Development and validation of a nomogram for predicting 1-year mortality in infective endocarditis patients

Infective endocarditis (IE) is a serious cardiovascular infectious disease with persistently high mortality rates. Accurate prediction of long-term prognosis is crucial for developing individualized treatment strategies.

We retrospectively analyzed clinical data from 383 patients with confirmed IE, randomly divided into training (n = 268) and validation (n = 115) cohorts at a 7:3 ratio. Univariate Cox regression, LASSO Cox regression, and multivariate Cox regression were sequentially used to identify independent prognostic factors and construct a nomogram prediction model. Model performance was evaluated using concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Multivariate analysis identified five independent prognostic factors: age (HR = 1.018, 95% CI: 1.004-1.033, P = 0.012), heart failure (HR = 5.759, 95% CI: 2.999-11.060, P < 0.001), embolic events (HR = 3.647, 95% CI: 2.276-5.844, P < 0.001), vegetation diameter >10 mm (HR = 2.316, 95% CI: 1.464-3.664, P < 0.001), and surgical treatment (HR = 0.158, 95% CI: 0.094-0.267, P < 0.001). The nomogram demonstrated excellent discriminative ability with C-index of 0.879 in the training cohort and AUC of 0.965 (95% CI: 0.945-0.985) in the training cohort and 0.939 (95% CI: 0.891-0.986) in the validation cohort. Calibration curves showed good agreement between predicted and observed values, and DCA confirmed the clinical utility of the model.

The nomogram model developed in this study accurately predicts 1-year mortality risk in IE patients with excellent discrimination and calibration, providing a powerful tool for clinical risk stratification and treatment decision-making.