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Objective <p>This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnosed with infective endocarditis (IE), using retrospective data from a single tertiary care center. Additionally, key prognostic features were identified through model interpretability analysis.</p> Methods <p>A cohort of 444 patients with IE was retrospectively assessed and randomly divided into a training set (<i>n</i> = 310) and a test set (<i>n</i> = 134). Following feature selection, a Cox proportional hazards model and four ML-based survival models: Random Survival Forest, Extremely Randomized Survival Trees (EST), eXtreme Gradient Boosting, and Support Vector Machine were established. Model performance was assessed using the concordance index (C-index), time-dependent area under the curve (AUC), and Kaplan–Meier survival analysis. The model demonstrating optimal performance underwent interpretability analysis using Shapley additive explanations and an exploratory online tool was developed to demonstrate its functionality.</p> Results <p>Nine predictors were selected for model construction: hemoglobin concentration, presence of severe valvular regurgitation, occurrence of septic shock, New York Heart Association (NYHA) classification, serum albumin level, intracerebral hemorrhage, neutrophil percentage, history of coronary heart disease, and receipt of surgical intervention. The EST model demonstrated the highest predictive performance in the test set (C-index: 0.852), with the smallest discrepancy in performance between the training and test datasets, indicating favorable generalizability. Time-dependent AUCs at 30, 90, and 180 days were 0.957, 0.925, and 0.855, respectively. Surgical intervention was modeled as a protective factor, associated with lower mortality risk, while intracerebral hemorrhage, higher NYHA class, severe valvular regurgitation, and septic shock were associated with increased risk.</p> Conclusions <p>The EST model demonstrated a high predictive accuracy for 6-month mortality in patients with IE, outperforming both conventional and other ML models. Its robust performance and interpretability suggest that, following rigorous external and multicenter validation, it may serve as a hypothesis-generating tool&#xa0;for risk stratification. It is not yet validated for direct clinical application and should currently be considered exploratory.</p>

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Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月6日

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  • Xiujuan Wu,
  • Yue Han,
  • Weili Liu,
  • Song Lin,
  • Jie Yuan,
  • Yannan Ma,
  • Chuanyu Gao

摘要

Objective

This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnosed with infective endocarditis (IE), using retrospective data from a single tertiary care center. Additionally, key prognostic features were identified through model interpretability analysis.

Methods

A cohort of 444 patients with IE was retrospectively assessed and randomly divided into a training set (n = 310) and a test set (n = 134). Following feature selection, a Cox proportional hazards model and four ML-based survival models: Random Survival Forest, Extremely Randomized Survival Trees (EST), eXtreme Gradient Boosting, and Support Vector Machine were established. Model performance was assessed using the concordance index (C-index), time-dependent area under the curve (AUC), and Kaplan–Meier survival analysis. The model demonstrating optimal performance underwent interpretability analysis using Shapley additive explanations and an exploratory online tool was developed to demonstrate its functionality.

Results

Nine predictors were selected for model construction: hemoglobin concentration, presence of severe valvular regurgitation, occurrence of septic shock, New York Heart Association (NYHA) classification, serum albumin level, intracerebral hemorrhage, neutrophil percentage, history of coronary heart disease, and receipt of surgical intervention. The EST model demonstrated the highest predictive performance in the test set (C-index: 0.852), with the smallest discrepancy in performance between the training and test datasets, indicating favorable generalizability. Time-dependent AUCs at 30, 90, and 180 days were 0.957, 0.925, and 0.855, respectively. Surgical intervention was modeled as a protective factor, associated with lower mortality risk, while intracerebral hemorrhage, higher NYHA class, severe valvular regurgitation, and septic shock were associated with increased risk.

Conclusions

The EST model demonstrated a high predictive accuracy for 6-month mortality in patients with IE, outperforming both conventional and other ML models. Its robust performance and interpretability suggest that, following rigorous external and multicenter validation, it may serve as a hypothesis-generating tool for risk stratification. It is not yet validated for direct clinical application and should currently be considered exploratory.