Suppr超能文献

基于树的机器学习的静脉-动脉体外膜肺氧合患者急性脑损伤风险预测模型:体外生命支持组织注册分析

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

作者信息

Kalra Andrew, Bachina Preetham, Shou Benjamin L, Hwang Jaeho, Barshay Meylakh, Kulkarni Shreyas, Sears Isaac, Eickhoff Carsten, Bermudez Christian A, Brodie Daniel, Ventetuolo Corey E, Kim Bo Soo, Whitman Glenn J R, Abbasi Adeel, Cho Sung-Min

机构信息

Division of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md.

Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pa.

出版信息

JTCVS Open. 2024 Jun 8;20:64-88. doi: 10.1016/j.xjon.2024.06.001. eCollection 2024 Aug.

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.

摘要

目的

我们旨在确定机器学习能否预测急性脑损伤,并识别接受静脉-动脉体外膜肺氧合(VA-ECMO)治疗的患者发生急性脑损伤的可改变风险因素。

方法

我们纳入了体外生命支持组织注册中心(2009 - 2021年)中接受静脉-动脉体外膜肺氧合或体外心肺复苏的成年人(年龄≥18岁)。我们的主要结局是急性脑损伤: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、CatBoost、LightGBM和XGBoost机器学习算法(10折留一法交叉验证)来预测并识别对急性脑损伤最重要的特征。我们总共提取了65个特征:人口统计学特征、体外膜肺氧合前/体外膜肺氧合时的实验室值,以及体外膜肺氧合前/体外膜肺氧合时的设置。

结果

在35855例接受静脉-动脉体外膜肺氧合(非体外心肺复苏)的患者中(中位年龄57.8岁,66%为男性),7.7%(n = 2769)发生了急性脑损伤。在静脉-动脉体外膜肺氧合(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受试者工作特征曲线下面积分别为0.67、0.67和0.62。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%。体外膜肺氧合持续时间更长、24小时体外膜肺氧合泵流量更高以及体外膜肺氧合时的氧分压更高与急性脑损伤相关。在10775例接受体外心肺复苏的患者中(中位年龄57.1岁,68%为男性),16.5%(n = 1787)发生了急性脑损伤。急性脑损伤、中枢神经系统缺血和颅内出血的受试者工作特征曲线下面积分别为0.72、0.73和0.69。体外膜肺氧合持续时间更长、年龄更大以及24小时体外膜肺氧合泵流量更高与急性脑损伤相关。

结论

在最大规模的关于机器学习预测体外膜肺氧合神经并发症的研究中,在非体外心肺复苏和体外心肺复苏的静脉-动脉体外膜肺氧合中,更长的体外膜肺氧合持续时间和更高的24小时泵流量与急性脑损伤相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbd0/11405982/18e5a282b59a/ga1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验