (N=64). All subjects, patients with cystic fibrosis+control subjects (N=128). ** The correlation is significant at the 0.01 level (2 ends). * The correlation is significant at the 0.05 level (2 ends). Alpha=0.05. Statistical analysis was performed using Spearman correlation (Rho). In the legend is presented the correlation coefficient of Spearman's rank. Data marked with * and ** showed statistically significant p-value." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1282 "Ancho" => 3000 "Tamanyo" => 475775 ] ] "descripcion" => array:1 [ "en" => "Correlation between slope 3/PetCO2 and spirometry markers. (A) FEV1: [All subjects] (Moment 1) CC=−0.384**, p-value<0.001; (Moment 2) CC=−0.525**, p-value<0.001; (Moment 3) CC=−0.512**, p-value<0.001; (Moment 4) CC=−0.463**, p-value<0.001; (Moment 5) CC=−0.431**, p-value<0.001; [CFG] (Moment 1) CC=−0.351**, p-value=0.005; (Moment 2) CC=−0.499**, p-value<0.001; (Moment 3) CC=−0.503, p-value<0.001; (Moment 4) CC=−0.280, p-value=0.025; (Moment 5) CC=−0.353**, p-value=0.004; [CG] (Moment 1) CC=−0.093, p-value=0.467; (Moment 2) CC=−0.313*, p-value=0.012; (Moment 3) CC=−0.173, p-value=0.172; (Moment 4) CC=−0.239, p-value=0.058; (Moment 5) CC=−0.156, p-value=0.219. B. FVC: [All subjects] (Moment 1) CC=−0.336**, p-value<0.001; (Moment 2) CC=−0.435**, p-value<0.001; (Moment 3) CC=−0.409**, p-value<0.001; (Moment 4) CC=−0.361**, p-value<0.001; (Moment 5) CC=−0.342**, p-value<0.001; [CFG] (Moment 1) CC=0.341**, p-value=0.006; (Moment 2) CC=−0.457**, p-value<0.001; (Moment 3) CC=−0.438, p-value<0.001; (Moment 4) CC=−0.238, p-value=0.058; (Moment 5) CC=−0.327**, p-value=0.008; [CG] (Moment 1) CC=0.036, p-value=0.780; (Moment 2) CC=−0.131, p-value=0.302; (Moment 3) CC=0.066, p-value=0.603; (Moment 4) CC=−0.044, p-value=0.730; (Moment 5) CC=0.044, p-value=0.727. C. FEV1/FVC: [All subjects] (Moment 1) CC=−0.292**, p-value=0.001; (Moment 2) CC=−0.298**, p-value=0.001; (Moment 3) CC=−0.289**, p-value=0.001; (Moment 4) CC=−0.275**, p-value=0.002; (Moment 5) CC=−0.252**, p-value=0.004; [CFG] (Moment 1) CC=−0.177, p-value=0.162; (Moment 2) CC=−0.346**, p-value=0.005; (Moment 3) CC=−0.228, p-value=0.070; (Moment 4) CC=−0.230, p-value=0.068; (Moment 5) CC=−0.134, p-value=0.290; [CG] (Moment 1) CC=−0.119, p-value=0.349; (Moment 2) CC=0.077, p-value=0.543; (Moment 3) CC=0.092, p-value=0.471; (Moment 4) CC=0.278*, p-value=0.026; (Moment 5) CC=0.133, p-value=0.293. CC, correlation coefficient; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; slope 3/PetCO2, slope of phase 3 normalized by end-tidal carbon dioxide tension; CFG, group with cystic fibrosis (N=64); CG, control group (N=64). All subjects, patients with cystic fibrosis+control subjects (N=128). ** The correlation is significant at the 0.01 level (2 ends). * The correlation is significant at the 0.05 level (2 ends). Alpha=0.05. Statistical analysis was performed using Spearman correlation (Rho). In the legend is presented the correlation coefficient of Spearman's rank. Data marked with * and ** showed statistically significant p-value." ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 3575 "Ancho" => 2167 "Tamanyo" => 215923 ] ] "descripcion" => array:1 [ "en" => "Descriptive overview of correlations between variables obtained from spirometry and volumetric capnography. * G, groups; M, moments; 1, baseline time of the submaximal treadmill exercise test; 2, first and second minutes of the submaximal treadmill exercise test; 3, third and fourth minutes of the submaximal treadmill exercise test; 4, last two minutes of the submaximal treadmill exercise test; 5, measurement after the submaximal treadmill exercise test; CFG, cystic fibrosis group; CG, control group; All subjects, patients with cystic fibrosis+control subjects; IT, inspiratory time; ET, expiratory time; VT, expired tidal volume; VTalv, alveolar tidal volume; VD, anatomic dead space volume; slope 3, slope of phase 3 of capnography; slope 3/VT, slope of phase 3 normalized by VT; slope 3/PetCO2, slope of phase 3 normalized by partial pressure of carbon dioxide in the expired air; slope 2, slope of phase 2 of capnography; slope 2/VT, slope of phase 2 normalized by VT; VCO2, fraction of expired CO2; BMI, body mass index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; PetCO2, partial pressure of carbon dioxide in the expired air; EV, expiratory volume. Statistical analysis was performed using Spearman correlation (Rho). Blue, no correlation was observed; red, negative correlation was observed; green, positive correlation was observed." ] ] 4 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "kg, kilogram; m, meter. For variables with numeric distribution, the data are described as mean (confidence interval)±standard deviation; median (minimum to maximum). The difference between groups was calculated by the χ2 test for categorical variables and by Mann–Whitney test for numerical data considering α=0.05. Positive p-values are shown in bold." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Marker \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Group with cystic fibrosis \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Control group \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Gender (male) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">32/64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">30/64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.860 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="2" align="left" valign="top">Age (years)</td><td class="td" title="table-entry " align="left" valign="top">12.34 (11.48–13.21)±3.47 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">13.51 (12.34–14.68)±4.64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="2" align="char" valign="top">0.490</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">12 (6–23) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">11 (9–25) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="2" align="left" valign="top">Weight (kg)</td><td class="td" title="table-entry " align="left" valign="top">34.80 (31.47–38.12)±13.30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">46.39 (42.75–50.02)±14.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="2" align="char" valign="top">≤0.001</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">33.75 (12.70–74.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">44 (25.80–87.90) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="2" align="left" valign="top">Height (m)</td><td class="td" title="table-entry " align="left" valign="top">1.42 (1.37–1.46)±0.19 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.52 (1.49–1.56)±0.132 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="2" align="char" valign="top">0.003</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">1.45 (1–1.74) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.58 (1.20–1.87) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="2" align="left" valign="top">Body mass index (kg/m2)</td><td class="td" title="table-entry " align="left" valign="top">16.72 (15.93–17.50)±3.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">19.56 (18.72–20.40)±3.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="2" align="char" valign="top">≤0.001</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">15.87 (11.30–28.63) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">19.01 (13.46–27.13) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="2" align="left" valign="top">Shwachman-Kulczycki score</td><td class="td" title="table-entry " align="left" valign="top">77.93 (76.20–79.67)±6.94 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="2" align="char" valign="top">–</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">80 (60–90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1972913.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Distribution of patients with cystic fibrosis and healthy controls included in the study according to the clinical and laboratory markers." ] ] 5 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "CFTR, cystic fibrosis transmembrane regulator; N, number of patients." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">CFTR mutation \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">N \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Percentage (%) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Cumulative percentage \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/F508del \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">22 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">34.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">34.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/G542X \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/1812-1G>A \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">48.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/c.1717-1G>A \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/N1303K \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">54.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">3120+1G>A/R1066C \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">57.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/2183AA>G \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">59.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/R1066C \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">60.9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/R1162X \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/R553X \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">64.1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">2183AA>G/2183AA>G \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">65.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">R1162X/R1162X \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">67.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del/Unknown mutation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79.8 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">G542X/Unknown mutation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">81.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">I507V/Unknown mutation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">83 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">R1162X/Unknown mutation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Unknown mutation/Unknown mutation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">100 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1972911.png" ] ] ] ] "descripcion" => array:1 [ "en" => "CFTR mutations genotypes of patients with cystic fibrosis enrolled in the study (n=64)." ] ] 6 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "FVC (% predicted), forced vital capacity; FEV1 (% predicted), forced expiratory volume in one second; CFG, group with cystic fibrosis; CG, control group. *, p-value<0.001. The statistical analysis was performed regarding the Mann–Whitney test. α=0.05." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " rowspan="10" align="left" valign="top">FVC (% predicted)*</td><td class="td" title="table-entry " align="left" valign="top">CFG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean±SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">78.60±20.68 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">80.50 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">32 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">121 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">73.44–83.77 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean±SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95.78±11.56 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">93 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">69 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">130 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">92.89–98.67 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="10" align="left" valign="top">FEV1 (% predicted)*</td><td class="td" title="table-entry " align="left" valign="top">CFG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean±SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">68.98±22.45 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">65.50 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">19 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">117 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">63.38–74.59 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean ± SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">91.94±16.96 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">90 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">69 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">125 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">87.70–96.17 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="10" align="left" valign="top">FEV1/FVC (% predicted)*</td><td class="td" title="table-entry " align="left" valign="top">CFG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean±SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">85.66±11.56 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">87.50 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">52 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">107 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">82.77–88.54 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Mean±SD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">98.23±8.95 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Median \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">100 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Minimum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">74 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Maximum \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">125 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">96–100.47 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1972912.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Descriptive analysis of spirometry variables considering the groups with cystic fibrosis (CFG) and control (CG)." ] ] 7 => array:5 [ "identificador" => "upi0005" "tipo" => "MULTIMEDIAECOMPONENTE" "mostrarFloat" => false "mostrarDisplay" => true "Ecomponente" => array:2 [ "fichero" => "mmc1.docx" "ficheroTamanyo" => 12653 ] ] 8 => array:5 [ "identificador" => "upi0010" "tipo" => "MULTIMEDIAECOMPONENTE" "mostrarFloat" => false "mostrarDisplay" => true "Ecomponente" => array:2 [ "fichero" => "mmc2.docx" "ficheroTamanyo" => 73589 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:34 [ 0 => array:3 [ "identificador" => "bib0175" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Exercise as part of a cystic fibrosis therapeutic routine" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "S. 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Correlation between parameters of volumetric capnography and spirometry during a submaximal exercise protocol on a treadmill in patients with cystic fibrosis and healthy controls

