摘要目的:探讨院内心脏骤停（in-hospital cardiac arrest, IHCA）的影响因素，建立列线图预测模型。方法:回顾性收集2008—2019年美国重症监护医学信息数据库-Ⅳ中进入重症监护室（intensive care unit, ICU）治疗的患者作为研究对象，排除未成年、重复入院及ICU停留小于24 h的患者，随机将人群分为训练集和内部验证集（7∶3）。采用单因素和多因素Logistic回归分析IHCA的影响因素并建立列线图模型，通过校正曲线、受试者工作曲线（receiver operating characteristic curve, ROC）以及决策曲线分析（decision curve analysis, DCA）对模型进行评价。选择急诊重症监护室合作研究数据库的重症人群数据对模型进行外部验证。结果:本研究共纳入41 951例重症患者，随机分为训练集（ n=29 366）和内部验证集（ n=12 585）。多因素分析发现心肌梗死、肺源性心脏病、心源性休克、呼吸衰竭、急性肾损伤、呼吸频率、葡萄糖、红细胞压积、钠、阴离子间隙、血管活性药物和有创机械通气是IHCA的独立影响因素，结合以上变量构建列线图。在训练集中，列线图ROC的曲线下面积（area under the curve, AUC）为0.817（95% CI：0.785~0.847），校正曲线显示其预测概率和实际概率具有一致性，DCA显示其具有良好的临床净获益。在内部验证集中，列线图对IHCA具有相似的预测价值（AUC=0.807，95% CI: 0.760~0.862）。在外部验证集（ n=87 626）中，列线图预测IHCA的能力同样稳健（AUC=0.804，95% CI: 0.786~0.822）。此外，列线图对院内死亡同样具有预测价值（AUC=0.818，95% CI: 0.802~0.834）。 结论:本研究基于IHCA的危险因素建立的列线图模型具有良好的预测能力，并且在外部验证中预测效能稳健，有助于临床医师评估院内危重症患者发生心脏骤停的风险。

abstractsObjective:To explore the independent risk factors of in-hospital cardiac arrest (IHCA) in critically ill patients and construct a nomogram model to predict the risk of IHCA based on the identified risk factors.Methods:Patients who were admitted to the intensive care units (ICUs) from 2008 to 2019 were retrospectively enrolled from the Medical Information Mart for Intensive Care -Ⅳ database. The patients were excluded if they (1) were younger than 18 years old, (2) had repeated ICU admission records, or (3) had an ICU stay shorter than 24 h. The patients were randomly divided into the training and internal validation cohorts (7 : 3). Univariate and multivariate logistic regression models were used to identify independent risk factors of IHCA, and a nomogram was constructed based on these independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model. Finally, the nomogram was externally validated using the emergency ICU collaborative research database.Results:A total of 41,951 critically ill patients were enrolled (training cohort, n=29 366; internal validation cohort, n=12 585). Multivariate analysis showed that myocardial infarction, pulmonary heart disease, cardiogenic shock, respiratory failure, acute kidney injury, respiratory rate, glucose, hematocrit, sodium, anion gap, vasoactive drug use, and invasive mechanical ventilation were independent risk factors of IHCA. Based on the above risk factors, a nomogram for predicting IHCA was constructed. The area under the ROC curve (AUC) of the nomogram was 0.817 (95% CI: 0.785–0.847). The calibration curve showed that the predicted and actual probabilities of the nomogram were consistent. Moreover, DCA showed that the nomogram had clinical benefits for predicting IHCA. In the internal validation cohort, the nomogram had a similar predictive value of IHCA (AUC=0.807, 95% CI: 0.760–0.862). In an external validation cohort of 87,626 critically ill patients, the nomogram had stable ability for predicting IHCA (AUC=0.804, 95% CI: 0.786–0.822). In addition, the nomogram also had predictive value for in-hospital mortality (AUC=0.818, 95% CI: 0.802-0.834). Conclusions:The nomogram is constructed based on identified independent risk factors, which has good predictive value for IHCA. Moreover, the performance of the nomogram in the external validation cohort is robust. The study findings may help clinicians to assess the risk of IHCA in critically ill patients.