학술논문
A Prediction Rule to Stratify Mortality Risk of Patients with Pulmonary Tuberculosis.
Document Type
Article
Author
Bastos, Helder Novais; Osório, Nuno S.; Castro, António Gil; Ramos, Angélica; Carvalho, Teresa; Meira, Leonor; Araújo, David; Almeida, Leonor; Boaventura, Rita; Fragata, Patrícia; Chaves, Catarina; Costa, Patrício; Portela, Miguel; Ferreira, Ivo; Magalhães, Sara Pinto; Rodrigues, Fernando; Sarmento-Castro, Rui; Duarte, Raquel; Guimarães, João Tiago; Saraiva, Margarida
Source
Subject
*TUBERCULOSIS treatment
*TUBERCULOSIS risk factors
*CONGESTIVE heart failure treatment
*SEVERITY of illness index
*PREDICTION models
*DEATH rate
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Language
ISSN
1932-6203
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. [ABSTRACT FROM AUTHOR]