An ensemble machine learning-based risk stratification tool for 30-day mortality prediction in critically ill cardiovascular patients.

ABSTRACT:

Background

Early mortality prediction in critically ill patients with cardiovascular disease remains challenging. This study aimed to develop and validate an ensemble machine learning (ML) model to predict 30-day mortality, comparing its performance with conventional severity scores and interrogating the incremental prognostic value of stress hyperglycemia ratio (SHR).

Methods

A retrospective cohort of 1,595 ICU patients with cardiovascular disease combined with diabetes (2008-2022) was analyzed. SHR was calculated as admission glucose divided by estimated average glucose (eAG) from HbA1c. Six ML models (eXtreme Gradient Boosting [XGBoost], Decision Tree [DT], Random Forest [RF], Artificial Neural Network [ANN], Logistic Regression [LR], and Support Vector Machine [SVM]) wer

SUBMITTER: Lei M 

PROVIDER: S-EPMC12487267 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature