Development and Validation of a Risk Mortality Prediction Model for Patients with Pulmonary Tuberculosis Complicated by Severe Community-Acquired Pneumonia in the Intensive Care Unit.

Purpose: The mortality rate from pulmonary tuberculosis (PTB) complicated by severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU) remains high. We aimed to develop a rapid and simple model for the early assessment and stratification of prognosis in these patients.

Patients And Methods: All adult patients with PTB complicated by SCAP admitted to the ICU of a tertiary hospital in Chengdu, Sichuan, China between 2019 and 2021 (development cohort) and 2022 (validation cohort) were retrospectively included. Data on demographics, comorbidities, laboratory values, and interventions were collected. The outcome was the 28-day mortality. Stepwise backward multivariate Cox analysis was used to develop a mortality risk prediction score model. Receiver operating characteristic (ROC) and calibration curves were used to evaluate the model's predictive efficiency. Decision curve analysis (DCA) was used to validate the model's clinical value and impact on decision making.

Results: Overall, 357 and 168 patients were included in the development and validation cohorts, respectively. The Pulmonary Tuberculosis Severity Index (PTSI) score included long-term use of glucocorticoid, body mass index (BMI) <18.5 kg/m, diabetes, blood urea nitrogen (BUN) ≥7.14 mmol/L, PO/FiO <150 mmHg, and vasopressor use. The area under the ROC curve (AUC) values were 0.817 (95% CI: 0.772-0.863) and 0.814 for the development and validation cohorts, respectively. The PTSI score had a higher AUC than the APACHE II, SOFA, and CURB-65 score. The calibration curves indicated good calibration in both cohorts. The DCA of the PTSI score indicated the high clinical application of the model compared with the APACHE II and SOFA scores.

Conclusion: This prognostic tool was designed to rapidly evaluate the 28-day mortality risk in individuals with PTB complicated by SCAP. It can stratify this patient group into relevant risk categories, guide targeted interventions, and enhance clinical decision making, thereby optimizing patient care and improving outcomes.