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Background <p>Infective endocarditis (IE) is a fatal cardiovascular disease with varied clinical manifestations but rapid progression. A series of existing risk models helped identify IE patients with high risk, but the imperfect predictive performance and limited application called for better predictive systems.</p> Methods <p>The single-centered, retrospective observational study applied four machine learning methods for predictive model construction: LASSO logistic regression, random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN). A 10-fold cross-validated area under the receiver operating characteristic curve (AUC-ROC) was used for performance evaluation.</p> Results <p>A total of 1705 patients with IE were enrolled in the study, with 119 in-hospital deaths and 178 deaths after 6-month follow-up. RF achieved the highest AUC-ROCs for in-hospital and six-month mortality prediction (in-hospital: 0.83, 6-month: 0.85). RF was also applied to assess variable importance. The following variables were selected by RF as top important predictors for both in-hospital and six-month mortality prediction: total bilirubin, N-terminal pro-B-type natriuretic peptide, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age.</p> Conclusions <p>A risk model with machine learning approach was integrated in purpose of prognosis prediction in IE patients, helping rapid risk stratification and in-time management clinically.</p> Clinical trial number <p>Not applicable.</p>

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Mortality predicting models for patients with infective endocarditis: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Yang Zi-yang,
  • Wang Qi,
  • Xingyan Liu,
  • Haolin Li,
  • Shouhong Wang,
  • Danqing Yu,
  • Xuebiao Wei

摘要

Background

Infective endocarditis (IE) is a fatal cardiovascular disease with varied clinical manifestations but rapid progression. A series of existing risk models helped identify IE patients with high risk, but the imperfect predictive performance and limited application called for better predictive systems.

Methods

The single-centered, retrospective observational study applied four machine learning methods for predictive model construction: LASSO logistic regression, random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN). A 10-fold cross-validated area under the receiver operating characteristic curve (AUC-ROC) was used for performance evaluation.

Results

A total of 1705 patients with IE were enrolled in the study, with 119 in-hospital deaths and 178 deaths after 6-month follow-up. RF achieved the highest AUC-ROCs for in-hospital and six-month mortality prediction (in-hospital: 0.83, 6-month: 0.85). RF was also applied to assess variable importance. The following variables were selected by RF as top important predictors for both in-hospital and six-month mortality prediction: total bilirubin, N-terminal pro-B-type natriuretic peptide, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age.

Conclusions

A risk model with machine learning approach was integrated in purpose of prognosis prediction in IE patients, helping rapid risk stratification and in-time management clinically.

Clinical trial number

Not applicable.