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

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.

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.

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.

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.

Not applicable.