Outcome-Stratified Analysis of Biomarker Trajectories for Patients Infected With Severe Acute Respiratory Syndrome Coronavirus 2. Issue 10 (12th May 2021)

• Record Type: Journal Article
• Authors: Bowring, Mary G; Wang, Zitong; Xu, Yizhen; Betz, Joshua; Muschelli, John; Garibaldi, Brian T; Zeger, Scott L
• Is Part Of: American journal of epidemiology. Volume 190:Issue 10(2021)
• Page Start: 2094
• Page End: 2106
• Publication Date: 2021-05-12
• Subjects: case–control design -- COVID-19 -- linear mixed effects models -- longitudinal data; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwab138
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: 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 (Spo 2 ) to fraction of inspired oxygen (Fio 2 ), 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 Spo 2 -to-Fio 2 ratio trajectories diverge approximately 8–10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Spo 2 -to-Fio 2 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.