Department of Emergency Medicine, College of Medicine, Soonchunhyang University, Republic of Korea.
ioCrops, Inc., Seoul, Republic of Korea.
Am J Emerg Med. 2022 Dec;62:41-48. doi: 10.1016/j.ajem.2022.09.035. Epub 2022 Sep 30.
Out-of-hospital cardiac arrest (OHCA) is a leading cause of death, and research has identified limitations in analyzing the factors related to the incidence of cardiac arrest and the frequency of bystander cardiopulmonary resuscitation. This study conducts a cluster analysis of the correlation between location-related factors and the outcome of patients with OHCA using two machine learning methods: variational autoencoder (VAE) and the Dirichlet process mixture model (DPMM).
Using the prospectively collected Smart Advanced Life Support registry in South Korea between August 2015 and December 2018, a secondary retrospective data analysis was performed on patients with OHCA with a presumed cause of cardiac arrest in adults of 18 years or older. VAE and DPMM were used to create clusters to determine groups with a common nature among those with OHCA.
Among 5876 OHCA cases, 1510 patients were enrolled in the final analysis. Decision tree-based models, which have an accuracy of 95.36%, were also used to interpret the characteristics of clusters. A total of 8 clusters that had similar spatial characteristics were identified using DPMM and VAE. Among the generated clusters, the averages of the four clusters that exhibited a high survival to discharge rate and a favorable neurological outcome were 9.6% and 6.1%, and the averages of the four clusters that exhibited a low outcome were 5.1% and 3.5% respectively. In the decision tree-based models, the most important feature that could affect the prognosis of an OHCA patient was being transferred to a higher-level emergency center.
This methodology can facilitate the development of a regionalization strategy that can improve the survival rate of cardiac arrest patients in different regions.
院外心脏骤停（OHCA）是导致死亡的主要原因之一，研究已经确定了分析与心脏骤停发生率和旁观者心肺复苏频率相关因素的局限性。本研究使用两种机器学习方法：变分自编码器（VAE）和狄利克雷过程混合模型（DPMM），对与位置相关的因素与 OHCA 患者结局之间的相关性进行聚类分析。
使用韩国 2015 年 8 月至 2018 年 12 月期间前瞻性收集的智能高级生命支持登记处，对 18 岁及以上成人心脏骤停假定病因的 OHCA 患者进行二次回顾性数据分析。使用 VAE 和 DPMM 来创建聚类，以确定 OHCA 患者中具有共同性质的组。
在 5876 例 OHCA 病例中，有 1510 例患者纳入最终分析。基于决策树的模型，其准确率为 95.36%，也用于解释聚类的特征。使用 DPMM 和 VAE 共确定了 8 个具有相似空间特征的聚类。在生成的聚类中，表现出高出院存活率和良好神经结局的四个聚类的平均值分别为 9.6%和 6.1%，而表现出低结局的四个聚类的平均值分别为 5.1%和 3.5%。在基于决策树的模型中，最能影响 OHCA 患者预后的重要特征是被转往更高级别急救中心。
该方法学可以促进制定区域化战略，从而提高不同地区心脏骤停患者的存活率。
