Journal Article

Derivation and external validation of a risk score for predicting HIV-associated tuberculosis to support case finding and preventive therapy scale-up: A cohort study

Agizew, Tefera, Boyd, Rosanna, Auld, Andrew F., Date, Anand, Liu, Yuliang, Mathoma, Anikie, Serumola, Christopher, Finlay, Alyssa, Rankgoane-Pono, Goabaone, Shepherd, James C., Charalambous, Salome, Grant, Alison D., Kerkhoff, Andrew D., Ellerbrock, Tedd V., Fielding, Katherine, Hanifa, Yasmeen, Mathebula-Modongo, Unami, Shiraishi, Ray W., Bicego, George, Wood, Robin, Pono, Pontsho, Alexander, Heather

2021

Overview

Among people living with HIV (PLHIV), more flexible and sensitive tuberculosis (TB) screening tools capable of detecting both symptomatic and subclinical active TB are needed to (1) reduce morbidity and mortality from undiagnosed TB; (2) facilitate scale-up of tuberculosis preventive therapy (TPT) while reducing inappropriate prescription of TPT to PLHIV with subclinical active TB; and (3) allow for differentiated HIV-TB care. We used Botswana XPRES trial data for adult HIV clinic enrollees collected during 2012 to 2015 to develop a parsimonious multivariable prognostic model for active prevalent TB using both logistic regression and random forest machine learning approaches. A clinical score was derived by rescaling final model coefficients. The clinical score was developed using southern Botswana XPRES data and its accuracy validated internally, using northern Botswana data, and externally using 3 diverse cohorts of antiretroviral therapy (ART)-naive and ART-experienced PLHIV enrolled in XPHACTOR, TB Fast Track (TBFT), and Gugulethu studies from South Africa (SA). Predictive accuracy of the clinical score was compared with the World Health Organization (WHO) 4-symptom TB screen. Among 5,418 XPRES enrollees, 2,771 were included in the derivation dataset; 67% were female, median age was 34 years, median CD4 was 240 cells/μL, 189 (7%) had undiagnosed prevalent TB, and characteristics were similar between internal derivation and validation datasets. Among XPHACTOR, TBFT, and Gugulethu cohorts, median CD4 was 400, 73, and 167 cells/μL, and prevalence of TB was 5%, 10%, and 18%, respectively. Factors predictive of TB in the derivation dataset and selected for the clinical score included male sex (1 point), ≥1 WHO TB symptom (7 points), smoking history (1 point), temperature >37.5°C (6 points), body mass index (BMI) <18.5kg/m2 (2 points), and severe anemia (hemoglobin <8g/dL) (3 points). Sensitivity using WHO 4-symptom TB screen was 73%, 80%, 94%, and 94% in XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively, but increased to 88%, 87%, 97%, and 97%, when a clinical score of ≥2 was used. Negative predictive value (NPV) also increased 1%, 0.3%, 1.6%, and 1.7% in XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively, when the clinical score of ≥2 replaced WHO 4-symptom TB screen. Categorizing risk scores into low (<2), moderate (2 to 10), and high-risk categories (>10) yielded TB prevalence of 1%, 1%, 2%, and 6% in the lowest risk group and 33%, 22%, 26%, and 32% in the highest risk group for XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively. At clinical score ≥2, the number needed to screen (NNS) ranged from 5.0 in Gugulethu to 11.0 in XPHACTOR. Limitations include that the risk score has not been validated in resource-rich settings and needs further evaluation and validation in contemporary cohorts in Africa and other resource-constrained settings. The simple and feasible clinical score allowed for prioritization of sensitivity and NPV, which could facilitate reductions in mortality from undiagnosed TB and safer administration of TPT during proposed global scale-up efforts. Differentiation of risk by clinical score cutoff allows flexibility in designing differentiated HIV-TB care to maximize impact of available resources.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adult / Anti-Retroviral Agents - adverse effects / Anti-Retroviral Agents - therapeutic use / Antiretroviral drugs / Antiretroviral therapy / Antitubercular Agents - adverse effects / Antitubercular Agents - therapeutic use / Biology and Life Sciences / Body mass index / Botswana - epidemiology / CD4 antigen / Clinical Trials as Topic / Clinics / Cohort analysis / Coinfection / Complications and side effects / Computer and Information Sciences / Datasets / Early Diagnosis / Earth Sciences / Female / Hemoglobin / HIV / HIV infection / HIV Infections - diagnosis / HIV Infections - drug therapy / HIV Infections - epidemiology / HIV Infections - virology / HIV Long-Term Survivors / Human immunodeficiency virus / Humans / Learning algorithms / Machine learning / Male / Mass Screening / Medicine and Health Sciences / Middle Aged / Morbidity / Mortality / Physical Sciences / Predictive Value of Tests / Prevalence / Prevention / Preventive Health Services / Prognosis / Reproducibility of Results / Research and Analysis Methods / Risk Assessment / Risk Factors / Scaling / Social Sciences / Tuberculosis / Tuberculosis - diagnosis / Tuberculosis - epidemiology / Tuberculosis - microbiology / Tuberculosis - prevention & control