BackgroundOzanimod is a novel immune modulator that could be useful in viral pulmonary infections by reducing lung inflammation. It is an S1P receptor ligand known to induce bradycardia and more serious adverse cardiac effects, such as atrioventricular block and QT interval prolongation. We present a substudy of the COVID-19 Ozanimod Intervention (COZI) trial in which ozanimod was administered in acute pulmonary infection patients, to assess cardiac safety. MethodsIn this pilot randomized open-label trial, COVID-19 patients requiring oxygen support were randomized into 2 groups: standard-of-care + ozanimod (OZA) vs standard-of-care alone (SOC). All patients were monitored with a 14-day electrocardiogram monitor (CardioSTAT, Icentia, Quebec, QC) during their hospitalization. We evaluated the cardiac effects of ozanimod on heart rate (HR), PR interval length, and QT interval duration. ResultsA total of 42 patients were analyzed: 23 in the SOC group and 19 in the OZA group. Mean hourly HR over the first 10 days of treatment decreased in the OZA group, compared with that in the SOC group (P < 0.0001). The maximum decrease in HR occurred on day 3. The maximum decrease in HR occurred on day 3, without a significant difference between groups: 49 beats per minute (interquartile range, 42-59) in the OZA group, and 54 beats per minute (48–60) in the SOC group, P = 0.45. No high-degree atrioventricular block was recorded. QT and PR interval median values were within the normal range in both groups, without a significant difference. ConclusionsThe maximal reduction in HR occurred 3 days after the onset of ozanimod treatment in patients hospitalized for COVID-19, but it did not remain significant over the 10-day treatment period. No relevant cardiac adverse event was observed.

BackgroundAdministrative health data and cardiac device registries can be used to empirically evaluate outcomes and costs after implantable cardioverter defibrillator (ICD) implantation. These datasets often have incomplete information on the indication for implantation (primary vs secondary prevention of sudden cardiac death). MethodsWe used 16 years of population-based cardiac device registry and administrative health data from British Columbia, Canada, to derive and internally validate statistical models that predict the likely indication for ICD implantation. We used chart review data as the reference standard for ICD indication in the Cardiac Device Registry database (CDR; 2004-2012 [Cardiac Services BC]) and nonmissing indication as the reference standard in the Heart Information System registry database (HEARTis; 2013-2019 [Cardiac Services BC]). We created 3 logistic regression prediction models in each database: one using only registry data, one using only administrative data, and one using both registry and administrative data. We assessed the predictive performance of each model using standard metrics after optimism correction with 200 bootstrap resamples. ResultsModels that used registry data alone demonstrated excellent predictive performance (sensitivity ≥ 89%; specificity ≥ 87%). Models that used only administrative data performed well (sensitivity ≥ 84%; specificity ≥ 70%). Models that used both registry and administrative data showed modest gains over those that used registry data alone (sensitivity ≥ 90%; specificity ≥ 89%). ConclusionsAdministrative health data and cardiac device registry data can distinguish secondary prevention ICDs from primary prevention ICDs with acceptable sensitivity and specificity. Imputation of missing ICD indication might make these data resources more useful for research and health system monitoring.