A Prediction Rule to Stratify Mortality Risk of Patients with Pulmonary Tuberculosis

PLoS One. 2016 Sep 16;11(9):e0162797. doi: 10.1371/journal.pone.0162797. eCollection 2016.

Abstract

Tuberculosis imposes high human and economic tolls, including in Europe. This study was conducted to develop a severity assessment tool for stratifying mortality risk in pulmonary tuberculosis (PTB) patients. A derivation cohort of 681 PTB cases was retrospectively reviewed to generate a model based on multiple logistic regression analysis of prognostic variables with 6-month mortality as the outcome measure. A clinical scoring system was developed and tested against a validation cohort of 103 patients. Five risk features were selected for the prediction model: hypoxemic respiratory failure (OR 4.7, 95% CI 2.8-7.9), age ≥50 years (OR 2.9, 95% CI 1.7-4.8), bilateral lung involvement (OR 2.5, 95% CI 1.4-4.4), ≥1 significant comorbidity-HIV infection, diabetes mellitus, liver failure or cirrhosis, congestive heart failure and chronic respiratory disease-(OR 2.3, 95% CI 1.3-3.8), and hemoglobin <12 g/dL (OR 1.8, 95% CI 1.1-3.1). A tuberculosis risk assessment tool (TReAT) was developed, stratifying patients with low (score ≤2), moderate (score 3-5) and high (score ≥6) mortality risk. The mortality associated with each group was 2.9%, 22.9% and 53.9%, respectively. The model performed equally well in the validation cohort. We provide a new, easy-to-use clinical scoring system to identify PTB patients with high-mortality risk in settings with good healthcare access, helping clinicians to decide which patients are in need of closer medical care during treatment.

MeSH terms

  • Adult
  • Cohort Studies
  • Female
  • Humans
  • Male
  • Middle Aged
  • Risk Factors
  • Tuberculosis, Pulmonary / mortality*

Grants and funding

This work was supported by Fundação Amélia de Mello/José de Mello Saúde and Sociedade Portuguesa de Pneumologia (SPP). This work was developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). NSO is a FCT (Fundação para a Ciência e Tecnologia) investigator. MS is an Associate FCT Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.