Nomogram-based prediction of in-hospital mortality and embolic events in infective endocarditis

Background Infective endocarditis (IE) remains a life-threatening infection associated with high short-term mortality and frequent thromboembolic complications. Reliable bedside tools for early and individualized risk stratification remain limited. This study aimed to identify predictors of in-hospital mortality and new embolic events in adult patients with IE and to develop and internally validate nomogram-based prediction models. Methods This retrospective cohort study included consecutive adult patients with definite IE admitted to Guangzhou First People’s Hospital between January 2020 and December 2025, according to the 2023 Duke–ISCVID criteria. Six routinely available variables–age, vegetation size, white blood cell (WBC) count, log 2 -transformed N-terminal pro-B-type natriuretic peptide (NT-proBNP), log 2 -transformed systemic immune-inflammation index (SII), and Prognostic Nutritional Index (PNI)–were entered into multivariable logistic regression models. Nomograms were constructed and evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots with 1,000 bootstrap resamples, and decision curve analysis (DCA). Results A total of 62 patients were included. In-hospital mortality occurred in 8 patients (12.9%) and new embolic events in 13 patients (21.0%). For the embolic model, echocardiographic vegetation size and WBC count were independently associated with embolic risk, yielding an AUC of 0.882. For the mortality model, log 2 -NT-proBNP was independently associated with in-hospital death, with the nomogram demonstrating a discriminative AUC of 0.806. Calibration analysis confirmed consistency between the predicted and observed probabilities. DCA indicated a positive net clinical benefit across threshold probabilities of 1%–42% for mortality and 5%–66% for embolic events. Conclusion NT-proBNP was independently associated with in-hospital mortality, whereas vegetation size and WBC count were key predictors of embolic risk in patients with IE. These nomogram-based models provide practical tools for individualized bedside risk assessment. External validation in larger multicenter cohorts is warranted.