A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization.
Hui HuFrancine LadenJaime HartPeter JamesJennifer FisheWilliam R HoganElizabeth ShenkmanJiang BianPublished in: Exposome (2023)
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.
Keyphrases
- sars cov
- coronavirus disease
- electronic health record
- respiratory syndrome coronavirus
- end stage renal disease
- chronic kidney disease
- ejection fraction
- physical activity
- risk factors
- air pollution
- early onset
- risk assessment
- clinical trial
- prognostic factors
- healthcare
- machine learning
- climate change
- body composition
- open label
- deep learning
- minimally invasive
- phase iii
- clinical decision support
- case control