Abstract：
BACKGROUND: Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.
METHODS: All demographic, clinical, and circuit-related variables were extracted for adults with VV-ECMO support at a tertiary care center from 2016 to 2022. The primary outcome was good neurological outcome (GNO) at discharge defined as a modified Rankin Scale of 0-3.
RESULTS: Of 99 total VV-ECMO patients (median age = 48 years; 65% male), 37% had a GNO. The best performing ML model achieved an area under the receiver operating characteristic curve of 0.87. Feature importance analysis identified down-trending gas/sweep/blender flow, FiO2, and pump speed as the most salient features for predicting GNO.
CONCLUSIONS: Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.
METHODS: All demographic, clinical, and circuit-related variables were extracted for adults with VV-ECMO support at a tertiary care center from 2016 to 2022. The primary outcome was good neurological outcome (GNO) at discharge defined as a modified Rankin Scale of 0-3.
RESULTS: Of 99 total VV-ECMO patients (median age = 48 years; 65% male), 37% had a GNO. The best performing ML model achieved an area under the receiver operating characteristic curve of 0.87. Feature importance analysis identified down-trending gas/sweep/blender flow, FiO2, and pump speed as the most salient features for predicting GNO.
CONCLUSIONS: Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.
摘要：
背景：在接受静脉-静脉体外膜氧合（VV-ECMO）支持的患者中，神经系统并发症很常见。我们使用机器学习（ML）算法来识别这些患者的神经系统预后预测因子。
方法：所有人口统计，临床,从2016年至2022年,我们为在三级医疗中心接受VV-ECMO支持的成人提取了与电路相关的变量.主要结局是出院时良好的神经系统结局（GNO），定义为0-3的改良Rankin量表。
结果：在总共99名VV-ECMO患者中（中位年龄=48岁；65％为男性），37%有GNO。性能最佳的ML模型在接收器工作特性曲线下的面积为0.87。特征重要性分析确定了向下趋势的气体/吹扫/搅拌机流量,FiO2和泵转速是预测GNO的最显著特征。
结论：利用启动前和启动后变量，ML确定了最佳预测神经系统结局的ECMO生理和肺部疾病。
方法：所有人口统计，临床,从2016年至2022年,我们为在三级医疗中心接受VV-ECMO支持的成人提取了与电路相关的变量.主要结局是出院时良好的神经系统结局（GNO），定义为0-3的改良Rankin量表。
结果：在总共99名VV-ECMO患者中（中位年龄=48岁；65％为男性），37%有GNO。性能最佳的ML模型在接收器工作特性曲线下的面积为0.87。特征重要性分析确定了向下趋势的气体/吹扫/搅拌机流量,FiO2和泵转速是预测GNO的最显著特征。
结论：利用启动前和启动后变量，ML确定了最佳预测神经系统结局的ECMO生理和肺部疾病。
