Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis.Retrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer-Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted.The research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems.The nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.© 2024. The Author(s).