Analysis of Protein Biomarkers From Hospitalized COVID-19 Patients Reveals Severity-Specific Signatures and Two Distinct Latent Profiles With Differential Responses to Corticosteroids.

To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers. Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality. Emergency departments from four medical centers. Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results. None. Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54). In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.

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Drs. Verhoef, Lopez-Espina, Reddy, and Sinha received support for article research from the National Institutes of Health (NIH). Drs. Spicer and Churpek disclosed grant funding from NIH/National Institute of General Medical Sciences, R01 GM123193; Department of Defense/Peer-reviewed medical research program, W81XWH-21-1-0009l; and NIH/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), R01-DK126933-A1. Drs. Lopez-Espina’s and Reddy’s institutions received funding from the NIH. Drs. Lopez-Espina, Bhargava, Schmalz, and Reddy disclosed they are employees of Presnosis. Dr. Sims’s institution received funding from Prenosis. Dr. Churpek’s institution received funding from National Heart, Lung, and Blood Institute R01-HL157262-01, NIH/National Institute on Aging R21 AG068720-01, NIH/National Institute on Drug Abuse R01 DA051464-01, NIH/NIDDK R21DK113420-01A1, and he disclosed there is a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. The remaining authors have disclosed that they do not have any potential conflicts of interest.