全暴露体的空间和环境关联研究以及 COVID-19 住院治疗的多暴露体得分。

Exposome Pub Date : 2023-04-11 eCollection Date: 2023-05-01 DOI:10.1093/exposome/osad005
Hui Hu, Francine Laden, Jaime Hart, Peter James, Jennifer Fishe, William Hogan, Elizabeth Shenkman, Jiang Bian
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摘要

环境暴露与 COVID-19 的严重程度有关。以往的研究只研究了极少数环境因素,而且往往只是单独研究,而没有考虑环境的整体性或暴露体。此外,现有的严重 COVID-19 风险预测模型主要依赖于人口和临床因素。为了弥补这些不足,我们开展了一项空间和环境暴露体关联研究(ExWAS),并利用来自个人空间和环境暴露体的丰富信息,开发了 COVID-19 住院治疗的多暴露体评分(PES)。研究人员从 OneFlorida+ 临床研究网络获取了 2020 年 3 月至 2021 年 10 月期间 50 368 名年龄在 18 岁及以上、SARS-CoV-2 PCR/抗原实验室检测呈阳性或确诊为 COVID-19 的患者的个人电子健康记录。根据地理编码的居住史,我们将来自 10 个数据源的共计 194 个空间和环境暴露组因素与每位患者进行了时空关联。我们在 ExWAS 中使用了标准的两阶段程序,并使用梯度提升决策树模型开发和验证了 PES。我们确定了与 COVID-19 住院治疗密切相关的四个暴露组测量指标,包括 2-氯苯乙酮、低食物可及性、邻里贫困和健身中心可及性降低。在不考虑暴露组因素的情况下,所有患者的初始预测模型的测试曲线下面积(AUC)为 0.778。纳入暴露组数据后,测试曲线下面积(AUC)增至 0.787。在以无合并症和年龄在 18-24 岁的人群为重点的亚组分析中也观察到了类似的结果。这项针对 COVID-19 住院治疗的空间和环境暴露组研究证实了之前报道的风险因素,但也产生了新的预测因素,值得进行更有针对性的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization.

Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.

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