A Treatment-Decision Score for HIV-Infected Children With Suspected Tuberculosis

Abstract

Background: Diagnosis of tuberculosis should be improved in children infected with HIV to reduce mortality. We developed prediction scores to guide antituberculosis treatment decision in HIV-infected children with suspected tuberculosis.

Methods: HIV-infected children with suspected tuberculosis enrolled in Burkina Faso, Cambodia, Cameroon, and Vietnam (ANRS 12229 PAANTHER 01 Study), underwent clinical assessment, chest radiography, Quantiferon Gold In-Tube (QFT), abdominal ultrasonography, and sample collection for microbiology, including Xpert MTB/RIF (Xpert). We developed 4 tuberculosis diagnostic models using logistic regression: (1) all predictors included, (2) QFT excluded, (3) ultrasonography excluded, and (4) QFT and ultrasonography excluded. We internally validated the models using resampling. We built a score on the basis of the model with the best area under the receiver operating characteristic curve and parsimony.

Results: A total of 438 children were enrolled in the study; 251 (57.3%) had tuberculosis, including 55 (12.6%) with culture- or Xpert-confirmed tuberculosis. The final 4 models included Xpert, fever lasting >2 weeks, unremitting cough, hemoptysis and weight loss in the past 4 weeks, contact with a patient with smear-positive tuberculosis, tachycardia, miliary tuberculosis, alveolar opacities, and lymph nodes on the chest radiograph, together with abdominal lymph nodes on the ultrasound and QFT results. The areas under the receiver operating characteristic curves were 0.866, 0.861, 0.850, and 0.846, for models 1, 2, 3, and 4, respectively. The score developed on model 2 had a sensitivity of 88.6% and a specificity of 61.2% for a tuberculosis diagnosis.

Conclusions: Our score had a good diagnostic performance. Used in an algorithm, it should enable prompt treatment decision in children with suspected tuberculosis and a high mortality risk, thus contributing to significant public health benefits.

Trial registration: ClinicalTrials.gov NCT01331811.