P.L.F. Parazzia,

Corresponding author

palomaparazzi@yahoo.com.br

Corresponding authors.

, F.A.L. Marsona,b,

Corresponding author

fernandolimamarson@hotmail.com

Corresponding authors.

, M.A.G.O. Ribeiroa, C.I.S. Schivinskic, J.D. Ribeiroa

a Department of Pediatrics, Faculty of Medical Sciences, University of Campinas, Tessália Vieira de Camargo, 126, Cidade Universitária “Zeferino Vaz”, Postal Code: 13083-887 Campinas, São Paulo, Brazil

b Department of Medical Genetics, Faculty of Medical Sciences, University of Campinas, Tessália Vieira de Camargo, 126, Cidade Universitária “Zeferino Vaz”, Postal Code: 13083-887 Campinas, São Paulo, Brazil

c Center of Physical Education and Sports, State University of Santa Catarina, Postal Code: 88080-350 Coqueiros, Florianópolis, Santa Catarina, Brazil

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&#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;066&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;603&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;044&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;730&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;044&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;727&#46; C&#46; FEV<span class="elsevierStyleInf">1</span>&#47;FVC&#58; &#91;All subjects&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;292&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;298&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;289&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;275&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;002&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;252&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;004&#59; &#91;CFG&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;177&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;162&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;346&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;005&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;228&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;070&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;230&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;068&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;134&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;290&#59; &#91;CG&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;119&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;349&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;077&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;543&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;092&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;471&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;278&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;026&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;133&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;293&#46; CC&#44; correlation coefficient&#59; FVC&#44; forced vital capacity&#59; FEV<span class="elsevierStyleInf">1</span>&#44; forced expiratory volume in one second&#59; slope 3&#47;PetCO<span class="elsevierStyleInf">2</span>&#44; slope of phase 3 normalized by end-tidal carbon dioxide tension&#59; CFG&#44; group with cystic fibrosis &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>64&#41;&#59; CG&#44; control group &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>64&#41;&#46; All subjects&#44; patients with cystic fibrosis<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>control subjects &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>128&#41;&#46; &#42;&#42; The correlation is significant at the 0&#46;01 level &#40;2 ends&#41;&#46; &#42; The correlation is significant at the 0&#46;05 level &#40;2 ends&#41;&#46; Alpha<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46; Statistical analysis was performed using Spearman correlation &#40;Rho&#41;&#46; In the legend is presented the correlation coefficient of Spearman&#39;s rank&#46; Data marked with &#42; and &#42;&#42; showed statistically significant <span class="elsevierStyleItalic">p</span>-value&#46;</p>"
        ]
      ]
    ]
    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Patients with cystic fibrosis &#40;CF&#41; &#40;OMIM&#58; &#35;<a id="intr0005" class="elsevierStyleInterRef" href="omim:219700">219700</a>&#41; from birth&#44; show a progressive deterioration of lung structures&#44;<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">1&#44;2</span></a> with functional changes that are perpetuated throughout life&#46; These include decreased&#58; peripheral capillary oxygen saturation &#40;SpO<span class="elsevierStyleInf">2</span>&#41;&#44; the ratio of anatomic dead space and expired tidal volume &#40;VD&#47;VT&#41;&#44; forced expiratory volume in one second &#40;FEV<span class="elsevierStyleInf">1</span>&#41; and the ability to perform exercise&#46; Moreover&#44; increased respiratory rate &#40;RR&#41;&#44; heart rate &#40;HR&#41; and partial pressure of exhaled carbon dioxide &#40;PetCO<span class="elsevierStyleInf">2</span>&#41; in arterial blood have been shown&#46;<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">2&#8211;4</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Challenges in the management of lung disease in CF include the early detection of lung damage&#44; monitoring of disease progression and the identification and validation of parameters to evaluate response to treatment&#46;<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">5&#8211;7</span></a> In this context&#44; studies have proposed the use of new markers and instruments&#44; such as clinical scores&#44;<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">8</span></a> computed tomography&#44;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">6</span></a> sputum culture or endotracheal secretion&#44;<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">9</span></a> lung clearance index&#44;<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">10</span></a> functional tests &#91;six-minute walk test &#40;6-MWT&#41;<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">11</span></a> and treadmill tests<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">4</span></a>&#93;&#44; transepithelial potential difference&#44;<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">12</span></a> exhaled nitric oxide&#44;<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">13</span></a> impulse oscillometry&#44;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">14</span></a> condensed exhaled<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">15</span></a> and volumetric capnography &#40;VCap&#41;&#44;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5&#44;7&#44;16</span></a> with the latter recently being considered a promising useful tool in the assessment of functional impairment of the lungs&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5&#44;7&#44;16</span></a> Also&#44; until now&#44; spirometry has been considered the best lung function test to monitor lung disease progression in CF for more than two decades&#44; but the VCap has emerged as a feasible new tool&#46;<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">5&#8211;7&#44;16</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">The VCap may reflect the increased alveolar heterogeneity with the evolution of lung disease&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5&#44;7&#44;16</span></a> The VCap presents a curve from the measurement of the concentration of end tidal CO<span class="elsevierStyleInf">2</span>&#44; compared to values measured simultaneously in expired volumes during a single respiratory cycle&#44; representing an alternative in the search for links between structural damage and functional changes&#46;<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">5&#44;16</span></a> VCap is a noninvasive&#44; easy to use method&#44; with simple maintenance and low cost&#46;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">4</span></a> Also&#44; recently&#44; alterations of the small airways in patients with CF and normal spirometry were detected by VCap showing the feasibly of performing this test early&#44; before severe pulmonary structure changes occur&#46;<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">5</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The VCap allows calculations of several indices among them the slope of phase 3 &#40;slope 3&#41; and little is known about its behavior during the physical exercise in individuals with CF&#46; Slope 3 is a measure of the gas elimination curve and contains information about the transport of gases in the alveolar airways of the pulmonary periphery&#44; hence the alveolar plateau&#46; In addition&#44; the slope of phase 2 &#40;slope 2&#41; describes the removal of CO<span class="elsevierStyleInf">2</span> from the alveoli&#44; representing the mixture of alveolar gas and VD gas&#46; Thus&#44; slope 2 is determined by the abrupt rise of CO<span class="elsevierStyleInf">2</span> in the exhaled air from the VD&#46;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">17</span></a> Thus&#44; the VCap reflects disturbances of lung function and may be useful as a non-invasive method of estimating the change in function in many lung diseases&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5&#44;7&#44;16</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">The combination of VCap and exercise and the comparison with an already validated technique&#44; such as spirometry&#44; can provide us with valuable information about gas exchange during exercise&#44; as well as&#44; help us understand the limitation of these individuals in activities of daily life&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">The scientific community has turned its attention to methods and parameters that may be more sensitive in the early detection of these kinds of lung damage so that they can be combated early&#46; Measurements of gas exchange of various forms during rest have already been obtained&#46; However&#44; the role of physical exercise&#44; as an