Mitigating the Severity of COVID-19 Illness in the Primary Care Patient Population through Early Identification and Close Monitoring of Underlying Comorbidities

Abstract

AbstractPurposePrior studies have identified risk factors which prognosticate severity of SARS-CoV-2 illness among hospitalized patients. Since the majority of patients first present to ambulatory care sites, there is a need to identify early predictors of disease progression in this population.MethodsThis retrospective cohort study investigated the impact of underlying comorbid conditions on SARS-CoV-2 infection severity in the ambulatory setting. All patients who presented to a single federally qualified health center (FQHC) between March-May 2020 with a positive SARS-CoV-2 test were reviewed for inclusion. Patient demographics, symptomology, prior medical history, and outcomes were collected.Results301 patients were included, with nearly equal numbers of patients with (n=151) and without (n=150) underlying comorbidities. Overall, 269 patients (89%) had a mild outcome and 32 patients (11%) had a severe outcome. Advanced age (OR: 9.4 [95% CI: 3.4-27.4], p < 0.001) and male gender (OR: 3.2 [95% CI: 1.2-9.8], p = 0.02) were significant predictors of severe outcomes. Additionally, every obesity category (1: BMI = 30.0–34.9; 2: BMI = 35–39.9; 3: BMI = 40.0+) was associated with more severe outcomes compared to non-obese (OR: 3.5, p = 0.05; OR: 5.2, p = 0.03; OR: 13.9, p = 0.01). Compared to an HbA1C < 6, an HbA1C of 7.1–8.0 showed a clinically significant association.ConclusionSARS-CoV-2 severity can be prognosticated in the ambulatory population by the presence and severity of pre-existing comorbidities. Early identification and risk stratification of these comorbidities will allow clinicians to develop plans for closer monitoring to prevent severe illness.