Journal Article

Predictive model for severe COVID-19 using SARS-CoV-2 whole-genome sequencing and electronic health record data, March 2020-May 2021

Zhu, Lei, Srinivasa, Vatsala, Marsh, Jane W., Phan, Tung, Wells, Alan, Griffith, Marissa P., Harrison, Lee H., Van Tyne, Daria, Snyder, Graham M., Marroquin, Oscar C., Waggle, Kady, Collins, Kevin

2022

Overview

We used SARS-CoV-2 whole-genome sequencing (WGS) and electronic health record (EHR) data to investigate the associations between viral genomes and clinical characteristics and severe outcomes among hospitalized COVID-19 patients. We conducted a case-control study of severe COVID-19 infection among patients hospitalized at a large academic referral hospital between March 2020 and May 2021. SARS-CoV-2 WGS was performed, and demographic and clinical characteristics were obtained from the EHR. Severe COVID-19 (case patients) was defined as having one or more of the following: requirement for supplemental oxygen, mechanical ventilation, or death during hospital admission. Controls were hospitalized patients diagnosed with COVID-19 who did not meet the criteria for severe infection. We constructed predictive models incorporating clinical and demographic variables as well as WGS data including lineage, clade, and SARS-CoV-2 SNP/GWAS data for severe COVID-19 using multiple logistic regression. Of 1,802 hospitalized SARS-CoV-2-positive patients, we performed WGS on samples collected from 590 patients, of whom 396 were case patients and 194 were controls. Age (p = 0.001), BMI (p = 0.032), test positive time period (p = 0.001), Charlson comorbidity index (p = 0.001), history of chronic heart failure (p = 0.003), atrial fibrillation (p = 0.002), or diabetes (p = 0.007) were significantly associated with case-control status. SARS-CoV-2 WGS data did not appreciably change the results of the above risk factor analysis, though infection with clade 20A was associated with a higher risk of severe disease, after adjusting for confounder variables (p = 0.024, OR = 3.25; 95%CI: 1.31-8.06). Among people hospitalized with COVID-19, older age, higher BMI, earlier test positive period, history of chronic heart failure, atrial fibrillation, or diabetes, and infection with clade 20A SARS-CoV-2 strains can predict severe COVID-19.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Age / Analysis / Biology and life sciences / Body mass index / Cardiac arrhythmia / Cardiovascular disease / Chronic infection / Chronic obstructive pulmonary disease / Comorbidity / Computer and Information Sciences / Congestive heart failure / Coronaviruses / COVID-19 / COVID-19 diagnostic tests / Demographic variables / Demographics / Demography / Diabetes / Diabetes mellitus / DNA sequencing / Electronic health records / Electronic medical records / Electronic records / Ethnicity / Factor analysis / Fibrillation / Gender / Gene sequencing / Genomes / Health risks / Heart failure / Hispanic Americans / Hospitalization / Hospitals / Mechanical ventilation / Medical laboratories / Medical records / Medicine and Health Sciences / Nucleotide sequencing / Oxygen / Patients / Phylogenetics / Prediction models / Risk analysis / Risk factors / Severe acute respiratory syndrome / Severe acute respiratory syndrome coronavirus 2 / Ventilation / Viral diseases / Whole genome sequencing