Kim Tae Jung, Suh Jungyo, Park Soo-Hyun, Kim Youngjoon, Ko Sang-Bae
Department of Neurology, Seoul National University College of Medicine, Seoul, Korea.
Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea.
Neurocrit Care. 2025 Feb 20. doi: 10.1007/s12028-025-02222-3.
A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning methods.
The neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC).
Of the 390 total patients (mean age 64.2 years; 71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method was 0.965 (95% confidence interval 0.941-0.976).
The NOCA score represents a simple method for predicting neurological outcomes, with good performance in patients with CA, using a machine learning analysis that incorporates widely available variables.
多模式方法可能被证明对预测心脏骤停（CA）后的临床结果有效。我们旨在开发一种实用的预测模型，该模型使用机器学习方法纳入与CA相关的临床因素和多种预后测试。
使用2018年5月至2023年6月期间390例CA患者的数据，开发了用于预测不良结果的心脏骤停后神经学结果（NOCA）方法。结果为不良神经学结果，定义为出院时脑功能分类评分为3 - 5分。我们分析了31个描述CA时情况、人口统计学、合并症和预后研究的变量。基于极端梯度提升算法开发预后方法，并进行三重交叉验证和超参数优化。使用受试者工作特征曲线分析并计算曲线下面积（AUC）来评估预测模型的性能。
在390例患者（平均年龄64.2岁；71.3%为男性）中，235例（60.3%）出院时出现不良结果。我们使用最小绝对收缩和选择算子回归选择预测不良神经学结果的变量。发现格拉斯哥昏迷量表 - M（最佳运动反应）、脑电图特征、神经瞳孔指数、从CA到自主循环恢复的时间以及脑成像在NOCA评分中是重要的关键参数。NOCA方法的AUC为0.965（95%置信区间0.941 - 0.976）。
NOCA评分代表了一种预测神经学结果的简单方法，在CA患者中表现良好，使用了纳入广泛可用变量的机器学习分析。
