PDF(Pubmed)
Abstract：
BACKGROUND: Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset.
RESULTS: We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/).
CONCLUSIONS: The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.
RESULTS: We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/).
CONCLUSIONS: The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.
摘要：
背景：很少有早期院外心脏骤停（OHCA）患者的预测模型经过外部验证。本研究旨在使用大型全国数据集从外部验证OHCA结果的更新预测模型。
结果：我们对JAAM-OHCA（院外心脏骤停生存的院内重症监护综合登记和日本急性医学协会院外心脏骤停登记）进行了二次分析。更新了先前开发的用于实现自发循环恢复的心脏骤停患者的预测模型。使用来自JAAM-OHCA注册中心的56个机构的数据进行外部验证。主要结果是90天脑功能分类评分。使用推导集更新了两个模型(n=3337)。模型1包括患者人口统计学，院前信息,和入院时的初始节律；模型2包括自发循环恢复后立即在医院获得的信息。在验证集（n=4250）中，模型1和2的C统计量为0.945（95％CI，0.935-0.955）和0.958（95％CI，0.951-0.960），分别。两个模型都很好地校准到观察到的结果。决策曲线分析表明，模型2在所有风险阈值下的净收益均高于模型1。开发了一个基于网络的计算器来估计不良结果的概率(https://pcas-prediction。shinyapps.io/90d_lasso/)。
结论：更新的模型为医学专业人员提供了有价值的信息，可以预测OHCA患者的长期神经系统预后。可能在临床决策过程中发挥重要作用。
结果：我们对JAAM-OHCA（院外心脏骤停生存的院内重症监护综合登记和日本急性医学协会院外心脏骤停登记）进行了二次分析。更新了先前开发的用于实现自发循环恢复的心脏骤停患者的预测模型。使用来自JAAM-OHCA注册中心的56个机构的数据进行外部验证。主要结果是90天脑功能分类评分。使用推导集更新了两个模型(n=3337)。模型1包括患者人口统计学，院前信息,和入院时的初始节律；模型2包括自发循环恢复后立即在医院获得的信息。在验证集（n=4250）中，模型1和2的C统计量为0.945（95％CI，0.935-0.955）和0.958（95％CI，0.951-0.960），分别。两个模型都很好地校准到观察到的结果。决策曲线分析表明，模型2在所有风险阈值下的净收益均高于模型1。开发了一个基于网络的计算器来估计不良结果的概率(https://pcas-prediction。shinyapps.io/90d_lasso/)。
结论：更新的模型为医学专业人员提供了有价值的信息，可以预测OHCA患者的长期神经系统预后。可能在临床决策过程中发挥重要作用。
