背景:COVID-19(PASC)的后遗症,也被称为长科维德,是急性COVID-19后一系列长期症状的广泛分组。这些症状可能发生在一系列生物系统中,导致在确定PASC的危险因素和该疾病的病因方面面临挑战。对预测未来PASC的特征的理解是有价值的,因为这可以为识别高风险个体和未来的预防工作提供信息。然而,目前有关PASC危险因素的知识有限。
目的:使用来自国家COVID队列合作组织的55,257名患者(其中1名PASC患者与4名匹配对照)的样本,作为美国国立卫生研究院长期COVID计算挑战的一部分,我们试图从一组经筛选的临床知情协变量中预测PASC诊断的个体风险.国家COVID队列合作组织包括来自美国84个地点的2200多万患者的电子健康记录。
方法:我们预测了个体PASC状态,给定协变量信息,使用SuperLearner(一种集成机器学习算法,也称为堆叠)来学习梯度提升和随机森林算法的最优组合,以最大化接收器算子曲线下的面积。我们基于3个级别评估了变量重要性(Shapley值):个体特征,时间窗口,和临床领域。我们使用一组随机选择的研究地点从外部验证了这些发现。
结果:我们能够准确预测个体PASC诊断(曲线下面积0.874)。观察期长度的个体特征,急性COVID-19和病毒性下呼吸道感染期间卫生保健相互作用的数量对随后的PASC诊断最具预测性.暂时,我们发现基线特征是未来PASC诊断的最具预测性的,与之前的特征相比,during,或急性COVID-19后。我们发现医疗保健使用的临床领域,人口统计学或人体测量学,和呼吸因素是PASC诊断的最具预测性的因素。
结论:这里概述的方法提供了一个开放源代码,使用超级学习者使用电子健康记录数据预测PASC状态的应用示例,可以在各种设置中复制。在个体预测因子和临床领域,我们一致发现,与医疗保健使用相关的因素是PASC诊断的最强预测因子.这表明,任何使用PASC诊断作为主要结果的观察性研究都必须严格考虑异质医疗保健的使用。我们的研究结果支持以下假设:临床医生可能能够在急性COVID-19诊断之前准确评估患者的PASC风险,这可以改善早期干预和预防性护理。我们的发现还强调了呼吸特征在PASC风险评估中的重要性。
■RR2-10.1101/2023.07.27.23293272。
BACKGROUND: Postacute
sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited.
OBJECTIVE: Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States.
METHODS: We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites.
RESULTS: We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis.
CONCLUSIONS: The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment.
UNASSIGNED: RR2-10.1101/2023.07.27.23293272.