Department of Biomedical Engineering, Seoul National University College of Medicine; Department of Emergency Medicine, Seoul National University College of Medicine.
Department of Emergency Medicine, Seoul National University College of Medicine.
Resuscitation. 2019 Sep;142:127-135. doi: 10.1016/j.resuscitation.2019.07.020. Epub 2019 Jul 27.
This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients.
Adult OHCA patients who had a presumed cardiac etiology and a sustained return of spontaneous circulation between 2013 and 2016 were analyzed; 80% of the individuals were analyzed for training and 20% were analyzed for validation. We developed using six machine learning algorithms: logistic regression (LR), extreme gradient boosting (XGB), support vector machine, random forest, elastic net (EN), and neural network. Variables that could be obtained within 24 hours of the emergency department visit were used. The area under the receiver operation curve (AUROC) was calculated to assess the discrimination. Calibration was assessed by the Hosmer-Lemeshow test. Reclassification was assessed by using the continuous net reclassification index (NRI).
A total of 19,860 OHCA patients were included in the analysis. Of the 15,888 patients in the training group, 2228 (14.0%) had a good neurological recovery; of the 3972 patients in the validation group, 577 (14.5%) had a good neurological recovery. The LR, XGB, and EN models showed the highest discrimination powers (AUROC (95% CI)) of 0.949 (0.941-0.957) for all), and all three models were well calibrated (Hosmer-Lemeshow test: p >0.05). The XGB model reclassified patients according to their true risk better than the LR model (NRI: 0.110), but the EN model reclassified patients worse than the LR model (NRI: -1.239).
The best performing machine learning algorithm was the XGB and LR algorithm.
本研究旨在训练、验证和比较使用机器学习分析预测 OHCA 患者良好神经恢复的预测模型。
分析了 2013 年至 2016 年间患有推定心脏病因和持续自主循环恢复的成年 OHCA 患者；80%的个体用于训练分析，20%的个体用于验证分析。我们使用了六种机器学习算法：逻辑回归（LR）、极端梯度提升（XGB）、支持向量机、随机森林、弹性网络（EN）和神经网络。使用在急诊科就诊后 24 小时内可以获得的变量。计算受试者工作特征曲线下的面积（AUROC）以评估区分度。通过 Hosmer-Lemeshow 检验评估校准。通过连续净重新分类指数（NRI）评估重新分类。
共纳入 19860 例 OHCA 患者进行分析。在训练组的 15888 例患者中，2228 例（14.0%）有良好的神经恢复；在验证组的 3972 例患者中，577 例（14.5%）有良好的神经恢复。LR、XGB 和 EN 模型显示出最高的区分能力（AUROC（95%CI））为 0.949（0.941-0.957）（所有模型），所有三个模型都具有良好的校准能力（Hosmer-Lemeshow 检验：p>0.05）。XGB 模型根据患者的真实风险更好地重新分类患者（NRI：0.110），但 EN 模型比 LR 模型更差地重新分类患者（NRI：-1.239）。
表现最好的机器学习算法是 XGB 和 LR 算法。
