Department of Nursing, Fujian Provincial Hospital, Fujian, China.
Department of Nursing, Fujian Health College, Fujian, China.
Clin Cardiol. 2019 Nov;42(11):1087-1093. doi: 10.1002/clc.23255. Epub 2019 Sep 11.
In-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).
A predicting model could help to identify the risk of IHCA among patients admitted with ACS.
We 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.
The 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).
We 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.
院内心搏骤停（IHCA）可能是可以预防的，患者在事件发生前通常会出现生理恶化的迹象。我们的目的是开发和验证一种简单的临床预测模型，以识别因急性冠状动脉综合征（ACS）住院的心脏骤停（CA）患者的 IHCA 风险。
预测模型可以帮助识别因 ACS 住院的患者发生 IHCA 的风险。
我们进行了一项病例对照研究，分析了 21337 例成年 ACS 患者，其中 164 例发生了 CA。从电子健康记录中提取生命体征、人口统计学和实验室数据。采用 10 折交叉验证进行决策树分析，以预测 IHCA 的风险。
决策树分析检测到七个解释变量，变量的重要性如下：VitalPAC 早期预警评分（ViEWS）、致命性心律失常、Killip 分级、肌钙蛋白 I、血尿素氮、年龄和糖尿病。开发的决策树模型的敏感性为 0.762，特异性为 0.882，接受者操作特征曲线（ROC）下面积（AUC）为 0.844（95%CI，0.805 至 0.849）。10 折交叉验证风险估计值为 0.198，而乐观校正后的 AUC 为 0.823（95%CI，0.786 至 0.860）。
我们已经开发并内部验证了一种具有良好区分能力的决策树模型来预测 IHCA 的风险。这种简单的预测模型可为医疗保健工作者提供一种实用的床边工具，并可能对 ACS 恶化患者的决策产生积极影响。
