Two clinical prediction tools to inform rapid tuberculosis treatment decision-making in children

Abstract

Abstract Background and Objectives In the absence of bacteriologic confirmation to diagnose tuberculosis (TB) in children, it is suggested that treatment should be initiated when sufficient clinical evidence of disease is available. However, it is unclear what clinical evidence is sufficient to make this decision. To identify children who would benefit from rapid initiation of TB treatment, we developed two clinical prediction tools. Methods We conducted a secondary analysis of a prospective intensified TB patient-finding intervention conducted in Pakistan in 2014-2016.TB disease was determined through either bacteriologic confirmation or a clinical diagnosis. We derived two tools; one uses Classification and Regression Tree (CART) analysis to develop decision trees while the second uses multivariable logistic regression to calculate a risk score. Results Of the 5,162 and 5,074 children included in the CART and prediction-score, respectively, 1,417 (27.5%) and 1,365 (26.9%) were eligible for TB treatment. CART identified abnormal chest radiographs and family history of TB as the most important predictors (area under the receiver operating characteristic curve [AUC]: 0.949). The final prediction-score model included age group (0-4, 5-9, 10-14), weight <5th percentile, cough, fever, weight loss, chest radiograph suggestive of TB disease, and family history of TB; the identified best cutoff score was 9 (AUC: 0.985%). Conclusions Use of clinical evidence was sufficient to accurately identify children who would benefit from treatment initiation. Our tools performed well compared to existing algorithms, though needs to be externally validated prior to operationalization.