A Descriptive Cohort Study of Drug Utilization Patterns Among Patients Hospitalized With Coronavirus Disease 2019 in the United States, January 2021-February 2022.

Background: There is a dearth of drug utilization studies for coronavirus disease 2019 (COVID-19) treatments in 2021 and beyond after the introduction of vaccines and updated guidelines; such studies are needed to contextualize ongoing COVID-19 treatment effectiveness studies during these time periods. This study describes utilization patterns for corticosteroids, interleukin-6 (IL-6) inhibitors, Janus kinase inhibitors, and remdesivir among hospitalized adults with COVID-19, over the entire hospitalization, and within hospitalization periods categorized by respiratory support requirements. Methods: This descriptive cohort study included United States adults hospitalized with COVID-19 admitted from 1 January 2021 through 1 February 2022; data included HealthVerity claims and hospital chargemaster. The number and distribution of patients were reported for the first 3 drug regimen lines initiated. Results: The cohort included 51 066 patients; the most common initial drug regimens were corticosteroids (23.4%), corticosteroids plus remdesivir (25.1%), and remdesivir (4.4%). IL-6 inhibitors and Janus kinase inhibitors were included in later drug regimens and were more commonly administered with both corticosteroids and remdesivir than with corticosteroids alone. IL-6 inhibitors were more commonly administered than Janus kinase inhibitors when patients received high-flow oxygen or ventilation. Conclusions: These findings provide important context for comparative studies of COVID-19 treatments with study periods extending into 2021 and later. While prescribing generally aligned with National Institutes of Health COVID-19 treatment guidelines during this period, these findings suggest that prescribing preference, potential confounding by indication, and confounding by prior/concomitant use of other therapeutics should be considered in the design and interpretation of comparative studies.

A Structured Process to Identify Fit-for-Purpose Study Design and Data to Generate Valid and Transparent Real-World Evidence for Regulatory Uses.

Generating evidence from real-world data requires fit-for-purpose study design and data. In addition to validity, decision makers require transparency in the reasoning that underlies study design and data source decisions. The 2019 Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real-World Evidence (SPACE) and the 2021 Structured Process to Identify Fit-For-Purpose Data (SPIFD)-intended to be used together-provide a step-by-step guide to identify decision grade, fit-for-purpose study design and data. In this update (referred to as "SPIFD2" to encompass both the design and data aspects) we provide an update to these frameworks that combines the templates into one, more explicitly calls for articulation of the hypothetical target trial and sources of bias that may arise in the real-world emulation, and provides explicit references to the Structured Template and Reporting Tool for Real-World Evidence (STaRT-RWE) tables that we suggest using immediately after invoking the SPIFD2 framework. Following the steps recommended in the SPIFD2 process requires due diligence on the part of the researcher to ensure that every aspect of study design and data selection is rationalized and supported by evidence. The resulting stepwise documentation enables reproducibility and clear communication with decision makers, and it increases the likelihood that the evidence generated is valid, fit-for-purpose, and sufficient to support healthcare and regulatory decisions.

Describing characteristics and treatment patterns of patients hospitalized with COVID-19 by race and ethnicity in a national RWD during the early months of the pandemic.

OBJECTIVE: To describe differences by race and ethnicity in treatment patterns among hospitalized COVID-19 patients in the US from March-August 2020. METHODS: Among patients in de-identified Optum electronic health record data hospitalized with COVID-19 (March-August 2020), we estimated odds ratios of receiving COVID-19 treatments of interest (azithromycin, dexamethasone, hydroxychloroquine, remdesivir, and other steroids) at hospital admission, by race and ethnicity, after adjusting for key covariates of interest. RESULTS: After adjusting for key covariates, Black/African American patients were less likely to receive dexamethasone (adj. OR [95% CI]: 0.83 [0.71, 0.96]) and more likely to receive other steroids corticosteroids (adj. OR [95% CI]: 2.13 [1.90, 2.39]), relative to White patients. Hispanic/Latino patients were less likely to receive dexamethasone than Not Hispanic/Latino patients (adj. OR [95% CI]: 0.69 [0.58, 0.82]). CONCLUSIONS: Our findings suggest that COVID-19 treatments patients received in Optum varied by race and ethnicity after adjustment for other possible explanatory factors. In the face of rapidly evolving treatment landscapes, policies are needed to ensure equitable access to novel and repurposed therapeutics to avoid disparities in care by race and ethnicity.

Categorization of COVID-19 severity to determine mortality risk.

PURPOSE: Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data. METHODS: Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID-19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non-invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28-day maximum to report mortality risks and rates overall and by stratified by severity. Trends for heterogeneity in mortality risk and rate across severity classifications were evaluated using Cochran-Armitage and Logrank trend tests, respectively. RESULTS: Among 118 117 patients, the algorithm categorized patients in increasing severity as NEITHER (36.7%), O2/NIV (54.3%), and IMV (9.0%). Associated mortality risk (and 95% CI) was 11.8% (11.6-12.0%) overall and increased with severity [3.4% (3.2-3.5%), 11.5% (11.3-11.8%), 47.3% (46.3-48.2%); p < 0.001]. Mortality rate per 1000 person-days (and 95% CI) was 15.1 (14.9-15.4) overall and increased with severity [5.7 (5.4-6.0), 14.5 (14.2-14.9), 32.7 (31.8-33.6); p < 0.001]. CONCLUSION: As expected, we observed a positive association between the algorithm-defined severity on admission and 28-day mortality risk and rate. Although performance remains to be validated, this provides some assurance that this algorithm may be used for confounding control or stratification in treatment effect studies.