Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
PLoS One. 2022 Jul 12;17(7):e0271381. doi: 10.1371/journal.pone.0271381. eCollection 2022.
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.
我们使用 SARS-CoV-2 全基因组测序 (WGS) 和电子健康记录 (EHR) 数据，研究住院 COVID-19 患者的病毒基因组与临床特征和严重结局之间的关联。
我们对 2020 年 3 月至 2021 年 5 月期间在一家大型学术转诊医院住院的严重 COVID-19 感染患者进行了病例对照研究。进行了 SARS-CoV-2 WGS，从 EHR 中获得了人口统计学和临床特征。严重 COVID-19（病例患者）定义为以下一种或多种情况：需要补充氧气、机械通气或住院期间死亡。对照为诊断为 COVID-19 但不符合严重感染标准的住院患者。我们构建了预测模型，将临床和人口统计学变量以及 WGS 数据（包括谱系、分支和 SARS-CoV-2 SNP/GWAS 数据）纳入其中，用于严重 COVID-19，使用多变量逻辑回归。
在 1802 名住院 SARS-CoV-2 阳性患者中，我们对 590 名患者的样本进行了 WGS，其中 396 名患者为病例患者，194 名患者为对照。年龄（p=0.001）、BMI（p=0.032）、检测阳性时间（p=0.001）、Charlson 合并症指数（p=0.001）、慢性心力衰竭史（p=0.003）、心房颤动史（p=0.002）或糖尿病（p=0.007）与病例对照状态显著相关。尽管在调整混杂变量后，感染分支 20A 与严重疾病的风险增加相关（p=0.024，OR=3.25；95%CI：1.31-8.06），但 SARS-CoV-2 WGS 数据并没有明显改变上述危险因素分析的结果。
在因 COVID-19 住院的人群中，年龄较大、BMI 较高、检测阳性时间较早、有慢性心力衰竭、心房颤动或糖尿病史以及感染分支 20A SARS-CoV-2 株可预测严重 COVID-19。
