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Predictive model for severe COVID-19 using SARS-CoV-2 whole-genome sequencing and electronic health record data, March 2020-May 2021.
Zhu, Lei; Marsh, Jane W; Griffith, Marissa P; Collins, Kevin; Srinivasa, Vatsala; Waggle, Kady; Van Tyne, Daria; Snyder, Graham M; Phan, Tung; Wells, Alan; Marroquin, Oscar C; Harrison, Lee H.
  • Zhu L; Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Marsh JW; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
  • Griffith MP; Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Collins K; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
  • Srinivasa V; Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Waggle K; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
  • Van Tyne D; Healthcare Data and Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America.
  • Snyder GM; Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Phan T; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
  • Wells A; Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Marroquin OC; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
  • Harrison LH; Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
PLoS One ; 17(7): e0271381, 2022.
Article in English | MEDLINE | ID: covidwho-1933385
ABSTRACT

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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).

CONCLUSIONS:

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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Atrial Fibrillation / COVID-19 / Heart Failure Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0271381

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Atrial Fibrillation / COVID-19 / Heart Failure Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0271381