An Integrated Clinical and Biomarker Model Using Penalized Regression to Predict In-Hospital Mortality in Acute Pulmonary Embolism

Background: Early mortality risk stratification is essential in acute pulmonary embolism (PE). Integrating clinical variables with biomarkers may enhance prognostic accuracy beyond established tools. Methods: In a retrospective cohort of 322 patients with confirmed acute PE, we evaluated syncope, right-ventricular (RV) dysfunction, systolic blood pressure (SBP) and biochemical markers as candidate predictors of in-hospital mortality. A penalized logistic regression model using LASSO (least absolute shrinkage and selection operator) was developed and internally validated with five-fold cross-validation and 200 bootstrap repetitions. Discrimination, calibration and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), Brier score and decision-curve analysis (DCA). Results: In-hospital mortality was 5.6% (n = 18). LASSO retained four predictors: syncope, RV dysfunction, lower SBP and higher troponin levels. The optimism-corrected AUC was 0.70 (95% CI 0.63–0.77), with strong calibration (Brier score 0.066). DCA showed that the model provided greater net benefit than treat-all, treat-none, and sPESI strategies across threshold probabilities of approximately 7–25%, supporting its potential value for early triage. NT-proBNP, D-dimer and lactate did not add incremental predictive information after penalization. Conclusions: A simple, interpretable model integrating clinical parameters and troponin demonstrates good predictive performance and favorable clinical utility for early mortality risk estimation in acute PE. External validation is required before broader implementation.