Aloka Chakravarty

Purpose: To characterize select laboratory tests ordered versus reported for patients diagnosed with COVID‐19 in administrative healthcare and commercial laboratory data.Methods: Among patients with an outpatient COVID‐19 diagnosis claim in HealthVerity data (01/01/2021–12/31/2022), this study described baseline characteristics and descriptively compared SARS‐CoV‐2 diagnostic tests and liver function tests from administrative healthcare (insurance claims and hospital billing data) and commercial laboratories, overall and by code type (e.g., CPT, LOINC). Select liver function tests were also described by method‐specific and methodless LOINC.Results: Among 214 998 patients with COVID‐19, 46.1% had a SARS‐CoV‐2 molecular diagnostic test recorded within 7 days of diagnosis (in either administrative or laboratory data); 44.5% had a corresponding CPT in medical claims, while only 10.0% had a corresponding LOINC in laboratory data. In contrast, the six most common liver function tests (albumin, aspartate aminotransferase, total protein, alkaline phosphatase, alanine aminotransferase, and total bilirubin) were identified in 55.7%–56.6% of patients via LOINC, but only in 3.2%–4.2% via CPT claims. Of the total count of select liver function tests performed in the laboratory data, 99.7% of aspartate aminotransferase, 96.1% of direct bilirubin, and 82.9% of lactate dehydrogenase were reported by methodless LOINC rather than method‐specific LOINC.Conclusions: Important differences were identified between orders for SARS‐CoV‐2 diagnostic tests and liver function tests, as well as missingness of LOINC method, highlighting challenges related to completeness of laboratory data in real‐world data sources. These challenges underscore a need to improve data quality when considering the utility of laboratory data for research.

Background: As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. Methods: Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. Results: Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred 30%) in some datasets—limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. Conclusion: Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources–a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

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.

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.

Qualification of a biomarker for use in a medical product development program requires a statistical strategy that aligns available evidence with the proposed context of use (COU), identifies any d...