Automating Population-Based Studies for 30-Day Adverse Drug Event Detection in Older Adults with Chronic Kidney Disease Using High-Throughput Computing

Aim/Objective: To use high-throughput computing and automation to conduct 700? drug-safety cohort studies in older adults in Ontario, Canada. Methods: The studies were population-based, new-user cohort studies conducted using linked administrative health care databases in Ontario, Canada (January 1, 2008, to March 1, 2020). Individuals aged 66 years or older with a baseline estimated glomerular filtration rate (eGFR) measurement within 12 months before the cohort entry who filled at least one outpatient prescription through the Ontario Drug Benefit program were included. We identified 3.2 million older adults in the source population during the study period and built 700? medication cohorts, each containing mutually exclusive groups of new users and nonusers. Nonusers were randomly assigned cohort entry dates that followed the same distribution of prescription start dates as new users. New users and nonusers were balanced on *400 baseline health characteristics using inverse probability of treatment weighting on propensity scores within 3 strata of baseline eGFR: C60, 45 to \ 60, \ 45 mL/min per 1.73 m2. We compared new user and nonuser groups on 74 clinically relevant outcomes in the 30 days after cohort entry. In each cohort, eGFR-stratum-specific weighted risk ratios and risk differences were obtained using modified Poisson regression and binomial regression, respectively. Additive and multiplicative interactions by eGFR category were examined. Drugoutcome associations that met pre-specified criteria (identified signals) will be further examined in additional analyses and visualizations. Results: The initial medication cohorts had a median of 6120 new users per cohort (IQR: 1469-38 839) and a median of 1 088 301 nonusers (IQR: 751 697-1 267 009). Medications with the largest number of new users were amoxicillin trihydrate (n = 1 000 032), cephalexin (n = 571 566), prescription acetaminophen (n = 571 563), and ciprofloxacin (n = 504,374); 19% to 29% of new users in these cohorts had an eGFR \ 60. We found a significant increase in 368 exposure-outcome associations as kidney function declined. Antibiotics (macrolides and cephalosporins), SSRIs, and benzodiazepines, were linked with the most associations. clarithromycin (22) and baclofen (17) were the most frequently associated medications with ADR. Conclusion: This accelerated approach to conducting postmarket drug-safety studies has the potential to more efficiently detect drugsafety signals in a vulnera