indicator of prognosis and&#47;or therapeutic instrument&#44; is an area of research interest in several diseases&#44; particularly respiratory diseases&#46; Physical exercise in CF has been studied as a marker of prognosis or as a tool to improve quality of life&#44; and respiratory morbidity&#46;<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">1&#44;3&#44;4</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">In this context&#44; the objective of the study was to verify the correlation between VCap parameters during submaximal treadmill exercise test with validated markers of spirometry in patients with CF and healthy controls&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Material and methods</span><p id="par0040" class="elsevierStylePara elsevierViewall">We conducted a cross-sectional and controlled study&#44; which included patients with CF &#40;CFG&#41; from a university hospital and healthy volunteers as a control group &#40;CG&#41;&#46; All patients with CF who agreed to participate were included&#46; Those over 18 years of age&#44; and caregivers of minors&#44; signed the consent form&#44; and the study followed the ethical standards of the Helsinki agreement and was approved by the Ethics Committee of the institution &#40;&#35;1182&#47;2009&#41;&#46;</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Group of patients with cystic fibrosis &#40;CFG&#41;</span><p id="par0045" class="elsevierStylePara elsevierViewall">Inclusion criteria was age between six and 25 years &#40;inclusive&#41;&#46; The CF was diagnosed according to international consensus criteria for sweat test &#40;chloride values above 60<span class="elsevierStyleHsp" style=""></span>mEq&#47;L&#41; and&#47;or the presence of two <span class="elsevierStyleItalic">CFTR</span> mutations identified &#40;<span class="elsevierStyleItalic">cystic fibrosis transmembrane regulator</span>&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">18</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">The clinical stability was controlled by the use of two clinical scores&#58; Cystic Fibrosis Foundation Clinical Score and Cystic Fibrosis Clinical Score<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">8&#44;19&#44;20</span></a> and the severity of the disease was classified per the Shwachman&#8211;Kulczycki score&#46;<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">21</span></a> Patients with lung exacerbation were not included&#46; Also&#44; we did not include patients on oxygen therapy&#46; All patients with CF performed the submaximal treadmill exercise test without interruption&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Control group &#40;CG&#41;</span><p id="par0055" class="elsevierStylePara elsevierViewall">The CG included children&#44; adolescents and young healthy adults aged between six to 25 years&#44; randomly selected among students from public and private schools of the same university district&#46; All participants answered a questionnaire about health and none had acute respiratory disease at the time of data collection by clinical evaluation and spirometry markers&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Evaluation procedures</span><p id="par0060" class="elsevierStylePara elsevierViewall">All participants performed sequentially&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Spirometry</span><p id="par0065" class="elsevierStylePara elsevierViewall">Before starting the submaximal treadmill exercise test&#44; the follow anthropometric measurements were conducted&#58; weight &#40;kg&#41;&#44; height &#40;cm&#41; and calculation of body mass index &#40;BMI&#41;&#46; Next&#44; all subjects underwent spirometry according to the guidelines of the American Thoracic Society&#46;<a class="elsevierStyleCrossRefs" href="#bib0280"><span class="elsevierStyleSup">22&#44;23</span></a> Spirometry was performed using a CPFS&#47;D spirometer &#40;MedGraphics&#44; Saint Paul&#44; Minnesota&#44; USA&#59; PF BREEZE Software version 3&#46;8 B for Windows 95&#47;98&#47;NT&#41;&#44; and the percentage of predicted values of FEV<span class="elsevierStyleInf">1</span> &#40;FEV<span class="elsevierStyleInf">1</span>&#37; predicted&#41;&#44; forced vital capacity &#40;FVC&#37; predicted&#41; and the ratio of FEV<span class="elsevierStyleInf">1</span>&#47;FVC were analyzed&#46; Spirometry data are shown in percentage of the predicted value according to the Polgar and Promadhat &#40;1971&#41;&#44; Pereira et al&#46; &#40;2007&#41; and Duarte et al&#46; &#40;2007&#41; equations&#46;<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">23&#8211;25</span></a></p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Volumetric capnography &#40;VCap&#41;</span><p id="par0070" class="elsevierStylePara elsevierViewall">For VCap&#44; a Co2smo Plus<span class="elsevierStyleSup">&#174;</span> DX 8100 Monitor &#40;Novametrix Medical Systems&#44; Wallingford&#44; CT&#44; USA&#41; was used&#44; which monitored the participants during the submaximal treadmill exercise test&#46; This is a noninvasive monitor with capnography&#44; pulse oximetry and pneumotachography function&#46; The capnography and pneumotachography measures were obtained in real time by analysis of expired gases&#46; The Co2smo Plus<span class="elsevierStyleSup">&#174;</span> was connected to a computer equipped with software to record the flow&#44; volume&#44; pressure&#44; pressure-volume&#44; flow-volume and capnography measurements and curves&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">The sensor monitor was connected to a mouthpiece&#44; and a nose clip was used to prevent air escaping&#46; At the end of the collection&#44; an offline sequence of subjects&#8217; respiratory cycles was selected for admission to a coefficient of variation for the lowest expired VT of 25&#37; of the current volume of the mean&#44; and the PetCO<span class="elsevierStyleInf">2</span> was admitted to a coefficient of variation less than 10&#37; in millimeters of mercury &#40;mmHg&#41;&#46; Respiratory cycles presenting a value of zero in slope 3 were eliminated&#46; The result obtained was the average of the variables during four minutes of monitoring&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5</span></a></p><p id="par0080" class="elsevierStylePara elsevierViewall">The following measures were analyzed&#58;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">i&#46;</span><p id="par0085" class="elsevierStylePara elsevierViewall">Slope 3 was calculated using a linear regression curve of CO<span class="elsevierStyleInf">2</span>&#47;exhaled volume&#44; measured in millimeters of mercury per liter &#40;mmHg&#47;L&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">ii&#46;</span><p id="par0090" class="elsevierStylePara elsevierViewall">VT&#58; volume of air exhaled in one breath&#44; measured in milliliters &#40;mL&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">iii&#46;</span><p id="par0095" class="elsevierStylePara elsevierViewall">Slope 2 was calculated using a linear regression curve of CO<span class="elsevierStyleInf">2</span>&#47;exhaled volume&#44; measured in mmHg&#47;L&#46;</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">iv&#46;</span><p id="par0100" class="elsevierStylePara elsevierViewall">RR&#58; measured in breaths per minute&#46;</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">v&#46;</span><p id="par0105" class="elsevierStylePara elsevierViewall">Volume carbon dioxide &#40;VCO<span class="elsevierStyleInf">2</span>&#41;&#58; volume of carbon dioxide exhaled in one breath in mL&#46;</p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">vi&#46;</span><p id="par0110" class="elsevierStylePara elsevierViewall">PetCO<span class="elsevierStyleInf">2</span>&#44; measured in mmHg&#46;</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">vii&#46;</span><p id="par0115" class="elsevierStylePara elsevierViewall">VD&#58; volume of air that remains in the conducting airways&#44; measured in mL&#46;</p></li><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">viii&#46;</span><p id="par0120" class="elsevierStylePara elsevierViewall">Alveolar tidal volume &#40;VTalv&#41;&#58; VT minus the VD in mL&#46;</p></li><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel">ix&#46;</span><p id="par0125" class="elsevierStylePara elsevierViewall">VD&#47;VT ratio&#46;</p></li></ul></p><p id="par0130" class="elsevierStylePara elsevierViewall">As the Co2smo Plus<span class="elsevierStyleSup">&#174;</span> monitor has pulse oximetry capability we also collected&#58; &#40;i&#41; HR measured in beats per minute and &#40;ii&#41; SpO<span class="elsevierStyleInf">2</span> measured as a percentage &#40;&#37;&#41;&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5</span></a></p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Submaximal treadmill exercise test</span><p id="par0135" class="elsevierStylePara elsevierViewall">To determine how these variables are affected by exercise and lung disease&#44; the participants walked on a Caloi Electronics<span class="elsevierStyleSup">&#174;</span> Pro CL 5004 treadmill for a period of six minutes&#59; with increasing velocity&#44; according to the tolerance of each individual&#46; In this context&#44; aiming at a submaximal test&#44; the participants did not reach full capacity of exercise&#44; choosing the proper exercise intensity at a submaximal level of effort&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;26&#44;27</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">Just before the start of the submaximal treadmill exercise test&#44; the participant remained at rest for five minutes for the measurement of cardiorespiratory variables&#44; VCap and modified Borg scale &#40;the classification of the subjective perception of effort&#41;&#46; Then the submaximal treadmill exercise test began and cardiorespiratory data &#40;RR&#44; HR&#44; SpO<span class="elsevierStyleInf">2</span> and VCap&#41; were measured throughout the submaximal treadmill exercise test by the Co2smo Plus<span