实用休克杂志（中英文）2025，Vol.9Issue(1)：11-19,9.
重症监护病房内心脏骤停患者的预后因素分析及预测模型建立
Analysis of prognostic factors and development of a predictive model for cardiac arrest in the intensive care unit
摘要
Abstract
Objective Patients with cardiac arrest in the intensive care unit(ICU)face poorer prognoses.This study aimed to identify independent risk factors influencing the return of spontaneous circulation(ROSC)in ICU patients with cardiac arrest and construct a predictive model that could fa-cilitate early intervention.Methods A single-center retrospective cohort study was conducted.Pa-tients who experienced cardiac arrest in the ICU of Hospital,between January 2017 and December 2019 were included.Clinical data,the worst vital signs,and arterial blood gas parameters within 8～24 hours before cardiac arrest were collected and categorized.Variables were screened using logistic regression and LASSO regression to build a nomogram model.The model's discrimination,calibra-tion,and clinical utility were evaluated using the receiver operating characteristic(ROC)curve,cali-bration curve,and decision curve analysis(DCA).Results Among 427 cardiac arrest patients,121(28.3％)achieved ROSC.Multivariate logistic regression identified four independent risk factors for ROSC,shockable rhythm(OR=3.10,95％CI:1.67～5.77,P＜0.001),cardiac etiology(OR=1.98,95％CI:1.19～3.27,P=0.008),arterial oxygen saturation(OR=0.36,95％CI:0.19～0.68,P=0.001),and systolic blood pressure(OR=0.26,95％CI:0.09～0.77,P=0.015).A nomogram incorporating these factors demonstrated moderate predictive accuracy,with an area under the ROC curve of 0.694(95％CI:0.636～0.751).The Hosmer-Lemeshow test indicated good calibration(P=0.796),and DCA confirmed its clinical utility.Conclusions Shock-able rhythm,cardiac etiology,arterial oxygen saturation,and systolic blood pressure are independent predictors of ROSC in ICU patients with cardiac arrest.The nomogram model based on these factors holds clinical value for early risk stratification and intervention,potentially improving outcomes in this high-risk population.关键词
心脏骤停/重症监护病房/自主循环恢复/预测模型Key words
Cardiac arrest/Intensive care unit/Return of spontaneous circulation/Prediction model引用本文复制引用
高璐瑶,桑文涛,吴硕,边圆,徐峰,陈玉国..重症监护病房内心脏骤停患者的预后因素分析及预测模型建立[J].实用休克杂志（中英文）,2025,9(1):11-19,9.基金项目
山东省重点研发计划(项目编号:2022CXGC010504、2024CXPT089) （项目编号:2022CXGC010504、2024CXPT089）
