Background Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles. We aimed to improve IHCA predictions by combining vital sign indicators with laboratory test results and, optionally, International Classification of Disease-10 block for diagnosis (ICD10BD). Methods We conducted a retrospective cohort study in the general ward (GW) and intensive care unit (ICU) of a 680-bed secondary healthcare institution. We included 62,061 adults admitted to the Department of Internal Medicine from January 2010 to August 2022. IHCAs were identified based on cardiopulmonary resuscitation prescriptions. Patient- days within three days preceding IHCAs were labeled as case days; all others were control days. The eXtreme Gradient Boosting (XGBoost) model was trained using daily vital signs, 14 laboratory test results, and ICD10BD. Results In the GW, among 1,299,448 patient-days from 62,038 patients, 1,367 days linked to 713 patients were cases. In the ICU, among 117,190 patient-days from 16,881 patients, 1,119 days from 444 patients were cases. The area under the ROC curve for IHCA prediction model was 0.934 and 0.896 in the GW and ICU, respectively, using the combination of vital signs, laboratory test results, and ICD10BD; 0.925 and 0.878, respectively, with vital signs and laboratory test results; and 0.839 and 0.828, respectively, with only vital signs. Conclusions Incorporating laboratory test results or combining laboratory test results and ICD10BD with vital signs as predictor variables in the XGBoost model potentially enhances clinical decision-making and improves patient outcomes in hospital settings.

中文翻译：

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一种机器学习方法，用于使用单日生命体征、实验室测试结果和疾病 10 块的国际分类进行诊断来预测院内心脏骤停。


背景 预测院内心脏骤停 （IHCA） 对于潜在降低死亡率和改善患者预后至关重要。然而，大多数仅依赖生命体征的模型可能无法全面捕捉患者的风险概况。我们旨在通过将生命体征指标与实验室测试结果相结合，以及可选的国际疾病分类 10 区诊断 （International Classification of Disease-10 block for diagnose， ICD10BD） 来改进 IHCA 预测。方法 我们在一家拥有 680 个床位的二级医疗机构的普通病房 （GW） 和重症监护病房 （ICU） 进行了一项回顾性队列研究。我们纳入了 2010 年 1 月至 2022 年 8 月期间内科收治的 62,061 名成年人。IHCA 是根据心肺复苏处方确定的。IHCA 前 3 天内的患者天数被标记为病例天数;所有其他都是控制日。eXtreme Gradient Boosting （XGBoost） 模型使用每日生命体征、 14 个实验室测试结果和 ICD10BD 进行训练。结果 在 GW 中，在 62,038 名患者的 1,299,448 个患者日中，与 713 名患者相关的 1,367 天是病例。在 ICU 中，在 16,881 名患者的 117,190 名患者日中，有 444 名患者的 1,119 天是病例。结合生命体征、实验室检查结果和 ICD10BD，IHCA 预测模型的 GW 和 ICU 的 ROC 曲线下面积分别为 0.934 和 0.896;生命体征和实验室检查结果分别为 0.925 和 0.878;和 0.839 和 0.828，只有生命体征。 结论 在 XGBoost 模型中纳入实验室测试结果或将实验室测试结果和ICD10BD作为预测变量作为预测变量可能会增强临床决策并改善医院环境中的患者预后。