class="elsevierStyleSup">&#174;</span>&#46;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">4</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">The exercise was accompanied by verbal stimulus &#40;&#8220;you are doing very well&#59; can you go a little faster&#63;&#8221;&#41; in the first minute and at the end of each minute&#46; Throughout the activity participants used a nozzle connected to the capnography sensor&#46;</p><p id="par0150" class="elsevierStylePara elsevierViewall">The exercise was based on the 6-MWT and protocols developed for treadmills&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;27&#8211;31</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">The modified Borg scale is an instrument that evaluates the subjective perception of the effort measured by a questionnaire&#44; 0 being no fatigue&#44; up to the value of 10 that indicates exhaustive exhaustion&#46; The scale was applied at rest &#40;before&#41; and immediately after the submaximal exertion test&#46; The Borg scale was applied&#44; only giving the explanation of the association with the perception of effort and that higher numbers represented a worst status&#46; The Borg scale is shown in <a class="elsevierStyleCrossRef" href="#sec0090">Supplementary Material 1</a>&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Statistical analysis</span><p id="par0160" class="elsevierStylePara elsevierViewall">Statistical analysis was performed for the CG and CFG at five different times in relation to the exercise&#58; &#40;1&#41; basal&#59; &#40;2&#41; one and two minutes into the test&#59; &#40;3&#41; three and four minutes into the test&#59; &#40;4&#41; five and six minutes into the test and &#40;5&#41; immediately post exercise&#46; To calculate the values from the markers achieved in VCap we used the mean from all the data obtained during the period of the analysis &#40;five different periods of time&#41;&#46; We also include the cardiorespiratory adaptation period to promote a complete understanding of the response to two tools used in the submaximal treadmill exercise test performed&#46; The association of the FVC&#44; FEV<span class="elsevierStyleInf">1</span> and FEV<span class="elsevierStyleInf">1</span>&#47;FVC between patients with CF and healthy control subjects was performed by the Mann&#8211;Whitney test&#46; Data were processed by the Statistical Package for Social Sciences software version 21&#46;0 &#40;SPSS Inc&#46;&#44; Chicago&#44; IL&#44; USA&#41;&#46; Correlations between spirometry and VCap were assessed by Spearman correlation test&#46; In the data&#44; the value of the Rho correlation coefficient is indicated&#46; In all analyses&#44; we adopted an alpha value of 0&#46;05&#46;</p></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Results</span><p id="par0165" class="elsevierStylePara elsevierViewall">A total of 128 subjects were evaluated&#44; 64 in each group&#46; There was no statistical difference in age and gender between the groups&#46; Anthropometric variables were lower in CFG for weight&#44; height and BMI compared to CG &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46; Among CFG&#44; the Shwachman&#8211;Kulczycki score ranged from 60 to 90 &#40;77&#46;93<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>6&#46;94&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41; and the profile of the <span class="elsevierStyleItalic">CFTR</span> mutations identified is shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#46; In the modified Borg scale&#44; subjects showed higher values after submaximal treadmill exercise test&#46; The greatest sensation of dyspnea occurred in the CFG &#40;<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0170" class="elsevierStylePara elsevierViewall">CFG had lower spirometric variables&#44; as a percentage of the predicted value&#44; compared to CG &#40;FVC&#37;&#58; 78&#46;61<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>20&#46;68 versus 95&#46;78<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>11&#46;56&#59; FEV<span class="elsevierStyleInf">1</span>&#37;&#58; 68&#46;98<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>22&#46;45 versus 91&#46;94<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>16&#46;96 and FEV<span class="elsevierStyleInf">1</span>&#47;FVC&#37;&#58; 85&#46;66<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>11&#46;56 versus 98&#46;23<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>8&#46;95&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41; &#40;<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0175" class="elsevierStylePara elsevierViewall">Slope 3 showed a low inverse correlation with FEV<span class="elsevierStyleInf">1</span> in both groups and at all moments of the submaximal treadmill exercise test&#46; The FVC and FEV<span class="elsevierStyleInf">1</span>&#47;FVC ratio showed a low inverse correlation with slope 3 only for CFG &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46; Values of slope 3 corrected by the spontaneous VT and PetCO<span class="elsevierStyleInf">2</span> showed results like slope 3 analyzed separately &#40;<a class="elsevierStyleCrossRefs" href="#fig0010">Figs&#46; 2 and 3</a>&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0180" class="elsevierStylePara elsevierViewall">The complete data for correlations considering the VCap and spirometric markers is shown in <a class="elsevierStyleCrossRef" href="#sec0090">Supplementary Material 2</a>&#46; Moreover&#44; the <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a> shows the descriptive analysis of all correlations performed&#46;</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Discussion</span><p id="par0185" class="elsevierStylePara elsevierViewall">The search for markers with higher sensitivity and specificity to assess the damage and consequences deriving from progression of lung disease in patients with CF has been increasingly encouraged and enhanced&#46; In this context&#44; this seems to be the first study that correlates the prognostic indices of spirometry with promising rates of VCap&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">It was noted that all indices analyzing VCap such as slope 3&#44; slope 3&#47;VT and slope 3&#47;PetCO<span class="elsevierStyleInf">2</span> correlated with validated spirometric variables such as FEV<span class="elsevierStyleInf">1</span>&#44; FVC and FEV<span class="elsevierStyleInf">1</span>&#47;FVC demonstrating that VCap can identify early minor changes in airways&#44; both at rest and mainly during exercise&#46;</p><p id="par0195" class="elsevierStylePara elsevierViewall">The CF causes progressive deterioration in the ability to perform physical exercise&#46; With the progression of lung disease&#44; the increased VD requires changes in ventilation to maintain adequate alveolar ventilation during exercise&#44; so changes in lung function over time are correlated with changes in exercise capacity&#46;<a class="elsevierStyleCrossRefs" href="#bib0185"><span class="elsevierStyleSup">3&#44;4&#44;16</span></a></p><p id="par0200" class="elsevierStylePara elsevierViewall">On the other hand&#44; regardless of severity&#44; both children and adults increase the ability to tolerate and benefit from exercise in the long term&#46;<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">28&#44;30</span></a> Exercise has been used as a tool to assess improvement or worsening of markers of cardiac function&#46;<a class="elsevierStyleCrossRefs" href="#bib0315"><span class="elsevierStyleSup">29&#8211;31</span></a></p><p id="par0205" class="elsevierStylePara elsevierViewall">In this sense&#44; VCap&#44; although widely used in clinical practice&#44; is underutilized in relation to HR&#44; probably from ignorance of the results of this clinical tool and its possibilities for practical applications&#46;<a class="elsevierStyleCrossRefs" href="#bib0320"><span class="elsevierStyleSup">30&#44;31</span></a></p><p id="par0210" class="elsevierStylePara elsevierViewall">Patients with CF are constantly subjected to numerous procedures&#44; some invasive and others which are noninvasive but require patient effort&#44; such as spirometry&#46; VCap comes as a counterproposal requiring minimal patient effort&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">4&#44;5</span></a></p><p id="par0215" class="elsevierStylePara elsevierViewall">Slope 3 is caused by sequential emptying of lung units with different CO<span class="elsevierStyleInf">2</span> concentrations reflecting the heterogeneity of less ventilated areas such as the lung periphery&#44; while FEV<span class="elsevierStyleInf">1</span> reflects the most proximal airway disease&#46;<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">5</span></a></p><p id="par0220" class="elsevierStylePara elsevierViewall">Ribeiro et al&#46; &#40;2012&#41; by comparing the values of spirometry and VCap noted that patients with CF had higher slope 3&#47;VT ratios&#44; regardless of the lung disease stage&#44; even in those with normal spirometry results&#44; showing that VCap can identify heterogeneity of ventilation distribution in the peripheral airways of patients with CF even with normal spirometry&#46;<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">5</span></a> In this context&#44; patients with CF require ventilatory increase during exercise caused by poor ventilation of the lungs resulting in heterogeneity between ventilation and perfusion&#44; since certain regions are hypoventilated while others are hyperventilated&#46;<a class="elsevierStyleCrossRefs" href="#bib0330"><span class="elsevierStyleSup">32&#8211;34&#44;7</span></a></p><p id="par0225" class="elsevierStylePara elsevierViewall">Finally&#44; the spirometry and VCap represents different aspects&#47;elements related to lung function &#91;&#40;spirometry&#41; mainly airway obstruction&#59; &#40;VCap&#41; alveolar ventilation heterogeneity and ventilation&#47;perfusion&#41;&#93; and this fact can be visualized by the low correlation coefficients observed in our sample between the two tools&#46; The lung is a complex