Volume 14 Issue 5
Sep.  2023
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ZHANG Nan, LIN Qingting, ZHU Huadong. Prediction Model for In-hospital Death of Patients with Cardiac Arrest[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(5): 1023-1030. doi: 10.12290/xhyxzz.2023-0378
Citation: ZHANG Nan, LIN Qingting, ZHU Huadong. Prediction Model for In-hospital Death of Patients with Cardiac Arrest[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(5): 1023-1030. doi: 10.12290/xhyxzz.2023-0378

Prediction Model for In-hospital Death of Patients with Cardiac Arrest

doi: 10.12290/xhyxzz.2023-0378

National High Level Hospital Clinical Research Funding 2022-PUMCH-B-110

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  • Corresponding author: ZHU Huadong, E-mail: zhuhuadong1970@126.com
  • Received Date: 2023-08-15
  • Accepted Date: 2023-09-19
  • Available Online: 2023-09-26
  • Publish Date: 2023-09-30
  •   Objective  To build a prediction model of the in-hospital death of patients with cardiac arrest.  Methods  This study is a retrospective analysis based on the medical information mart for intensive care-Ⅳ (MIMIC-Ⅳ)2.0. We gathered the information of patients above 18 years old, with cardiac arrest and intensive care unit (ICU) experience. A stepwise multi-variate logistic regression analysis was performed to filter variables, variables with P values < 0.05 were kept and enter as predictors of in-hospital death of patients with cardiac arrest. The model was evaluated with receiver operating characteristic (ROC) curve for discriminative power and with calibration curve for consistency. Finally, an online dynamic nomogram calculator was built to calculate the risk of in-hospital death.  Results  This study included 1772 patients with cardiac arrest. The mean age of those patients was (64.93±16.52) years old, and 963 (54.3%) patients suffered in-hospital death. The factors of the prediction model for in-hospital death of cardiac arrest patients constructed based on multi-variate logistic regression included: potential cardiac disease diagnosis, age adjusted Chalson comorbidity index(CCI), body mass index (BMI), vital signs, lowest lactic acid and lowest Glasgow coma scale (GCS) during the first 24 hours after entering ICU, cardiac ultrasound examination, invasive mechanical ventilation and vasopressin utilization. The sensitivity and specificity of the prediction model were 73.1%(95% CI: 0.702-0.759) and 71.6%(95% CI: 0.683-0.745), respectively. Area under the ROC curve was 0.806(95% CI: 0.786-0.826).  Conclusions  The prediction model built in this study can properly predict the in-hospital death of patients with cardiac arrest.
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