背景:静脉-体外膜氧合(VV-ECMO)是难治性呼吸衰竭患者的一种治疗方法。从体外膜氧合(ECMO)中拔管的决定通常涉及断奶试验和临床直觉。迄今为止,预测指标有限,无法指导临床决策,以确定哪些患者将成功断奶和拔管.
目的:本研究旨在帮助临床医生决定将患者从ECMO拔管,使用VV-ECMO结果的持续评估(CEVVO),基于深度学习的模型,用于预测VV-ECMO支持的患者拔管成功。可以每天应用运行度量以将患者分类为高风险和低风险组。利用这些数据,提供者可根据其专业知识和CEVVO考虑启动断奶试验.
方法:从哥伦比亚大学欧文医学中心接受VV-ECMO支持的118例患者收集数据。使用基于长期短期记忆的网络,CEVVO是第一个能够将离散临床信息与从ECMO设备收集的连续数据集成的模型。共进行了12套5折交叉验证,以评估性能,这是使用接收器工作特征曲线下面积(AUROC)和平均精度(AP)测量的。要将预测值转化为临床有用的度量,模型结果被校准并分层为风险组,范围从0(高风险)到3(低风险)。为了进一步研究CEVVO的性能优势,使用高斯过程回归生成2个合成数据集。第一个数据集保留了患者数据集的长期依赖性,而第二个没有。
结果:与现代模型相比,CEVVO始终表现出优异的分类性能(与第二高AUROC和AP相比,P<.001和P=.04)。尽管模型的逐个患者预测能力可能太低,无法整合到临床环境中(AUROC95%CI0.6822-0.7055;AP95%CI0.8515-0.8682),患者风险分类系统显示出更大的潜力.当在72小时测量时,高危人群拔管成功率为58%(7/12),而低危组的成功拔管率为92%(11/12;P=.04).当在96小时测量时,高危和低危组脱管率分别为54%(6/11)和100%(9/9),分别(P=0.01)。我们假设CEVVO的性能提高归因于其有效捕获瞬态时间模式的能力。的确,与逻辑回归和密集神经网络相比,CEVVO在具有固有时间依赖性的合成数据上表现出改进的性能(P<.001)。
结论:解释和整合大型数据集的能力对于创建能够帮助临床医生对VV-ECMO支持的患者进行风险分层的准确模型至关重要。我们的框架可以指导未来将CEVVO纳入更全面的重症监护监测系统。
BACKGROUND: Venovenous extracorporeal membrane oxygenation (VV-
ECMO) is a therapy for patients with refractory respiratory failure. The decision to decannulate someone from extracorporeal membrane oxygenation (ECMO) often involves weaning trials and clinical intuition. To date, there are limited prognostication metrics to guide clinical decision-making to determine which patients will be successfully weaned and decannulated.
OBJECTIVE: This
study aims to assist clinicians with the decision to decannulate a patient from
ECMO, using Continuous Evaluation of VV-
ECMO Outcomes (CEVVO), a deep learning-based model for predicting success of decannulation in patients supported on VV-
ECMO. The running metric may be applied daily to categorize patients into high-risk and low-risk groups. Using these data, providers may consider initiating a weaning
trial based on their expertise and CEVVO.
METHODS: Data were collected from 118 patients supported with VV-ECMO at the Columbia University Irving Medical Center. Using a long short-term memory-based network, CEVVO is the first model capable of integrating discrete clinical information with continuous data collected from an ECMO device. A total of 12 sets of 5-fold cross validations were conducted to assess the performance, which was measured using the area under the receiver operating characteristic curve (AUROC) and average precision (AP). To translate the predicted values into a clinically useful metric, the model results were calibrated and stratified into risk groups, ranging from 0 (high risk) to 3 (low risk). To further investigate the performance edge of CEVVO, 2 synthetic data sets were generated using Gaussian process regression. The first data set preserved the long-term dependency of the patient data set, whereas the second did not.
RESULTS: CEVVO demonstrated consistently superior classification performance compared with contemporary models (P<.001 and P=.04 compared with the next highest AUROC and AP). Although the model\'s patient-by-patient predictive power may be too low to be integrated into a clinical setting (AUROC 95% CI 0.6822-0.7055; AP 95% CI 0.8515-0.8682), the patient risk classification system displayed greater potential. When measured at 72 hours, the high-risk group had a successful decannulation rate of 58% (7/12), whereas the low-risk group had a successful decannulation rate of 92% (11/12; P=.04). When measured at 96 hours, the high- and low-risk groups had a successful decannulation rate of 54% (6/11) and 100% (9/9), respectively (P=.01). We hypothesized that the improved performance of CEVVO was owing to its ability to efficiently capture transient temporal patterns. Indeed, CEVVO exhibited improved performance on synthetic data with inherent temporal dependencies (P<.001) compared with logistic regression and a dense neural network.
CONCLUSIONS: The ability to interpret and integrate large data sets is paramount for creating accurate models capable of assisting clinicians in risk stratifying patients supported on VV-ECMO. Our framework may guide future incorporation of CEVVO into more comprehensive intensive care monitoring systems.