Nomogram for predicting mortality in hospitalized patients with infective endocarditis.

This study aimed to develop a nomogram for accurately predicting in-hospital mortality in patients with infective endocarditis (IE). We conducted a retrospective analysis of clinical, echocardiographic, and laboratory data from IE patients admitted between January 2010 and September 2024. 252 IE patients from the Second Hospital of Lanzhou University were included in the training cohort, while 65 IE patients from the First Hospital of Lanzhou University were enrolled for external validation. The least absolute shrinkage and selection operator (LASSO) regression method was used to identify factors associated with in-hospital mortality. A nomogram was constructed using multivariate logistic regression. Model performance was assessed using receiver operating characteristic (ROC) curve and calibration curve. Clinical utility was evaluated through decision curve analysis (DCA) and clinical impact curve (CIC). The nomogram included five independent risk factors: embolic events, vegetation size ≥ 10 mm, moderate or higher pulmonary hypertension, hydropericardium, and surgery. The area under the curve (AUC) of the nomogram in the training cohort was 0.850 (95% CI: 0.794-0.906), and external validation cohort was 0.819 (95% CI: 0.693-0.946). The calibration plot demonstrated excellent prediction consistency. Both DCA and CIC confirmed the clinical utility of the nomogram. We developed and validated a nomogram for predicting in-hospital mortality in patients with IE. The model demonstrated excellent performance and provided a useful tool to assist clinicians in decision-making and patient management.