Objectives

To evaluate the impact of community level information on the predictability of out-of-hospital cardiac arrest (OHCA) survival.

Methods

We used the Cardiac Arrest Registry to Enhance Survival (CARES) to geocode 9,595 Chicago incidents from 2014 to 2019 into community areas. Community variables including crime, healthcare, and economic factors from public data were merged with CARES. The merged data were used to develop ML models for OHCA survival. Models were evaluated using Area Under the Receiver Operating Characteristic curve (AUROC) and features were analyzed using SHapley Additive exPansion (SHAP) values.

Results

Baseline results using CARES data achieved an AUROC of 84%. The final model utilizing community variables increased the AUROC to 88%. A SHAP analysis between high and low performing community area clusters showed the high performing cluster is positively impacted by good health related features and good community safety features positively impact the low performing cluster.

Conclusion

Utilizing community variables helps predict neurologic outcomes with better performance than only CARES data. Future studies will use this model to perform simulations to identify interventions to improve OHCA survival.

中文翻译：

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利用社区层面的因素通过机器学习改进对院外心脏骤停结果的预测

 目标

评估社区层面信息对院外心脏骤停 (OHCA) 生存可预测性的影响。

 方法

我们使用心脏骤停登记处以提高生存率 (CARES) 将 2014 年至 2019 年的 9,595 起芝加哥事件地理编码到社区区域。来自公共数据的犯罪、医疗保健和经济因素等社区变量已与 CARES 合并。合并的数据用于开发 OHCA 生存的 ML 模型。使用接受者操作特征曲线下面积 (AUROC) 评估模型，并使用 SHapley Additive exPansion (SHAP) 值分析特征。

 结果

使用 CARES 数据的基线结果达到了 84% 的 AUROC。利用社区变量的最终模型将 AUROC 提高到 88%。高绩效社区区域集群和低绩效社区区域集群之间的 SHAP 分析表明，高绩效集群受到良好健康相关特征的积极影响，良好的社区安全特征对低绩效集群产生积极影响。

 结论

利用社区变量有助于预测神经系统结果，其性能比仅使用 CARES 数据更好。未来的研究将使用该模型进行模拟，以确定改善 OHCA 生存率的干预措施。