organ and numerous tools should be used&#44; at the same time&#44; to perform a wide diagnostic of the lung disease &#8211; one tool is a complement to another tool&#46;</p><p id="par0230" class="elsevierStylePara elsevierViewall">As a study limitation&#44; VCap reference values were not available in the literature&#44; so we developed a curve of healthy subject values for VCap&#44; as well as&#44; the association regarding age and sex &#40;e&#46;g&#46;&#44; these data can be done to spirometry&#44; but the same is not true to VCap&#41;&#46; Also&#44; we are not able to perform multiple linear regression procedure regarding the low number of patients with CF and healthy control subjects enrolled and the correlation analysis providing a number that summarizes the degree of linear relationship between the two variables &#40;spirometry and VCap&#41;&#46; Also&#44; the regression analysis provides an equation that describes the behavior of one of the variables as a function of the behavior of the other variable&#44; which is not known in VCap&#46; Thus&#44; to date&#44; knowledge about the cause and effect factor between both tools is limited and the markers of each tool appear to reflect on different phenotypic anomalies of the disease&#46; Thus&#44; in this first step&#44; we believe that the interpretation&#44; even with caution&#44; of the correlation&#44; and later on the regression&#44; is more relevant&#46; In addition&#44; the interpretation of the results with low coefficient correlations and the limits of linear regression method that informs on the intensity of the liaison between two variables is limited&#44; and new studies should be done to prove our findings&#46; But&#44; VCap tracking and monitoring of lung function in patients with CF may be a promising parameter&#46; Also&#44; follow-up studies may show more evidence of the effectiveness of the management of VCap in CF and other diseases&#46; In addition&#44; the VCap shows some limitations in discriminating the severity of the small airways alteration and it may be worth exploring another index to describe these alterations&#46; In this way&#44; studies should be performed including several indexes achieved from VCap&#44; as well&#44; the comparison between it and some lung function methods&#46;</p><p id="par0235" class="elsevierStylePara elsevierViewall">Although VCap is a tool used mainly for monitoring critically ill and mechanically ventilated subjects&#44; it has been proven to be a valuable tool in the evaluation of patients with CF before and during the exercise&#46; The VCap differences and advantages include the lack of need for forced breathing maneuvers and&#44; unlike spirometry&#44; the possible elimination of bias such as patient effort&#44; technical expertise and reference equations&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Conclusion</span><p id="par0240" class="elsevierStylePara elsevierViewall">The VCap indices had low correlation with parameter already validated in spirometry during submaximal treadmill exercise test such as FEV<span class="elsevierStyleInf">1</span>&#44; FVC and FEV<span class="elsevierStyleInf">1</span>&#47;FVC demonstrating that VCap is a useful tool for evaluating the pulmonary status of children and adolescents with CF being a complementary tool &#8211; spirometry &#40;airway obstruction&#41; and VCap &#40;alveolar ventilation heterogeneity and ventilation&#47;perfusion&#41; methods do not assess the same element&#46; The use of VCap and application associated with physical exercise provides information about respiratory behavior during this activity in these subjects&#44; and the presence of obstruction&#44; even in patients with unchanged FEV<span class="elsevierStyleInf">1&#46;</span></p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Ethical approval</span><p id="par0245" class="elsevierStylePara elsevierViewall">The study followed the ethical standards of the Helsinki agreement and was approved by the Ethics Committee of the institution &#40;&#35;1182&#47;2009&#41;&#46;</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Funding information</span><p id="par0250" class="elsevierStylePara elsevierViewall">Paloma L&#46;F&#46; Parazzi&#58; Conselho Nacional de Pesquisa e Desenvolvimento Cient&#237;fico e Tecnol&#243;gico &#40;<span class="elsevierStyleGrantSponsor" id="gs1">CNPq</span>&#41;&#59; Fernando A&#46;L&#46; Marson&#58; Funda&#231;&#227;o de Amparo &#224; Pesquisa do Estado de S&#227;o Paulo &#40;<span class="elsevierStyleGrantSponsor" id="gs2">FAPESP</span>&#41; for the studies &#35;2011&#47;12939-4 and &#35;2015&#47;12858-5&#59; Fundo de Apoio &#224; Pesquisa ao Ensino e &#224; Extens&#227;o da Universidade Estadual de Campinas for the study &#35;0648&#47;2015&#59; Jos&#233; D&#46; Ribeiro&#58; FAPESP for the study &#35;2011&#47;18845-1 and &#35;2015&#47;12183-8&#46;</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Authors&#8217; contribution</span><p id="par0255" class="elsevierStylePara elsevierViewall">PLFP&#44; FALM&#44; MAGOR&#44; CISS&#44; JDR &#8211; made substantial contributions to conception and design&#44; acquisition of data&#44; or analysis and interpretation of data&#59; involved in drafting the manuscript and revising it critically for important intellectual content&#59; gave final approval of the manuscript version to be published&#59; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work were appropriately investigated and resolved&#46;</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Conflicts of interest</span><p id="par0260" class="elsevierStylePara elsevierViewall">The authors declare that there is no conflict of interest&#46;</p></span></span>"
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              "titulo" => "Group of patients with cystic fibrosis &#40;CFG&#41;"
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              "titulo" => "Volumetric capnography &#40;VCap&#41;"
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            0 => "Capnography"
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            2 => "Lung function"
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          "clase" => "abr"
          "titulo" => "Abbreviations"
          "identificador" => "xpalclavsec1083120"
          "palabras" => array:27 [
            0 => "BMI"
            1 => "CF"
            2 => "CFG"
            3 => "<span class="elsevierStyleItalic">CFTR</span>"
            4 => "CG"
            5 => "CO<span class="elsevierStyleInf">2</span>"
            6 => "ET"
            7 => "EV"
            8 => "FEV<span class="elsevierStyleInf">1</span>"
            9 => "FVC"
            10 => "HR"
            11 => "IT"
            12 => "PetCO<span class="elsevierStyleInf">2</span>"
            13 => "RR"
            14 => "slope 2"
            15 => "slope 2&#47;VT"
            16 => "slope 3"
            17 => "slope 3&#47;PetCO<span class="elsevierStyleInf">2</span>"
            18 => "slope 3&#47;VT"
            19 => "SpO<span class="elsevierStyleInf">2</span>"
            20 => "6-MWT"
            21 => "VCap"
            22 => "VCO<span class="elsevierStyleInf">2</span>"
            23 => "VD"
            24 => "VD&#47;VT"
            25 => "VT"
            26 => "VTalv"
          ]
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Spirometry is the most frequently used test to evaluate the progression of lung damage in cystic fibrosis &#40;CF&#41;&#46; However&#44; there has been low sensitivity in detecting early lung changes&#46; In this context&#44; our objective was to identify the correlation between parameters of volumetric capnography &#40;VCap&#41; and spirometric parameters during a submaximal treadmill exercise test&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A cross-sectional and controlled study which included 64 patients with CF &#40;CFG&#41; and 64 healthy control subjects &#40;CG&#41; was performed&#46; The CFG was from a university hospital and the CG from local schools&#46; All participants underwent spirometry and VCap before&#44; during and after the submaximal treadmill exercise test&#46; The main variable analyzed by VCap was the slope of phase 3 &#40;slope 3&#41;&#44; which indicates the &#91;exhaled carbon dioxide&#93; at the end of expiration&#44; and expresses the heterogeneity of gas emptying in pulmonary periphery&#46; The correlation analysis between spirometry and VCap was conducted using the Spearman correlation test&#44; considering <span class="elsevierStyleItalic">&#945;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The indices analyzed by VCap showed correlation with parameters of VCap&#46; Slope 3 showed an inverse correlation with forced expiratory volume in the first second of forced vital capacity &#40;FEV<span class="elsevierStyleInf">1</span>&#41; in both groups and at all moments of the submaximal treadmill exercise test&#46; Forced vital capacity &#40;FVC&#41; and FEV<span class="elsevierStyleInf">1</span>&#47;FVC ratio showed an inverse correlation with slope 3 only for CFG&#46; Values of slope 3 corrected by the spontaneous tidal volume &#40;VT&#41; and end-tidal carbon dioxide tension &#40;PetCO<span class="elsevierStyleInf">2</span>&#41; showed results similar to slope 3 analyzed separately&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Parameters of VCap such as slope 3&#44; slope 3&#47;VT and slope 3&#47;PetCO<span class="elsevierStyleInf">2</span> correlated with sensitive variables of spirometry such as FEV<span class="elsevierStyleInf">1</span>&#44; FVC and FEV<span class="elsevierStyleInf">1</span>&#47;FVC ratio&#46; For the evaluated variables&#44; there was consistency in the correlation between the two tests&#44; which may indicate the impact of CF on pulmonary physiology&#46;</p></span>"
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            "identificador" => "abst0005"
            "titulo" => "Introduction"
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            "titulo" => "Methods"
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            "identificador" => "abst0015"
