Dataset Information

Genomic Signatures of SARS-CoV-2 Associated with Patient Mortality.

ABSTRACT: Infections with SARS-CoV-2 can progress toward multiple clinical outcomes, and the identification of factors associated with disease severity would represent a major advance to guide care and improve prognosis. We tested for associations between SARS-CoV-2 genomic variants from an international cohort of 2508 patients and mortality rates. Findings were validated in a second cohort. Phylogenetic analysis of SARS-CoV-2 genome sequences revealed four well-resolved clades which had significantly different mortality rates, even after adjusting for patient demographic and geographic characteristics. We further identified ten single-nucleotide polymorphisms (SNPs) in the SARS-CoV-2 genome that were associated with patient mortality. Three SNPs remained associated with mortality in a generalized linear model (GLM) that also included patient age, sex, geographic region, and month of sample collection. Multiple SNPs were confirmed in the validation cohort. These SNPs represent targets to assess the mechanisms underlying COVID-19 disease severity and warrant straightforward validation in functional studies.

SUBMITTER: Dumonteil E

PROVIDER: S-EPMC7912856 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. The utility of cycle threshold, at admission, as a predictor of disease severity has not been thoroughly investigated.Methods and findingsWe conducted a retrospective study of SARS-CoV-2 positive, hospitalized patients from 3/26/2020 to 8/5/2020 who had SARS-CoV-2 Ct data within 48 hours of admission (n = 1044). Only patients with complete survival data, discharged (n = 774) or died in hospital (n = 270), were included in our analysis. Laboratory, demographic, and clinical data were extracted from electronic medical records. Multivariable logistic regression was applied to examine the relationship of patient mortality with Ct values while adjusting for established risk factors. Ct was analyzed as continuous variable and subdivided into quartiles to better illustrate its relationship with outcome. Cumulative incidence curves were created to assess whether there was a survival difference in the setting of the competing risks of death versus patient discharge. Mean Ct at admission was higher for survivors (28.6, SD = 5.8) compared to non-survivors (24.8, SD = 6.0, P<0.001). In-hospital mortality significantly differed (p<0.05) by Ct quartile. After adjusting for age, gender, BMI, hypertension and diabetes, increased cycle threshold was associated with decreased odds of in-hospital mortality (0.91, CI 0.89-0.94, p<0.001). Compared to the 4th Quartile, patients with Ct values in the 1st Quartile (Ct <22.9) and 2nd Quartile (Ct 23.0-27.3) had an adjusted odds ratio of in-hospital mortality of 3.8 and 2.6 respectively (p<0.001). The discriminative ability of Ct to predict inpatient mortality was found to be limited, possessing an area under the curve (AUC) of 0.68 (CI 0.63-0.71).ConclusionSARS-CoV-2 Ct was found to be an independent predictor of patient mortality. However, further study is needed on how to best clinically utilize such information given the result variation due to specimen quality, phase of disease, and the limited discriminative ability of the test.

Dataset Information

Genomic Signatures of SARS-CoV-2 Associated with Patient Mortality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets