ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2026
A Point-of-Care Prediction Tool for Recurrent Tuberculosis.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundAn estimated 10% of tuberculosis (TB) survivors who recently completed treatment in India develop TB again. We sought to develop a parsimonious model for predicting TB recurrence that can help target post-treatment active case finding among the highest-risk TB survivors.
methodsThe TB Aftermath trial enrolled TB survivors at treatment completion from 6 public TB clinics in Maharashtra, India, and assessed participants at 6-month intervals. Our prediction endpoint was recurrent TB diagnosed within 18 months of treatment completion. Candidate variables included risk factors for recurrence identified a priori and lung function assessments. We used LASSO (Least Absolute Shrinkage and Selection Operator) regression to shortlist predictors and estimated probability of recurrence using logistic regression. We conducted internal validation, assessed discrimination, and plotted calibration. Model selection was based on practical utility and predictive accuracy. For our selected model, we identified a cutoff for achieving 90% sensitivity.
resultsAmong 1033 participants, we identified 85 (8.2%) recurrences. Several 5-item models measurable at treatment completion had moderate discrimination. Our selected model included sex, household income, body mass index, peak expiratory flow from spirometry, and history of multiple TB episodes. The selected model had a cross-validated c-statistic of .69 (95% CI: .56-.77) and acceptable calibration (intercept: .03 [95% CI: -.03, .09]; slope: .66 [.08-1.24]). TB survivors with a predicted probability ≥3.7% accounted for 90% of recurrences.
conclusionsA 5-item tool, measurable at treatment completion, showed moderate predictive accuracy for recurrent TB. At scale, a simple 5-item prediction tool may increase efficiency of post-treatment active case finding.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.