            "titulo" => "Results"
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            "identificador" => "abst0020"
            "titulo" => "Conclusion"
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            "apendice" => "<p id="par0275" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia><elsevierMultimedia ident="upi0010"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary material"
            "identificador" => "sec0090"
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&#42;&#42; The correlation is significant at the 0&#46;01 level &#40;2 ends&#41;&#46; &#42; The correlation is significant at the 0&#46;05 level &#40;2 ends&#41;&#46; Alpha<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46; Statistical analysis was performed using Spearman correlation &#40;Rho&#41;&#46; In the legend is presented the correlation coefficient of Spearman&#39;s rank&#46; Data marked with &#42; and &#42;&#42; showed statistically significant <span class="elsevierStyleItalic">p</span>-value&#46;</p>"
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<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;409&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;361&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;342&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#91;CFG&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;341&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;006&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;457&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;438&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;238&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;058&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;327&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;008&#59; &#91;CG&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;036&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;780&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;131&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;302&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;066&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;603&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;044&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;730&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;044&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;727&#46; C&#46; FEV<span class="elsevierStyleInf">1</span>&#47;FVC&#58; &#91;All subjects&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;292&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;298&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;289&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;275&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;002&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;252&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;004&#59; &#91;CFG&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;177&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;162&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;346&#42;&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;005&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;228&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;070&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;230&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;068&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;134&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;290&#59; &#91;CG&#93; &#40;Moment 1&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;0&#46;119&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;349&#59; &#40;Moment 2&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;077&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;543&#59; &#40;Moment 3&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;092&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;471&#59; &#40;Moment 4&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;278&#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;026&#59; &#40;Moment 5&#41; CC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;133&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;293&#46; CC&#44; correlation coefficient&#59; FVC&#44; forced vital capacity&#59; FEV<span class="elsevierStyleInf">1</span>&#44; forced expiratory volume in one second&#59; slope 3&#47;PetCO<span class="elsevierStyleInf">2</span>&#44; slope of phase 3 normalized by end-tidal carbon dioxide tension&#59; CFG&#44; group with cystic fibrosis &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>64&#41;&#59; CG&#44; control group &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>64&#41;&#46; All subjects&#44; patients with cystic fibrosis<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>control subjects &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>128&#41;&#46; &#42;&#42; The correlation is significant at the 0&#46;01 level &#40;2 ends&#41;&#46; &#42; The correlation is significant at the 0&#46;05 level &#40;2 ends&#41;&#46; Alpha<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46; Statistical analysis was performed using Spearman correlation &#40;Rho&#41;&#46; In the legend is presented the correlation coefficient of Spearman&#39;s rank&#46; Data marked with &#42; and &#42;&#42; showed statistically significant <span class="elsevierStyleItalic">p</span>-value&#46;</p>"
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          "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Descriptive overview of correlations between variables obtained from spirometry and volumetric capnography&#46; &#42; G&#44; groups&#59; M&#44; moments&#59; 1&#44; baseline time of the submaximal treadmill exercise test&#59; 2&#44; first and second minutes of the submaximal treadmill exercise test&#59; 3&#44; third and fourth minutes of the submaximal treadmill exercise test&#59; 4&#44; last two minutes of the submaximal treadmill exercise test&#59; 5&#44; measurement after the submaximal treadmill exercise test&#59; CFG&#44; cystic fibrosis group&#59; CG&#44; control group&#59; All subjects&#44; patients with cystic fibrosis<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>control subjects&#59; IT&#44; inspiratory time&#59; ET&#44; expiratory time&#59; VT&#44; expired tidal volume&#59; VT<span class="elsevierStyleInf">alv</span>&#44; alveolar tidal volume&#59; VD&#44; anatomic dead space volume&#59; slope 3&#44; slope of phase 3 of capnography&#59; slope 3&#47;VT&#44; slope of phase 3 normalized by VT&#59; slope 3&#47;PetCO<span class="elsevierStyleInf">2</span>&#44; slope of phase 3 normalized by partial pressure of carbon dioxide in the expired air&#59; slope 2&#44; slope of phase 2 of capnography&#59; slope 2&#47;VT&#44; slope of phase 2 normalized by VT&#59; VCO<span class="elsevierStyleInf">2</span>&#44; fraction of expired CO<span class="elsevierStyleInf">2</span>&#59; BMI&#44; body mass index&#59; FVC&#44; forced vital capacity&#59; FEV<span class="elsevierStyleInf">1</span>&#44; forced expiratory volume in one second&#59; PetCO<span class="elsevierStyleInf">2</span>&#44; partial pressure of carbon dioxide in the expired air&#59; EV&#44; expiratory volume&#46; Statistical analysis was performed using Spearman correlation &#40;Rho&#41;&#46; Blue&#44; no correlation was observed&#59; red&#44; negative correlation was observed&#59; green&#44; positive correlation was observed&#46;</p>"
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          "leyenda" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">kg&#44; kilogram&#59; m&#44; meter&#46; For variables with numeric distribution&#44; the data are described as mean &#40;confidence interval&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>standard deviation&#59; median &#40;minimum to maximum&#41;&#46; The difference between groups was calculated by the <span class="elsevierStyleItalic">&#967;</span><span class="elsevierStyleSup">2</span> test for categorical variables and by Mann&#8211;Whitney test for numerical data considering <span class="elsevierStyleItalic">&#945;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46; Positive <span class="elsevierStyleItalic">p</span>-values are shown in bold&#46;</p>"
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                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Marker&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Group with cystic fibrosis&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Gender &#40;male&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">32&#47;64&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">30&#47;64&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;860&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="2" align="left" valign="top">Age &#40;years&#41;</td><td class="td" title="table-entry  " align="left" valign="top">12&#46;34 &#40;11&#46;48&#8211;13&#46;21&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>3&#46;47&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">13&#46;51 &#40;12&#46;34&#8211;14&#46;68&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>4&#46;64&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " rowspan="2" align="char" valign="top">0&#46;490</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">12 &#40;6&#8211;23&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">11 &#40;9&#8211;25&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="2" align="left" valign="top">Weight &#40;kg&#41;</td><td class="td" title="table-entry  " align="left" valign="top">34&#46;80 &#40;31&#46;47&#8211;38&#46;12&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>13&#46;30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">46&#46;39 &#40;42&#46;75&#8211;50&#46;02&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>14&#46;44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " rowspan="2" align="char" valign="top"><span class="elsevierStyleBold">&#8804;0&#46;001</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">33&#46;75 &#40;12&#46;70&#8211;74&#46;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">44 &#40;25&#46;80&#8211;87&#46;90&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="2" align="left" valign="top">Height &#40;m&#41;</td><td class="td" title="table-entry  " align="left" valign="top">1&#46;42 &#40;1&#46;37&#8211;1&#46;46&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>0&#46;19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">1&#46;52 &#40;1&#46;49&#8211;1&#46;56&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>0&#46;132&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " rowspan="2" align="char" valign="top"><span class="elsevierStyleBold">0&#46;003</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">1&#46;45 &#40;1&#8211;1&#46;74&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">1&#46;58 &#40;1&#46;20&#8211;1&#46;87&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="2" align="left" valign="top">Body mass index &#40;kg&#47;m<span class="elsevierStyleSup">2</span>&#41;</td><td class="td" title="table-entry  " align="left" valign="top">16&#46;72 &#40;15&#46;93&#8211;17&#46;50&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>3&#46;15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">19&#46;56 &#40;18&#46;72&#8211;20&#46;40&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>3&#46;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " rowspan="2" align="char" valign="top"><span class="elsevierStyleBold">&#8804;0&#46;001</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">15&#46;87 &#40;11&#46;30&#8211;28&#46;63&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">19&#46;01 &#40;13&#46;46&#8211;27&#46;13&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="2" align="left" valign="top">Shwachman-Kulczycki score</td><td class="td" title="table-entry  " align="left" valign="top">77&#46;93 &#40;76&#46;20&#8211;79&#46;67&#41;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>6&#46;94&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">&#8211;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " rowspan="2" align="char" valign="top">&#8211;</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">80 &#40;60&#8211;90&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">&#8211;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab1972913.png"
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        ]
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          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Distribution of patients with cystic fibrosis and healthy controls included in the study according to the clinical and laboratory markers&#46;</p>"
        ]
      ]
      5 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
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        "tabla" => array:2 [
          "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">CFTR</span>&#44; cystic fibrosis transmembrane regulator&#59; <span class="elsevierStyleItalic">N</span>&#44; number of patients&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">CFTR</span> mutation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">N</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Percentage &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Cumulative percentage&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;F508del&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">22&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">34&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">34&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;G542X&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">10&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">45&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;1812-1G&#62;A&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">48&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;c&#46;1717-1G&#62;A&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">51&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;N1303K&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">54&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">3120&#43;1G&#62;A&#47;R1066C&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">57&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;2183AA&#62;G&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">59&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;R1066C&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">60&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;R1162X&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">62&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;R553X&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">64&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">2183AA&#62;G&#47;2183AA&#62;G&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">65&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">R1162X&#47;R1162X&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">67&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">F508del&#47;Unknown mutation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">12&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">79&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">G542X&#47;Unknown mutation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">81&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">I507V&#47;Unknown mutation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">R1162X&#47;Unknown mutation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">84&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Unknown mutation&#47;Unknown mutation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">15&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">100&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">CFTR</span> mutations genotypes of patients with cystic fibrosis enrolled in the study &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>64&#41;&#46;</p>"
        ]
      ]
      6 => array:8 [
        "identificador" => "tbl0015"
        "etiqueta" => "Table 3"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
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            "identificador" => "at3"
            "detalle" => "Table "
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        "tabla" => array:2 [
          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">FVC &#40;&#37; predicted&#41;&#44; forced vital capacity&#59; FEV<span class="elsevierStyleInf">1</span> &#40;&#37; predicted&#41;&#44; forced expiratory volume in one second&#59; CFG&#44; group with cystic fibrosis&#59; CG&#44; control group&#46; &#42;&#44; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#46; The statistical analysis was performed regarding the Mann&#8211;Whitney test&#46; <span class="elsevierStyleItalic">&#945;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry  " rowspan="10" align="left" valign="top">FVC &#40;&#37; predicted&#41;&#42;</td><td class="td" title="table-entry  " align="left" valign="top">CFG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Mean<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">78&#46;60<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>20&#46;68&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">80&#46;50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">121&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">73&#46;44&#8211;83&#46;77&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Mean<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#46;78<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>11&#46;56&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">93&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">130&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">92&#46;89&#8211;98&#46;67&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="10" align="left" valign="top">FEV<span class="elsevierStyleInf">1</span> &#40;&#37; predicted&#41;&#42;</td><td class="td" title="table-entry  " align="left" valign="top">CFG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Mean<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">68&#46;98<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>22&#46;45&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">65&#46;50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">117&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">63&#46;38&#8211;74&#46;59&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Mean &#177; SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">91&#46;94<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>16&#46;96&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">125&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">87&#46;70&#8211;96&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " rowspan="10" align="left" valign="top">FEV<span class="elsevierStyleInf">1</span>&#47;FVC &#40;&#37; predicted&#41;&#42;</td><td class="td" title="table-entry  " align="left" valign="top">CFG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Mean<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">85&#46;66<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>11&#46;56&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">87&#46;50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">52&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">107&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">82&#46;77&#8211;88&#46;54&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Mean<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">98&#46;23<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>8&#46;95&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Median&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">100&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">74&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">125&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " align="" valign="top">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">95&#37; CI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">96&#8211;100&#46;47&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
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Article information

