A clinically interpretable prediction model for acute mortality in patients with pneumonia requiring mechanical ventilation

Patients with pneumonia admitted to the intensive care unit (ICU) requiring early mechanical ventilation are at high risk for short-term mortality. Early risk assessment is crucial for timely intervention, optimal resource allocation, and improved outcomes. This study aimed to develop and validate a clinically interpretable prediction model for predicting 7-day mortality in this high-risk population. Data from the MIMIC-IV database were used for model development and internal validation, while the SCRIPT and PLAGH cohorts served as external validation cohorts to assess generalizability across geographical regions and ethnicities. Feature selection was conducted using Lasso regression. We compared nine machine learning algorithms and selected the optimal model based on performance metrics including the area under the curve (AUC), calibration, decision curve analysis (DCA) and precision-recall (PR) curves. Model interpretability was enhanced through a nomogram for individualized risk visualization, supplemented by SHAP analysis to rank feature importance. A freely accessible web-based calculator was developed to facilitate individualized risk assessment in clinical practice. This study included 6,720 patients from MIMIC-IV, 492 from SCRIPT, and 136 from PLAGH. Eight predictors were selected: age, SOFA score, heart rate, respiratory rate, urine output, hemoglobin, lactate, and tracheostomy. The logistic regression model demonstrated the best performance, with an AUC of 0.79 (95%CI: 0.73–0.85) in the SCRIPT cohort and 0.90 (95% CI: 0.83–0.96) in the PLAGH cohort, outperforming the SOFA score (DeLong test, P < 0.01). The model exhibited good calibration, and DCA confirmed its clinical net benefit. A user-friendly web-based calculator was developed to facilitate clinician use. In this study, we developed and multicentrically validated an interpretable prediction model to predict short-term mortality in patients with pneumonia requiring early mechanical ventilation. The model demonstrated robust and consistent performance across multiple independent cohorts. This tool may assist clinicians in early risk stratification, facilitating timely intervention for high-risk patients while avoiding unnecessary treatments in those at low risk. Implementation as a freely available web-based calculator may further enhance care efficiency by aligning interventions with patient risk.