Optimizing Tuberculosis Diagnosis in Human Immunodeficiency Virus-Infected Inpatients Meeting the Criteria of Seriously Ill in the World Health Organization Algorithm

Abstract

Background: The World Health Organization (WHO) algorithm for the diagnosis of tuberculosis in seriously ill human immunodeficiency virus (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. The median age was 36 years, 65.5% were female, the median CD4 count was 89 cells/µL, and 35.3% were 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 >39°C, chest radiograph assessment, hemoglobin, and white cell count) was 0.811 (95% confidence interval, .802-.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%.

Conclusions: 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.

Figure 1.

Flow diagram for participant inclusion into the study. Abbreviations: CCF, congestive cardiac failure; COPD, chronic obstructive pulmonary disease; HIV, human immunodeficiency virus.

Figure 2.

Upper, Calibration plot for the assessment of variables included in a multivariate logistic regression model aimed at establishing a clinical prediction rule for the diagnosis of tuberculosis among 484 seriously ill human immunodeficiency virus (HIV)–infected participants presenting with a cough of any duration and 1 or more World Health Organization (WHO) danger sign. The line shows perfect calibration between observed and predicted tuberculosis. Lower, Discrimination of the multivariate logistic regression model aimed at establishing a clinical prediction rule for the diagnosis of tuberculosis among 484 seriously ill HIV-infected participants presenting with a cough of any duration and ≥1 WHO danger sign. Abbreviations: CI, confidence interval; ROC, receiver operating characteristic.