ISSN: 25310437
Original language: English

DOI:10.1016/j.pulmoe.2018.04.006

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Year/Month	Html	Pdf	Total

2024 November	10	3	13
2024 October	50	35	85
2024 September	47	30	77
2024 August	66	31	97
2024 July	41	41	82
2024 June	35	20	55
2024 May	43	36	79
2024 April	46	33	79
2024 March	47	19	66
2024 February	29	24	53
2024 January	28	31	59
2023 December	21	25	46
2023 November	47	53	100
2023 October	37	36	73
2023 September	23	42	65
2023 August	23	12	35
2023 July	37	31	68
2023 June	24	20	44
2023 May	50	29	79
2023 April	28	13	41
2023 March	96	32	128
2023 February	48	27	75
2023 January	32	22	54
2022 December	70	32	102
2022 November	61	45	106
2022 October	103	43	146
2022 September	38	51	89
2022 August	43	46	89
2022 July	45	86	131
2022 June	31	29	60
2022 May	32	40	72
2022 April	37	37	74
2022 March	39	48	87
2022 February	39	49	88
2022 January	55	50	105
2021 December	45	50	95
2021 November	33	47	80
2021 October	34	54	88
2021 September	34	49	83
2021 August	25	35	60
2021 July	29	28	57
2021 June	42	55	97
2021 May	86	71	157
2021 April	225	113	338
2021 March	87	52	139
2021 February	67	37	104
2021 January	55	38	93
2020 December	48	27	75
2020 November	52	38	90
2020 October	54	34	88
2020 September	62	37	99
2020 August	55	34	89
2020 July	74	37	111
2020 June	65	27	92
2020 May	78	30	108
2020 April	59	7	66
2020 March	63	23	86
2020 February	60	27	87
2020 January	61	27	88
2019 December	87	31	118
2019 November	77	21	98
2019 October	65	33	98
2019 September	62	24	86
2019 August	69	36	105
2019 July	50	25	75
2019 June	53	24	77
2019 May	65	29	94
2019 April	82	45	127
2019 March	128	68	196
2019 February	42	18	60
2019 January	56	32	88
2018 December	8	7	15
2018 November	2	1	3

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