An Interpretable Machine Learning Model for Predicting In-hospital Death Risk in Patients with Cardiac Arrest: Based on US Medical Information Mart for Intensive Care Database Ⅳ 2.0

Abstract

Objective To develop and validate an interpretable machine learning model based on clinical characteristics to predict the risk of in-hospital death in patients with cardiac arrest. Methods First clinical data of cardiac arrest patients admitted to ICU within 24 h and outcomes during hospitalization were extracted from Medical Information Mart for Intensive Care database Ⅳ (MIMIC-Ⅳ) 2.0. Six models predicting in-hospital death risk of cardiac arrest patients were constructed based on machine learning algorithm: XGBoost model, light gradient boosting machine (LGBM) model, decision tree (DT) model, K-nearest neighbor (KNN) model, Logistic regression model, and random forest (RF) model. Receiver operator characteristic (ROC) curve, clinical decision curve and calibration curve were used to evaluate the 6 models. Shapley additive explanation (SHAP) algorithm was used to explain and evaluate the effects of different clinical features on the optimal model to increase its interpretability. Results A total of 1465 patients with cardiac arrest who met inclusion and exclusion criteria were included in the study. Among them, 773 patients survived and 692 died during hospitalization. After screening, a total of 82 clinical features were included for machine learning model construction. Compared with the other five models, the LGBM model had a higher area under the curve for predicting in-hospital death in cardiac arrest patients [0.834(95% CI: 0.688-0.894)], higher prediction accuracy for the risk of death than the Logistic regression model and XGBoost model (calibration degree: 0.166), better clinical decision performance, and displayed optimal overall performance. SHAP algorithm analysis showed that the three clinical features that had the greatest impact on the output of LGBM model were Glasgow eyes score, bicarbonate level and white blood cell count. Conclusion Based on a large public medical and health database, a machine learning model named LGBM has the best performance to predict the risk of in-hospital death in patients with cardiac arrest, which will be helpful to assist more efficient clinical disease management and more precise medical intervention.

Keywords

• cardiac arrest
• prognostic prediction
• machine learning
• shap algorithms
• medical information mart for intensive care database