Optimizing Tuberculosis Diagnosis	in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm

Griesel, Rulan

Optimizing Tuberculosis Diagnosis in HIV-Infected Inpatients Meeting the Criteria of Seriously Ill in the WHO Algorithm

Master Thesis

2018

Abstract

Background The WHO algorithm for the diagnosis of tuberculosis in seriously ill HIV-infected patients lacks a firm evidence base. We aimed to develop a clinical prediction rule for the diagnosis of tuberculosis and to determine the diagnostic utility of the Xpert MTB/RIF assay in seriously ill HIV-infected patients. Methods We conducted a prospective study among HIV-infected inpatients with any cough duration and WHO-defined danger signs. Culture-positive tuberculosis from any site was the reference standard. A priori selected variables were assessed for univariate associations with tuberculosis. The most predictive variables were assessed in a multivariate logistic regression model and used to establish a clinical prediction rule for diagnosing tuberculosis. Results We enrolled 484 participants: median age 36 years, 65·5% female, median CD4 count 89 cells/μL, and 35·3% on antiretroviral therapy. Tuberculosis was diagnosed in 52·7% of participants. The c-statistic of our clinical prediction rule (variables: cough ≥14 days, unable to walk unaided, temperature >39oC, chest radiograph assessment, haemoglobin, and white cell count) was 0·811 (95%CI 0·802, 0·819). The classic tuberculosis symptoms (fever, night sweats, weight loss) added no discriminatory value in diagnosing tuberculosis. Xpert MTB/RIF assay sensitivity was 86·3% and specificity was 96·1%. Conclusion Our clinical prediction rule had good diagnostic utility for tuberculosis among seriously ill HIV-infected inpatients. Xpert MTB/RIF assay, incorporated into the updated 2016 WHO algorithm, had high sensitivity and specificity in this population. Our findings could facilitate improved diagnosis of tuberculosis among seriously ill HIV-infected inpatients in resource-constrained settings.

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