Data Source: the MI-COVID19 registry
We utilized data from the MI-COVID 19 registry, a multi-hospital Continuous Quality Initiative sponsored by Blue Cross Blue Shield of Michigan and Blue Care Network, which aims to improve care for hospitalized patients with COVID-19. Over 40 Michigan hospitals voluntarily participate in MI-COVID-19 [21]. Trained abstractors at each hospital collected detailed data on adult patients hospitalized with COVID-19 using a structured data collection template. Patient characteristics, including demographics, medical history, comorbidities, physical findings, laboratory results, imaging studies, and medications, were abstracted directly from medical records. The term “corticosteroid” was harmonized across abstractors and across sites to be systemic (enteral or intravenous) corticosteroid therapy. Data abstracters collected data on the dose, route, type, and duration of systemic steroids directly from medication administration records in the electronic chart.
For hospitals unable to abstract all COVID-19 hospitalizations because of high volumes, hospitalizations were sorted by day of admission (e.g., Monday–Sunday) and, for each day, a pseudo-random number (minute of hospital discharge) was used to select a sample of patients for abstraction.
Cohort Inclusion/Exclusion Criteria
We identified all patients hospitalized with laboratory-confirmed SARS-CoV-2 infection (between March 16, 2020 and August 24, 2020) from MI-COVID-19 registry hospitals, who received supplemental oxygen during day 1 and/or day 2 of hospitalization, remained alive, and were in a non-ICU hospital location. We excluded patients with: (1) length of hospitalization < 3 days; (2) no supplemental oxygen therapy during day 1 and/or day 2; (3) receipt of invasive mechanical ventilation or ICU level of care during day 1 and/or day 2; (4) admission via inter-hospital transfer; (5) who were pregnant; (6) transitioned to hospice within 3 h of hospital admission; or (7) discharged against medical advice. The goal of the inclusion and exclusion criteria was to emulate a target trial of corticosteroid therapy [22]. Because hospital days 1 and 2 were used for eligibility and strategy assignment, we ensured that no patients either met the study outcome (death, ICU, or invasive mechanical ventilation) or were ineligible for the study outcome (i.e., discharged alive from hospital) during days 1 or 2.
Treatment Assignment
Patients meeting study eligibility criteria were categorized into two treatment groups: early corticosteroids (treatment) and no early corticosteroids (comparison). Patients who received intravenous or oral dexamethasone, prednisone, methylprednisolone, or hydrocortisone within 2 days of arrival to the hospital or emergency department comprised the early steroid group. The comparison group included all patients who received no corticosteroids during the first 2 days of hospitalization. Pre-hospital corticosteroid use and corticosteroids use after day 2 were not considered in the study group assignment.
Study Outcomes
The primary outcome was clinical deterioration, defined as a composite of hospital mortality, ICU transfer, or receipt of invasive mechanical ventilation. Secondary outcomes included individual components of the composite outcome and hospital length of stay ≥ 7 days.
Subgroups
We examined the primary and secondary outcomes in the overall cohort and for several predefined subgroups by age (< 70 vs. ≥ 70 years), duration of symptoms prior to hospitalization (< 7 days vs. ≥ 7 days) and maximum oxygen requirement during hospital days 1 and 2 (FiO2 < 40% vs. FiO2 ≥ 40%).
Statistical Analysis
All variables were summarized with standard descriptive statistics, including mean and standard deviation (SD). Categorical variables were summarized using percentages. Propensity score regression adjustment was used to reduce selection bias. We used an inverse probability of treatment weighting (IPTW) approach based on patients’ propensity score (i.e., patients’ predicted probability of receiving corticosteroids given their baseline covariates) to balance the differences in baseline variables between treatment groups [23].
A non-parsimonious multivariable logistic regression model was constructed to estimate each patient’s propensity score. Variables of the propensity score (PS) model were prespecified before outcome analyses, and included: (1) patient demographics {age, gender, race, body mass index}; (2) co-morbidities {cardiac, pulmonary, diabetes, cancer}; (3) clinical symptoms on hospital presentation {fever, dyspnea}; (4) vital signs during day 1 and 2 of hospitalization {blood pressure, respiratory rate, highest oxygen support}; and (5) laboratory and radiology features on hospital presentation {creatinine, white blood cell count, and presence of imaging abnormalities}. These covariates were chosen based on clinical experience, review of literature, and data available in the COVID-19 registry [24]. We adjusted for date of admission (measured in half-month epochs) to account for temporal trends in treatment approach (e.g., hydroxychloroquine) and outcomes independent of corticosteroids.
Group distributions were evaluated to determine if the groups were comparable and the IPTW was calculated and normalized. After IPTW, the treatment and comparison groups were similar except for the slight differences in the proportion of admissions occurring during the June 2020 epochs.
We converted different steroids into maximum prednisone equivalents to reduce heterogeneity and to create a standardized framework for comparing steroid dose and duration between treatment and control groups (Steroid Conversion Calculator; MDCalc).
Characteristics between the unadjusted and adjusted groups were compared using t tests for continuous variables and Pearson’s chi-squared tests for categorical variables. Propensity score-weighted regression models were then fitted to compare primary and secondary outcomes between groups. Odds ratios with 95% confidence intervals (OR, 95% CI) were reported, and overall two-sided alpha-level of 0.05 was used to determine statistical significance. Data were analyzed using SAS software v.9.4 (SAS Institute, Cary, NC, USA).
IRB Statement
MI-COVID-19 was deemed to be quality improvement work and received the designation of non-regulated by the University of Michigan institutional review board. Each hospital participating in the Mi-COVID19 initiative is required to have a signed Data Use Agreement with the Coordinating Center for the collaborative. The data submitted is a limited dataset and is therefore sharable in an aggregated and de-identified format.