Identification of key predictors of hospital mortality in critically ill patients with embolic stroke using machine learning

Abstract

Embolic stroke (ES) is characterized by high morbidity and mortality. Its mortality predictors remain unclear. This study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: MIMIC-IV for training and internal validation, and eICU-CRD for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the ROC (AUROC). We adopted recursive feature elimination (RFE) for feature selection. We assessed model performance using three disease-severity scoring systems as benchmarks. Of the 1,566 and 207 ES patients enrolled in the two databases, there were 173 (15.70%), 73 (15.57%), and 36 (17.39%) hospital mortality in the training, internal validation, and external validation cohort, respectively. The random forest (RF) model had the largest AUROC (0.806) in the internal validation phase and was chosen as the best model. The AUROC of the RF compact (RF-COM) model containing the top six features identified by RFE was 0.795. In the external validation phase, the AUROC of the RF model was 0.838, and the RF-COM model was 0.830, outperforming other models. Our findings suggest that the RF model was the best model and the top six predictors of ES hospital mortality were Glasgow Coma Scale, white blood cell, blood urea nitrogen, bicarbonate, age, and mechanical ventilation.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

“…The prediction of mortality risk in AIS patients primarily focuses on ICU patients [ 22 , 23 , 37 ]. Wang et al developed a mortality prediction model for non-ICU AIS patients using various ML algorithms [ 24 ].…”

“…Its strength lies in precisely measuring the impact’s degree and direction that each feature exerts on the model’s output. In assessing mortality risk for AIS patients, research primarily focuses on those in intensive care unit (ICU) [ 22 , 23 ], which creates a gap in prognostic evaluations for non-ICU AIS patients. Studies involving non-ICU AIS patients face challenges related to imbalanced data distribution, with a mortality rate of less than 5%, and this imbalance remains unaddressed [ 24 ].…”