• Hong Li, Ting Ting Wu, Dong Liang Yang, Yang Song Guo, Pei Chang Liu, Yuan Chen, and Li Ping Xiao.
• Department of Nursing, Fujian Provincial Hospital, Fujian, China.
• Clin Cardiol. 2019 Nov 1; 42 (11): 1087-1093.

BackgroundIn-hospital cardiac arrest (IHCA) may be preventable, with patients often showing signs of physiological deterioration before an event. Our objective was to develop and validate a simple clinical prediction model to identify the IHCA risk among cardiac arrest (CA) patients hospitalized with acute coronary syndrome (ACS).HypothesisA predicting model could help to identify the risk of IHCA among patients admitted with ACS.MethodsWe conducted a case-control study and analyzed 21 337 adult ACS patients, of whom 164 had experienced CA. Vital signs, demographic, and laboratory data were extracted from the electronic health record. Decision tree analysis was applied with 10-fold cross-validation to predict the risk of IHCA.ResultsThe decision tree analysis detected seven explanatory variables, and the variables' importance is as follows: VitalPAC Early Warning Score (ViEWS), fatal arrhythmia, Killip class, cardiac troponin I, blood urea nitrogen, age, and diabetes. The development decision tree model demonstrated a sensitivity of 0.762, a specificity of 0.882, and an area under the receiver operating characteristic curve (AUC) of 0.844 (95% CI, 0.805 to 0.849). A 10-fold cross-validated risk estimate was 0.198, while the optimism-corrected AUC was 0.823 (95% CI, 0.786 to 0.860).ConclusionsWe have developed and internally validated a good discrimination decision tree model to predict the risk of IHCA. This simple prediction model may provide healthcare workers with a practical bedside tool and could positively impact decision-making with regard to deteriorating patients with ACS.© 2019 The Authors. Clinical Cardiology published by Wiley Periodicals, Inc.

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