Use of an EHR to inform an administrative data algorithm to categorize inpatient COVID-19 severity

STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSAlgorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data (RWD). ObjectiveTo explore use of electronic health record (EHR) data to inform an administrative data algorithm for classification of supplemental oxygen or noninvasive ventilation (O2/NIV) and invasive mechanical ventilation (IMV) to assess disease severity in hospitalized COVID-19 patients. DesignIn this retrospective cohort study, we developed an initial procedure-based algorithm to identify O2/NIV, IMV, and NEITHER O2/NIV nor IMV in two inpatient RWD sources. We then expanded the algorithm to explore the impact of adding diagnoses indicative of clinical need for O2/NIV (hypoxia, hypoxemia) or IMV (acute respiratory distress syndrome) and O2-related patient vitals available in the EHR. Observed changes in severity categorization were used to augment the administrative algorithm. SettingOptum de-identified COVID-19 EHR data and HealthVerity claims and chargemaster data (March - August 2020). ParticipantsAmong patients hospitalized with COVID-19 in each RWD source, our motivating example selected dexamethasone (DEX+) initiators and a random selection of patients who were non-initiators of a corticosteroid of interest (CSI-) matched on date of DEX initiation, age, sex, baseline comorbidity score, days since admission, and COVID-19 severity level (NEITHER, O2/NIV, IMV) on treatment index. Main Outcome and MeasuresInpatient COVID-19 severity was defined using the algorithms developed to classify respiratory support requirements among hospitalized COVID-19 patients (NEITHER, O2/NIV, IMV). Measures were reported as the treatment-specific distributions of patients in each severity level, and as observed changes in severity categorization between the initial procedure-based and expanded algorithms. ResultsIn the administrative data cohort with 5,524 DEX+ and CSI- patient pairs matched using the initial procedure-based algorithm, 30% were categorized as O2/NIV, 5% as IMV, and 65% as NEITHER. Among patients assigned NEITHER via the initial algorithm, use of an expanded algorithm informed by the EHR-based algorithm shifted 54% DEX+ and 28% CSI- to O2/NIV, and 2% DEX+ and 1% CSI- to IMV. Among patients initially assigned O2/NIV, 7% DEX+ and 3% CSI- shifted to IMV. Conclusions and RelevanceApplication of learnings from an EHR-based exploration to our administrative algorithm minimized treatment-differential misclassification of COVID-19 severity.

Effectiveness of the Single-Dose Ad26.COV2.S COVID Vaccine

ImportanceVaccination against the SARS-CoV-2 virus is critical to control the pandemic. Randomized trials demonstrated efficacy of the single-dose Ad26.COV2.S COVID vaccine but data on longer-term protection in clinical practice and effectiveness against variants are needed. ObjectiveTo assess the effectiveness of Ad26.COV2.S in preventing COVID infections and COVID-related hospitalizations in clinical practice, the longer-term stability of its protective effect and effectiveness against Delta variants. DesignCohort study of newly Ad26.COV2.S-vaccinated and unvaccinated individuals. SettingU.S. insurance claims data through July 2021. ParticipantsIndividuals 18 years and older newly vaccinated with Ad26.COV2.S and up to 10 unvaccinated individuals matched exactly by age, sex, date, location, comorbidity index plus 17 COVID-19 risk factors via propensity score (PS) matching. InterventionVaccination with Ad26.COV2.S versus no vaccination. Main outcomesWe estimated vaccine effectiveness (VE) for observed COVID-19 infection and COVID-19-related hospitalization, nationwide and stratified by age, immunocompromised status, calendar time, and states with high incidence of the Delta variant. We corrected VE estimates for under-recording of vaccinations in insurance data. ResultsAmong 390,517 vaccinated and 1,524,153 matched unvaccinated individuals, VE was 79% (95% CI, 77% to 80%) for COVID-19 and 81% (79% to 84%) for COVID-19-related hospitalizations. VE was stable over calendar time. Among states with high Delta variant incidence, VE during June/July 2021 was 78% (73% to 82%) for infections and 85% (73% to 91%) for hospitalizations. VE for COVID-19 was higher in individuals <50 years (83%; 81% to 85%) and lower in immunocompromised patients (64%; 57% to 70%). All estimates were corrected for under-recording; uncorrected VE was 69% (67% to 71%) and 73% (69% to 76%), for COVID-19 and COVID-19-related hospitalization, respectively. ConclusionsThese non-randomized data across U.S. clinical practices show high and stable vaccine effectiveness of Ad26.COV2.S over time before the Delta variant emerged to when the Delta variant was dominant.

Real-world data suggest antibody positivity to SARS-CoV-2 is associated with a decreased risk of future infection

ImportanceThere is limited evidence regarding whether the presence of serum antibodies to SARS-CoV-2 is associated with a decreased risk of future infection. Understanding susceptibility to infection and the role of immune memory is important for identifying at-risk populations and could have implications for vaccine deployment. ObjectiveThe purpose of this study was to evaluate subsequent evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among individuals who are antibody-positive compared with those who are antibody-negative, using real-world data. DesignThis was an observational descriptive cohort study. ParticipantsThe study utilized a national sample to create cohorts from a de-identified dataset composed of commercial laboratory test results, open and closed medical and pharmacy claims, electronic health records, hospital billing (chargemaster) data, and payer enrollment files from the United States. Patients were indexed as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test recorded in the database. Patients with more than 1 antibody test on the index date where results were discordant were excluded. Main Outcomes/MeasuresPrimary endpoints were index antibody test results and post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, as measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, such as recorded signs and symptoms or prior evidence of COVID-19 (diagnoses or NAAT+) and recorded comorbidities. ResultsWe included 3,257,478 unique patients with an index antibody test. Of these, 2,876,773 (88.3%) had a negative index antibody result, 378,606 (11.6%) had a positive index antibody result, and 2,099 (0.1%) had an inconclusive index antibody result. Patients with a negative antibody test were somewhat older at index than those with a positive result (mean of 48 versus 44 years). A fraction (18.4%) of individuals who were initially seropositive converted to seronegative over the follow up period. During the follow-up periods, the ratio (CI) of positive NAAT results among individuals who had a positive antibody test at index versus those with a negative antibody test at index was 2.85 (2.73 - 2.97) at 0-30 days, 0.67 (0.6 - 0.74) at 31-60 days, 0.29 (0.24 - 0.35) at 61-90 days), and 0.10 (0.05 - 0.19) at >90 days. ConclusionsPatients who display positive antibody tests are initially more likely to have a positive NAAT, consistent with prolonged RNA shedding, but over time become markedly less likely to have a positive NAAT. This result suggests seropositivity using commercially available assays is associated with protection from infection. The duration of protection is unknown and may wane over time; this parameter will need to be addressed in a study with extended duration of follow up. Key PointsO_ST_ABSQuestionC_ST_ABSCan real-world data be used to evaluate the comparative risk of SARS-CoV-2 infection for individuals who are antibody-positive versus antibody-negative? FindingOf patients indexed on a positive antibody test, 10 of 3,226 with a NAAT (0.3%) had evidence of a positive NAAT > 90 days after index, compared with 491 of 16,157 (3.0%) indexed on a negative antibody test. MeaningIndividuals who are seropositive for SARS-CoV-2 based on commercial assays may be at decreased future risk of SARS-CoV-2 infection.