Personal View
Do high rates of empirical treatment undermine the potential effect of new diagnostic tests for tuberculosis in high-burden settings?

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Summary

In tuberculosis-endemic settings, patients are often treated empirically, meaning that they are placed on treatment based on clinical symptoms or tests that do not provide a microbiological diagnosis (eg, chest radiography). New tests for tuberculosis, such as the Xpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA), are being implemented at substantial cost. To inform policy and rationally drive implementation, data are needed for how these tests affect morbidity, mortality, transmission, and population-level tuberculosis burden. If people diagnosed by use of new diagnostics would have received empirical treatment a few days later anyway, then the incremental benefit might be small. Will new diagnostics substantially improve outcomes and disease burden, or simply displace empirical treatment? Will the extent and accuracy of empirical treatment change with the introduction of a new test? In this Personal View, we review emerging data for how empirical treatment is frequently same-day, and might still be the predominant form of treatment in high-burden settings, even after Xpert implementation; and how Xpert might displace so-called true-positive, rather than false-positive, empirical treatment. We suggest types of studies needed to accurately assess the effect of new tuberculosis tests and the role of empirical treatment in real-world settings. Until such questions can be addressed, and empirical treatment is appropriately characterised, we postulate that the estimated population-level effect of new tests such as Xpert might be substantially overestimated.

Introduction

Although several factors, including reduction of poverty and improved access to treatment, are crucial to reduce the global burden of tuberculosis, accurate and rapid diagnostic tests are a major unmet need. Xpert MTB/RIF—an automated real-time PCR platform for diagnosis of tuberculosis and detection of rifampicin resistance—is endorsed by WHO1, 2 and the USA Food and Drug Administration and is undergoing implementation in several high-burden countries.3 Xpert is usable at the point-of-care4, 5 and can detect about two-thirds of smear-negative tuberculosis cases in less than 2 h.6 The widespread implementation of Xpert will need substantial investment by international donors and governments of resource-poor countries.7

Modelling studies have indicated that accurate and potentially same-day tuberculosis diagnostics could reduce mortality by 20–35% by enabling earlier initiation of tuberculosis treatment.8 However, in HIV-endemic settings with a high tuberculosis-related mortality, clinicians compensate for the shortcomings of smear microscopy (frequently the only routinely available tuberculosis test) with the initiation of treatment on the basis of clinical symptoms, less specific tests (such as chest radiography), or absence of a response to broad-spectrum antibiotics.9, 10 The initiation of treatment in the absence of a bacteriologically confirmed diagnosis is often referred to as empirical tuberculosis treatment.

In settings with high rates of empirical treatment, the effect of Xpert and other new tuberculosis tests such as the urine LAM (lipoarabinomannan) lateral flow assay11 on individual-level outcomes and population-level epidemiology might be lower than predicted (table). Although the number of bacteriologically confirmed diagnoses will increase with the roll-out of Xpert, how many of these newly detected patients would have been placed on treatment in the absence of Xpert, and when this would have occurred, is unknown. A proposed benefit of Xpert is improved outcomes (eg, lower mortality) in the sickest individuals; however, doctors are most likely to treat the same patients empirically (and treat them rapidly), such that the incremental benefit of Xpert might be diminished. Thus, certain key questions remain: will Xpert actually decrease the time to treatment initiation in high-burden settings with high rates of empirical treatment to an extent that affects outcomes for patients and ongoing transmission, or will it only replace empirical tuberculosis treatment that would otherwise occur near the same time? Will Xpert change empirical tuberculosis treatment practice, reduce the proportion of false-negative diagnoses, and reduce the proportion of patients with false-positive results who are placed on treatment inappropriately? Might some patients with tuberculosis but a negative Xpert result not receive treatment because of increased confidence in Xpert?

Section snippets

Drivers of empirical treatment

The clinical basis for empirical tuberculosis treatment varies across settings in accordance with factors that contribute to a pretest probability of a patient having tuberculosis or a poor outcome or both, which is weighted against a variable and subjective threshold for treatment initiation (figure). Such factors include baseline tuberculosis prevalence (eg, among patients with HIV with advanced immunosuppression), a clinical presentation suggestive of tuberculosis, results (if any) of

Xpert MTB/RIF and how its effect might be modulated by empirical treatment

Xpert detects 40–60% more tuberculosis cases than does microscopy.6 Because of the high rates of empirical treatment for smear-negative patients, it is unclear which patients empirically treated for smear-negative tuberculosis overlap with those who would be detected by Xpert. A multicentre study25 showed that Xpert reduced the proportion of untreated culture-positive patients from 40% to 15%; however, this reduction in dropouts can be offset by empirical treatment in pragmatic settings. For

Projections for the effect of Xpert

Since data for the population-level effect and cost-effectiveness of Xpert (eg, from continuing community-randomised trials) will probably not be available until mid-2014, projecting the effect of Xpert's deployment has become a priority for mathematical modellers. Although several analyses have assessed the potential economic implications of Xpert roll-out,7, 28, 29, 30 and other data-driven studies have estimated the effect of Xpert testing on individual patient-level outcomes,4, 25, 26, 27

Implications of high rates of empirical treatment for Xpert MTB/RIF placement

The extent to which Xpert will improve the number of true-positive patients with tuberculosis on treatment will depend on the setting-specific appropriateness and timing of empirical tuberculosis treatment. For example, in settings in which empirical treatment is prevalent and rapid (within 2–3 days), Xpert is unlikely to substantially improve outcomes for patients. However, in settings in which empirical treatment is delayed, the reduced time to diagnosis (from weeks to days) offered by Xpert

A way forward: collection of key data to inform on the true effect of Xpert

If we are to better understand the potential effect of Xpert on clinical and epidemiological outcomes in high-burden settings with frequent empirical diagnosis, we need to collect data that enable inference of outcomes when Xpert is available as opposed to scenarios when it is not. Thus, whether roll-outs of new diagnostic technologies reduce false-negative treatment decisions, and hence, affect morbidity and mortality, needs clarification. Cohort studies done before and after the introduction

Conclusion

Xpert is a substantial advance over smear microscopy, but the population-level effect of Xpert scale-up remains unclear because of uncertainty about the extent and accuracy of empirical treatment patterns in high-burden settings. Compared with the existing standard of care in such settings, in which smear microscopy is the only available tuberculosis test, Xpert might increase true-positive treatment decisions, but, depending on how much empirical treatment is displaced, the major effects of

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