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Updated: Aug 19, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Predicting mortality in pulmonary tuberculosis: A systematic review of prognostic models
Aimée Bert-Dulanto1, Esteban A Alarcón-Braga1, Ana Castillo-Soto1
1Peruvian University of Applied Sciences, Lima - Perú, Av Alameda San Marcos 11, Chorrillos 15067, Lima, Peru.
Background:
Pulmonary tuberculosis is a highly prevalent disease in low-income countries; clinical prediction tools allow healthcare personnel to catalog patients with a higher risk of death in order to prioritize medical attention.
Methodology:
We conducted a literature search on prognostic models aimed to predict mortality in patients diagnosed with pulmonary tuberculosis. We included prospective and retrospective studies where prognostic models predicting mortality were either developed or validated in patients diagnosed with pulmonary tuberculosis. Three reviewers independently assessed the quality of the included studies using the PROBAST tool (Prediction model study Risk of Bias Assessment Tool). A narrative review of the characteristics of each model was conducted.
Results:
Six articles (n = 3553 patients) containing six prediction models were included in the review. Most studies (5 out of 6) were retrospective cohorts, only one study was a prospective case-control study. All the studies had a high risk of bias according to the PROBAST tool in the overall assessment. Regarding the applicability of the prediction models, three studies had a low concern of applicability, two high concern and one unclear concern. Five studies developed new prediction rules. In general, the presented models had a good discriminatory ability, with areas under the curve fluctuating between 0.65 up to 0.91.
Conclusion:
None of the prognostic models included in the review accurately predict mortality in patients with pulmonary tuberculosis, due to great heterogeneity in the population and a high risk of bias.
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