Department of Emergency Medicine, College of Medicine National Taiwan University Taipei Taiwan.
Department of Emergency Medicine National Taiwan University Hospital Taipei Taiwan.
J Am Heart Assoc. 2024 Oct 15;13(20):e037088. doi: 10.1161/JAHA.124.037088. Epub 2024 Oct 11.
The aim of this study was to validate and compare the performance of statistical (Utstein-Based Return of Spontaneous Circulation and Shockable Rhythm-Witness-Age-pH) and machine learning-based (Prehospital Return of Spontaneous Circulation and Swedish Cardiac Arrest Risk Score) models in predicting the outcomes following out-of-hospital cardiac arrest and to assess the impact of the COVID-19 pandemic on the models' performance.
This retrospective analysis included adult patients with out-of-hospital cardiac arrest treated at 3 academic hospitals between 2015 and 2023. The primary outcome was neurological outcomes at hospital discharge. Patients were divided into pre- (2015-2019) and post-2020 (2020-2023) subgroups to examine the effect of the COVID-19 pandemic on out-of-hospital cardiac arrest outcome prediction. The models' performance was evaluated using the area under the receiver operating characteristic curve and compared by the DeLong test. The analysis included 2161 patients, 1241 (57.4%) of whom were resuscitated after 2020. The cohort had a median age of 69.2 years, and 1399 patients (64.7%) were men. Overall, 69 patients (3.2%) had neurologically intact survival. The area under the receiver operating characteristic curves for predicting neurological outcomes were 0.85 (95% CI, 0.83-0.87) for the Utstein-Based Return of Spontaneous Circulation score, 0.82 (95% CI, 0.81-0.84) for the Shockable Rhythm-Witness-Age-pH score, 0.79 (95% CI, 0.78-0.81) for the Prehospital Return of Spontaneous Circulation score, and 0.79 (95% CI, 0.77-0.81) for the Swedish Cardiac Arrest Risk Score model. The Utstein-Based Return of Spontaneous Circulation score significantly outperformed both the Prehospital Return of Spontaneous Circulation score (<0.001) and the Swedish Cardiac Arrest Risk Score model (=0.007). Subgroup analysis indicated no significant difference in predictive performance for patients resuscitated before versus after 2020.
In this external validation, both statistical and machine learning-based models demonstrated excellent and fair performance, respectively, in predicting neurological outcomes despite different model architectures. The predictive performance of all evaluated clinical scoring systems was not significantly influenced by the COVID-19 pandemic.
本研究旨在验证和比较统计（基于乌斯丁的自主循环恢复和可电击节律-目击者年龄-血 pH 值）和基于机器学习的（院前自主循环恢复和瑞典心脏骤停风险评分）模型在预测院外心脏骤停后结局方面的性能，并评估 COVID-19 大流行对模型性能的影响。
这是一项回顾性分析，纳入了在 2015 年至 2023 年期间在 3 所学术医院接受治疗的院外心脏骤停的成年患者。主要结局为出院时的神经结局。患者被分为前（2015-2019 年）和后 2020 年（2020-2023 年）亚组，以检查 COVID-19 大流行对院外心脏骤停结局预测的影响。使用接受者操作特征曲线下面积评估模型性能，并通过 DeLong 检验比较。该分析共纳入 2161 例患者，其中 1241 例（57.4%）在 2020 年后复苏。队列的中位年龄为 69.2 岁，1399 例（64.7%）为男性。总体而言，有 69 例（3.2%）患者存活且神经功能完整。预测神经结局的接受者操作特征曲线下面积分别为乌斯丁自主循环恢复评分 0.85（95%CI，0.83-0.87）、可电击节律-目击者年龄-pH 值评分 0.82（95%CI，0.81-0.84）、院前自主循环恢复评分 0.79（95%CI，0.78-0.81）和瑞典心脏骤停风险评分模型 0.79（95%CI，0.77-0.81）。乌斯丁自主循环恢复评分显著优于院前自主循环恢复评分（<0.001）和瑞典心脏骤停风险评分模型（=0.007）。亚组分析表明，2020 年前和 2020 年后复苏的患者在预测性能方面无显著差异。
在这项外部验证中，尽管模型结构不同，但统计和基于机器学习的模型在预测神经结局方面均表现出优异和良好的性能。所有评估的临床评分系统的预测性能均未受到 COVID-19 大流行的显著影响。
