Abstract：
Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission.
METHODS: The study population were 55 615 OHCA cases registered in the Swedish Cardiopulmonary Resuscitation Registry between 2010 and 2020. The dataset was split to training and validation sets (for model development) and test set (for evaluation of the final model). We used an XGBoost algorithm with stratified, repeated 10-fold cross-validation along with Optuna framework for hyperparameters tuning. The final model was trained on 10 features selected based on the importance scores and evaluated on the test set in terms of discrimination, calibration and bias-variance tradeoff. We used SHapley Additive exPlanations to address the \'black-box\' model and align with eXplainable artificial intelligence.
RESULTS: The final model achieved: area under the receiver operating characteristic value 0.964 (95% confidence interval (CI) [0.960-0.968]), sensitivity 0.606 (95% CI [0.573-0.634]), specificity 0.975 (95% CI [0.972-0.978]), positive predictive value (PPV) 0.664 (95% CI [0.625-0.696]), negative predictive value (NPV) 0.969 (95% CI [0.966-0.972]), macro F1 0.803 (95% CI [0.788-0.816]), and showed a very good calibration. SHAP features with the highest impact on the model\'s output were:\'ROSC on arrival to hospital\', \'Initial rhythm asystole\' and \'Conscious on arrival to hospital\'.
CONCLUSIONS: The XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.
METHODS: The study population were 55 615 OHCA cases registered in the Swedish Cardiopulmonary Resuscitation Registry between 2010 and 2020. The dataset was split to training and validation sets (for model development) and test set (for evaluation of the final model). We used an XGBoost algorithm with stratified, repeated 10-fold cross-validation along with Optuna framework for hyperparameters tuning. The final model was trained on 10 features selected based on the importance scores and evaluated on the test set in terms of discrimination, calibration and bias-variance tradeoff. We used SHapley Additive exPlanations to address the \'black-box\' model and align with eXplainable artificial intelligence.
RESULTS: The final model achieved: area under the receiver operating characteristic value 0.964 (95% confidence interval (CI) [0.960-0.968]), sensitivity 0.606 (95% CI [0.573-0.634]), specificity 0.975 (95% CI [0.972-0.978]), positive predictive value (PPV) 0.664 (95% CI [0.625-0.696]), negative predictive value (NPV) 0.969 (95% CI [0.966-0.972]), macro F1 0.803 (95% CI [0.788-0.816]), and showed a very good calibration. SHAP features with the highest impact on the model\'s output were:\'ROSC on arrival to hospital\', \'Initial rhythm asystole\' and \'Conscious on arrival to hospital\'.
CONCLUSIONS: The XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.
摘要：
院外心脏骤停（OHCA）是一种生存率低的危重症。在自发循环恢复的患者中，脑损伤是导致死亡的主要原因。在这项研究中,我们提出了一种可解释的机器学习方法来预测OHCA后的神经系统结果，使用入院时可用的信息。
方法：研究人群为2010年至2020年在瑞典心肺复苏登记处登记的55615例OHCA病例。将数据集分成训练和验证集(用于模型开发)和测试集(用于最终模型的评估)。我们使用了XGBoost算法，重复10倍交叉验证以及Optuna框架进行超参数调整。最终的模型是在根据重要性得分选择的10个特征上进行训练，并在判别方面对测试集进行评估，校准和偏差-方差权衡。我们使用SHapley加法扩张来解决“黑匣子”模型，并与可解释的人工智能保持一致。
结果：实现的最终模型：接收器工作特征值下的面积0.964（95％置信区间（CI）[0.960-0.968]），灵敏度0.606（95%CI[0.573-0.634]），特异性0.975（95%CI[0.972-0.978]），阳性预测值（PPV）0.664（95%CI[0.625-0.696]），阴性预测值（NPV）0.969（95%CI[0.966-0.972]），宏F10.803（95%CI[0.788-0.816]），并显示出非常好的校准。对模型输出影响最大的SHAP特征是：\'到达医院时的ROSC\'，\'初始节律性心搏停止\'和\'到达医院后意识到\'。
结论：在入院时具有10个可用特征的XGBoost机器学习模型在预测OHCA后的神经系统结局方面表现良好，没有明显的过度拟合迹象。
方法：研究人群为2010年至2020年在瑞典心肺复苏登记处登记的55615例OHCA病例。将数据集分成训练和验证集(用于模型开发)和测试集(用于最终模型的评估)。我们使用了XGBoost算法，重复10倍交叉验证以及Optuna框架进行超参数调整。最终的模型是在根据重要性得分选择的10个特征上进行训练，并在判别方面对测试集进行评估，校准和偏差-方差权衡。我们使用SHapley加法扩张来解决“黑匣子”模型，并与可解释的人工智能保持一致。
结果：实现的最终模型：接收器工作特征值下的面积0.964（95％置信区间（CI）[0.960-0.968]），灵敏度0.606（95%CI[0.573-0.634]），特异性0.975（95%CI[0.972-0.978]），阳性预测值（PPV）0.664（95%CI[0.625-0.696]），阴性预测值（NPV）0.969（95%CI[0.966-0.972]），宏F10.803（95%CI[0.788-0.816]），并显示出非常好的校准。对模型输出影响最大的SHAP特征是：\'到达医院时的ROSC\'，\'初始节律性心搏停止\'和\'到达医院后意识到\'。
结论：在入院时具有10个可用特征的XGBoost机器学习模型在预测OHCA后的神经系统结局方面表现良好，没有明显的过度拟合迹象。
