Interferon release does not add discriminatory value to smear-negative HIV-tuberculosis algorithms.

MX Rangaka; HP Gideon; KA Wilkinson; M Pai; J Mwansa-Kambafwile; G Maartens; JR Glynn ORCID logo; A Boulle; K Fielding ORCID logo; R Goliath; +3 more... R Titus; S Mathee; RJ Wilkinson; (2012) Interferon release does not add discriminatory value to smear-negative HIV-tuberculosis algorithms. The European respiratory journal, 39 (1). pp. 163-171. ISSN 0903-1936 DOI: 10.1183/09031936.00058911
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Clinical algorithms for evaluating HIV-infected individuals for tuberculosis (TB) prior to isoniazid preventive therapy (IPT) perform poorly, and interferon-γ release assays (IGRAs) have moderate accuracy for active TB. It is unclear whether, when used as adjunct tests, IGRAs add any clinical discriminatory value for active TB diagnosis in the pre-IPT assessment. 779 sputum smear-negative HIV-infected persons, established on or about to commence combined antiretroviral therapy (ART), were screened for TB prior to IPT. Stepwise multivariable logistic regression was used to develop clinical prediction models. The discriminatory ability was assessed by receiver operator characteristic area under the curve (AUC). QuantiFERON-TB Gold in-tube (QFT-GIT) was evaluated. The prevalence of smear-negative TB by culture was 6.4% (95% CI 4.9-8.4%). Used alone, QFT-GIT and the tuberculin skin test (TST) had comparable performance; the post-test probability of disease based on single negative tests was 3-4%. In a multivariable model, the QFT-GIT test did not improve the ability of a clinical algorithm, which included not taking ART, weight <60 kg, no prior history of TB, any one positive TB symptom/sign (cough ≥ 2 weeks) and CD4+ count <250 cells per mm(3), to discriminate smear-negative culture-positive and -negative TB (72% to 74%; AUC comparison p=0.33). The TST marginally improved the discriminatory ability of the clinical model (to 77%, AUC comparison p=0.04). QFT-GIT does not improve the discriminatory ability of current TB screening clinical algorithms used to evaluate HIV-infected individuals for TB ahead of preventive therapy. Evaluation of new TB diagnostics for clinical relevance should follow a multivariable process that goes beyond test accuracy.

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