Karatsu Shinsuke, Hirano Yohei, Kondo Yutaka, Okamoto Ken, Tanaka Hiroshi
Juntendo Iji Zasshi. 2023 May 20;69(3):222-230. doi: 10.14789/jmj.JMJ22-0035-OA. eCollection 2023.
The purpose of this study was to develop and validate a machine learning prediction model for the prognosis of non-cardiogenic out-of-hospital cardiac arrest (OHCA) with an initial non-shockable rhythm.
Data were obtained from a nationwide OHCA registry in Japan. Overall, 222,056 patients with OHCA and an initial non-shockable rhythm were identified from the registry in 2016 and 2017. Patients aged <18 years and OHCA caused by cardiogenic origin, cancer, and external factors were excluded. Finally, 58,854 participants were included.
Patients were classified into the training dataset (n=29,304, data from 2016) and the test dataset (n=29,550, data from 2017). The training dataset was used to train and develop the machine learning model, and the test dataset was used for internal validation. We selected XGBoost as the machine learning classifier. The primary outcome was the poor prognosis defined as cerebral performance category of 3-5 at 1 month. Eleven prehospital variables were selected as outcome predictors.
In validation, the machine learning model predicted the primary outcome with an accuracy of 90.8% [95% confidence interval (CI): 90.5-91.2], a sensitivity of 91.4% [CI: 90.7-91.4], a specificity of 74.1% [CI: 69.2-78.6], and an area under the receiver operating characteristic value of 0.89 [0.87-0.92]. The important features for model development were the prehospital return of spontaneous circulation, prehospital adrenaline administration, and initial electrical rhythm.
We developed a favorable machine learning model to predict the prognosis of non-cardiogenic OHCA with an initial non-shockable rhythm in the early stage of resuscitation.
本研究旨在开发并验证一种机器学习预测模型，用于预测初始心律不可电击的非心源性院外心脏骤停（OHCA）的预后情况。
数据取自日本全国范围的OHCA登记处。总体而言，2016年和2017年从登记处识别出222,056例初始心律不可电击的OHCA患者。排除年龄<18岁以及由心源性、癌症和外部因素导致的OHCA患者。最终纳入58,854名参与者。
将患者分为训练数据集（n = 29,304，2016年的数据）和测试数据集（n = 29,550，2017年的数据）。训练数据集用于训练和开发机器学习模型，测试数据集用于内部验证。我们选择XGBoost作为机器学习分类器。主要结局为预后不良，定义为复苏1个月时脑功能分级为3 - 5级。选择11个院前变量作为结局预测指标。
在验证中，机器学习模型预测主要结局的准确率为90.8% [95%置信区间（CI）：90.5 - 91.2]，灵敏度为91.4% [CI：90.7 - 91.4]，特异度为74.1% [CI：69.2 - 78.6]，受试者工作特征曲线下面积为0.89 [0.87 - 0.92]。模型开发的重要特征为院前自主循环恢复、院前肾上腺素使用及初始电节律。
我们开发了一种良好的机器学习模型，用于在复苏早期预测初始心律不可电击的非心源性OHCA的预后。
