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Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework
Resuscitation ( IF 6.5 ) Pub Date : 2021-11-26 , DOI: 10.1016/j.resuscitation.2021.11.029
Xiang Yi Wong 1 , Yu Kai Ang 2 , Keqi Li 3 , Yip Han Chin 4 , Sean Shao Wei Lam 5 , Kenneth Boon Kiat Tan 6 , Matthew Chin Heng Chua 3 , Marcus Eng Hock Ong 7 , Nan Liu 8 , Ahmad Reza Pourghaderi 9 , Andrew Fu Wah Ho 10 ,
Affiliation  

Background

Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC.

Methods

We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010–2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses.

Results

5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84–0.90) within the testing cohort.

Conclusion

We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.



中文翻译:

使用可解释的机器学习框架开发和验证 SARICA 评分以预测院外心脏骤停恢复自发循环后的生存率

背景

对实现自主循环恢复 (ROSC) 的院外心脏骤停 (OHCA) 患者进行准确和及时的预测对于临床决策、资源分配和与近亲的沟通至关重要。我们的目标是开发心脏骤停 ROSC 后的生存率 (SARICA),这是一种实用的临床决策工具,用于预测达到 ROSC 的 OHCA 患者的生存率。

方法

我们利用了 2010 年至 2018 年间基于人口的泛亚复苏结果研究中的真实新加坡数据。我们排除了没有 ROSC 的患者。数据集分为训练 (60%)、验证 (20%) 和测试 (20%) 群组。主要终点是生存(至 30 天或出院)。AutoScore 是一种可解释的基于机器学习的临床评分生成算法,用于开发 SARICA。候选因素是根据入院时通常可用的客观人口统计学和临床​​因素选择的。SARICA 的性能基于接受者操作曲线 (ROC) 分析进行评估。

结果

纳入 5970 名患者,其中 855 名(14.3%)存活。三变量模型被确定为最简约。通过随机森林选择确定了院前 ROSC、年龄和初始心律以供纳入。最后,SARICA 由这 3 个变量组成,范围从 0 到 10 分,在测试队列中实现了 ROC 下面积 (AUC) 为 0.87(95% 置信区间:0.84-0.90)。

结论

我们开发并内部验证了 SARICA 评分,以准确预测 OHCA 患者入院时的 ROSC 生存率。SARICA 具有临床实用性,并使用可解释的机器学习框架开发。SARICA 具有未知的普遍性,等待外部验证研究。

更新日期:2021-12-04
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