Albert Leng, Benjamin Shou, Olivia Liu, Preetham Bachina, Andrew Kalra, Errol L Bush, Glenn J R Whitman, Sung-Min Cho
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引用次数: 0
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）算法来确定这些患者神经系统结果的预测因素：提取了2016年至2022年在一家三级医疗中心接受VV-ECMO支持的成人的所有人口统计学、临床和电路相关变量。主要结果是出院时的良好神经功能预后（GNO），定义为改良Rankin量表0-3分：在99名VV-ECMO患者（中位年龄=48岁；65%为男性）中，37%的患者出现了GNO。表现最好的 ML 模型的接收者操作特征曲线下面积为 0.87。特征重要性分析表明，气体/扫气/吹气流量、FiO2 和泵速的下降趋势是预测 GNO 的最显著特征：利用启动前和启动后的变量，ML 确定了最能预测神经系统预后的 ECMO 生理和肺部条件。
Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.
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.
Conclusion: Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.
期刊介绍：
Lung publishes original articles, reviews and editorials on all aspects of the healthy and diseased lungs, of the airways, and of breathing. Epidemiological, clinical, pathophysiological, biochemical, and pharmacological studies fall within the scope of the journal. Case reports, short communications and technical notes can be accepted if they are of particular interest.
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