Study objective

We aimed to train and validate the time to on-scene return of spontaneous circulation prediction models using time-to-event analysis among out-of-hospital cardiac arrest patients.

Methods

Using a Korean population-based out-of-hospital cardiac arrest registry, we selected a total of 105,215 adults with presumed cardiac etiologies between 2013 and 2018. Patients from 2013 to 2017 and from 2018 were analyzed for training and test, respectively. We developed 4 time-to-event analyzing models (Cox proportional hazard [Cox], random survival forest, extreme gradient boosting survival, and DeepHit) and 4 classification models (logistic regression, random forest, extreme gradient boosting, and feedforward neural network). Patient characteristics and Utstein elements collected at the scene were used as predictors. Discrimination and calibration were evaluated by Harrell’s C-index and integrated Brier score.

Results

Among the 105,215 patients (mean age 70 years and 64% men), 86,314 and 18,901 patients belonged to the training and test sets, respectively. On-scene return of spontaneous circulation was achieved in 5,240 (6.1%) patients in the former set and 1,709 (9.0%) patients in the latter. The proportion of emergency medical services (EMS) management was higher and scene time interval longer in the latter. Median time from EMS scene arrival to on-scene return of spontaneous circulation was 8 minutes for both datasets. Classification models showed similar discrimination and poor calibration power compared to survival models; Cox showed high discrimination with the best calibration (C-index [95% confidence interval]: 0.873 [0.865 to 0.882]; integrated Brier score at 30 minutes: 0.060).

Conclusion

Incorporating time-to-event analysis could lead to improved performance in prediction models and contribute to personalized field EMS resuscitation decisions.

中文翻译：

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使用事件发生时间分析为院外心脏骤停患者开发自发循环预测的现场返回

学习目标

我们旨在使用院外心脏骤停患者的事件发生时间分析来训练和验证自发循环预测模型的现场恢复时间。

方法

使用基于韩国人群的院外心脏骤停登记处，我们选择了 2013 年至 2018 年期间共有 105,215 名推测为心脏病因的成年人。分别对 2013 年至 2017 年和 2018 年的患者进行了培训和测试分析。我们开发了 4 个时间事件分析模型（Cox 比例风险 [Cox]、随机生存森林、极端梯度提升生存和 DeepHit）和 4 个分类模型（逻辑回归、随机森林、极端梯度提升和前馈神经网络） . 在现场收集的患者特征和 Utstein 元素被用作预测因子。通过 Harrell 的 C 指数和综合 Brier 评分评估辨别和校准。

结果

在 105,215 名患者（平均年龄 70 岁，男性占 64%）中，分别有 86,314 名和 18,901 名患者属于训练集和测试集。前者有 5,240 名（6.1%）患者，后者有 1,709 名（9.0%）患者实现了现场自主循环恢复。后者的紧急医疗服务（EMS）管理比例更高，现场时间间隔更长。两个数据集从 EMS 现场到达到现场自主循环恢复的中位时间为 8 分钟。与生存模型相比，分类模型显示出相似的辨别力和较差的校准能力；Cox 显示出最佳校准的高辨别力（C 指数 [95% 置信区间]：0.873 [0.865 至 0.882]；30 分钟时的综合 Brier 评分：0.060）。

结论

结合事件发生时间分析可以提高预测模型的性能，并有助于个性化的现场 EMS 复苏决策。