Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring. (February 2022)

• Record Type: Journal Article
• Authors: Yijing, Li; Wenyu, Ye; Kang, Yang; Shengyu, Zhang; Xianliang, He; Xingliang, Jin; Cheng, Wang; Zehui, Sun; Mengxing, Liu
• Is Part Of: Computer methods and programs in biomedicine. Volume 214(2022)
• Publication Date: 2022-02
• Subjects: Cardiac arrest -- Prediction -- Machine learning -- Vital signs -- Bedside monitoring; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106568
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A real-time and interpretable machine learning model for predicting cardiac arrest in critically ill patients is proposed. 43 features are extracted based on four vital signs which are commonly used in bedside vital signs monitoring. SHAP value is used to capture the overall and real-time interpretability of the model. Compared with previous studies, this model is more suitable for bedside vital signs monitoring to predict cardiac arrest. The sensitivity and specificity were 0.86 and 0.85, respectively. Abstract: Purpose: Cardiac arrest (CA) is the most serious death-related event in critically ill patients and the early detection of CA is beneficial to reduce mortality according to clinical research. This study aims to develop and verify a real-time, interpretable machine learning model, namely cardiac arrest prediction index (CAPI), to predict CA of critically ill patients based on bedside vital signs monitoring. Methods: A total of 1, 860 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Based on vital signs, we extracted a total of 43 features for building machine learning model. Extreme Gradient Boosting (XGBoost) was used to develop a real-time prediction model. Three-fold cross validation determined the consistency of model accuracy. SHAP value was used to capture the overall and real-time interpretability of the model. Results: On the test set, CAPI predicted 95% of CA events, 80% of which were identified more than 25 min in advance, resulting in an area under the receiver operating characteristic curve (AUROC) of 0.94. The sensitivity, specificity, area under the precision-recall curve (AUPRC) and F1-score were 0.86, 0.85, 0.12 and 0.05, respectively. Conclusion: CAPI can help predict patients with CA in the vital signs monitoring at bedside. Compared with previous studies, CAPI can give more timely notifications to doctors for CA events. However, current performance was at the cost of alarm fatigue. Future research is still needed to achieve better clinical application.