Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysis

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引用次数: 0

Abstract

Objective

We aimed to determine if machine learning can predict acute brain injury and to identify modifiable risk factors for acute brain injury in patients receiving venoarterial extracorporeal membrane oxygenation.

Methods

We included adults (age ≥18 years) receiving venoarterial extracorporeal membrane oxygenation or extracorporeal cardiopulmonary resuscitation in the Extracorporeal Life Support Organization Registry (2009-2021). Our primary outcome was acute brain injury: central nervous system ischemia, intracranial hemorrhage, brain death, and seizures. We used Random Forest, CatBoost, LightGBM, and XGBoost machine learning algorithms (10-fold leave-1-out cross-validation) to predict and identify features most important for acute brain injury. We extracted 65 total features: demographics, pre-extracorporeal membrane oxygenation/on-extracorporeal membrane oxygenation laboratory values, and pre-extracorporeal membrane oxygenation/on-extracorporeal membrane oxygenation settings.

Results

Of 35,855 patients receiving venoarterial extracorporeal membrane oxygenation (nonextracorporeal cardiopulmonary resuscitation) (median age of 57.8 years, 66% were male), 7.7% (n = 2769) experienced acute brain injury. In venoarterial extracorporeal membrane oxygenation (nonextracorporeal cardiopulmonary resuscitation), the area under the receiver operator characteristic curves to predict acute brain injury, central nervous system ischemia, and intracranial hemorrhage were 0.67, 0.67, and 0.62, respectively. The true-positive, true-negative, false-positive, false-negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively, for acute brain injury. Longer extracorporeal membrane oxygenation duration, higher 24-hour extracorporeal membrane oxygenation pump flow, and higher on-extracorporeal membrane oxygenation partial pressure of oxygen were associated with acute brain injury. Of 10,775 patients receiving extracorporeal cardiopulmonary resuscitation (median age of 57.1 years, 68% were male), 16.5% (n = 1787) experienced acute brain injury. The area under the receiver operator characteristic curves for acute brain injury, central nervous system ischemia, and intracranial hemorrhage were 0.72, 0.73, and 0.69, respectively. Longer extracorporeal membrane oxygenation duration, older age, and higher 24-hour extracorporeal membrane oxygenation pump flow were associated with acute brain injury.

Conclusions

In the largest study predicting neurological complications with machine learning in extracorporeal membrane oxygenation, longer extracorporeal membrane oxygenation duration and higher 24-hour pump flow were associated with acute brain injury in nonextracorporeal cardiopulmonary resuscitation and extracorporeal cardiopulmonary resuscitation venoarterial extracorporeal membrane oxygenation.

基于树型机器学习的静脉体外膜氧合患者急性脑损伤风险预测模型:ELSO 登记分析
方法 我们纳入了体外生命支持组织注册表(2009-2021 年)中接受静脉体外膜氧合或体外心肺复苏的成人(年龄≥18 岁)。我们的主要结果是急性脑损伤:中枢神经系统缺血、颅内出血、脑死亡和癫痫发作。我们使用随机森林(Random Forest)、CatBoost、LightGBM 和 XGBoost 机器学习算法(10-fold leave-1-out 交叉验证)来预测和识别对急性脑损伤最重要的特征。我们共提取了 65 个特征:人口统计学、体外膜氧合前/体外膜氧合时实验室值、体外膜氧合前/体外膜氧合时设置。结果 在 35855 名接受静脉体外膜氧合(非体外心肺复苏)的患者中(中位年龄为 57.8 岁,66% 为男性),7.7%(n = 2769)经历了急性脑损伤。在静脉体外膜肺氧合(非体外心肺复苏)中,预测急性脑损伤、中枢神经系统缺血和颅内出血的接收器操作者特征曲线下面积分别为 0.67、0.67 和 0.62。对急性脑损伤的真阳性、真阴性、假阳性、假阴性、阳性和阴性预测值分别为 33%、88%、12%、67%、18% 和 94%。较长的体外膜氧合持续时间、较高的 24 小时体外膜氧合泵流量和较高的体外膜氧合氧分压与急性脑损伤有关。在接受体外心肺复苏的10775名患者中(中位年龄为57.1岁,68%为男性),16.5%(n = 1787)的患者出现了急性脑损伤。急性脑损伤、中枢神经系统缺血和颅内出血的接受者操作特征曲线下面积分别为 0.72、0.73 和 0.69。结论 在利用机器学习预测体外膜肺氧合神经系统并发症的最大规模研究中,在非体外心肺复苏和体外心肺复苏静脉动脉体外膜肺氧合中,较长的体外膜肺氧合持续时间和较高的 24 小时泵流量与急性脑损伤有关。
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