Outcome-Stratified Analysis of Biomarker Trajectories for Patients Infected With Severe Acute Respiratory Syndrome Coronavirus 2.
COVID-19
case–control design
linear mixed effects models
longitudinal data
Journal
American journal of epidemiology
ISSN: 1476-6256
Titre abrégé: Am J Epidemiol
Pays: United States
ID NLM: 7910653
Informations de publication
Date de publication:
01 10 2021
01 10 2021
Historique:
received:
24
10
2020
revised:
12
04
2021
accepted:
04
05
2021
pubmed:
14
5
2021
medline:
8
10
2021
entrez:
13
5
2021
Statut:
ppublish
Résumé
Longitudinal trajectories of vital signs and biomarkers during hospital admission of patients with COVID-19 remain poorly characterized despite their potential to provide critical insights about disease progression. We studied 1884 patients with severe acute respiratory syndrome coronavirus 2 infection from April 3, 2020, to June 25, 2020, within 1 Maryland hospital system and used a retrospective longitudinal framework with linear mixed-effects models to investigate relevant biomarker trajectories leading up to 3 critical outcomes: mechanical ventilation, discharge, and death. Trajectories of 4 vital signs (respiratory rate, ratio of oxygen saturation (Spo2) to fraction of inspired oxygen (Fio2), pulse, and temperature) and 4 laboratory values (C-reactive protein (CRP), absolute lymphocyte count (ALC), estimated glomerular filtration rate, and D-dimer) clearly distinguished the trajectories of patients with COVID-19. Before any ventilation, log(CRP), log(ALC), respiratory rate, and Spo2-to-Fio2 ratio trajectories diverge approximately 8-10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Spo2-to-Fio2 ratio, and estimated glomerular filtration rate trajectories again diverge 10-20 days before death or discharge. Trajectories improved until discharge and remained unchanged or worsened until death. Our approach characterizes the distribution of biomarker trajectories leading up to competing outcomes of discharge versus death. Moving forward, this model can contribute to quantifying the joint probability of biomarkers and outcomes when provided clinical data up to a given moment.
Identifiants
pubmed: 33984860
pii: 6274822
doi: 10.1093/aje/kwab138
pmc: PMC8241476
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2094-2106Subventions
Organisme : NIGMS NIH HHS
ID : T32 GM136577
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007309
Pays : United States
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.