Development of a Clinical Prediction Score Including Monocyte-to-Lymphocyte Ratio to Inform Tuberculosis Treatment Among Children With HIV: A Multicountry Study

Open Forum Infect Dis. 2022 Oct 14;9(11):ofac548. doi: 10.1093/ofid/ofac548. eCollection 2022 Nov.

Abstract

Background: Clinical pediatric tuberculosis (TB) diagnosis may lead to overdiagnosis particularly among children with human immunodeficiency virus (CHIV). We assessed the performance of monocyte-lymphocyte ratio (MLR) as a diagnostic biomarker and constructed a clinical prediction score to improve specificity of TB diagnosis in CHIV with limited access to microbiologic testing.

Methods: We pooled data from cohorts of children aged ≤13 years from Vietnam, Cameroon, and South Africa to validate the use of MLR ≥0.378, previously found as a TB diagnostic marker among CHIV. Using multivariable logistic regression, we created an internally validated prediction score for diagnosis of TB disease in CHIV.

Results: The combined cohort had 601 children (median age, 1.9 [interquartile range, 0.9-5.3] years); 300 (50%) children were male, and 283 (47%) had HIV. Elevated MLR ≥0.378 had sensitivity of 36% (95% confidence interval [CI], 23%-51%) and specificity of 79% (95% CI, 71%-86%) among CHIV in the validation cohort. A model using MLR ≥0.28, age ≥4 years, tuberculin skin testing ≥5 mm, TB contact history, fever >2 weeks, and chest radiograph suggestive of TB predicted active TB disease in CHIV with an area under the receiver operating characteristic curve of 0.85. A prediction score of ≥5 points had a sensitivity of 94% and specificity of 48% to identify confirmed TB, and a sensitivity of 82% and specificity of 48% to identify confirmed and unconfirmed TB groups combined.

Conclusions: Our score has comparable sensitivity and specificity to algorithms including microbiological testing and should enable clinicians to rapidly initiate TB treatment among CHIV when microbiological testing is unavailable.

Keywords: HIV; TB diagnosis; biomarker; pediatric TB; prediction score.