Risk of incident gout following exposure to recombinant zoster vaccine in US adults aged ≥50 years.

OBJECTIVE: To assess whether recombinant zoster vaccine (RZV) is associated with an increased risk of new-onset gout among US adults aged ≥50 years. METHODS: We conducted a real-world, retrospective safety study with a self-controlled risk interval (SCRI) design using administrative claims data. We included health plan members aged ≥50 years with RZV exposure, followed by incident gout within 60 days. Days 1-30 following RZV exposure were considered the risk window (RW), and days 31-60 were considered the control window (CW). We estimated the risk ratio (RR) of gout in the RW versus CW, using a conditional Poisson model. The primary analysis estimated the risk of incident gout following any RZV dose. Sensitivity analyses evaluated dose 1- and dose 2-specific risks, risk among patients compliant with recommended dose spacing of 60-183 days, adjustment for seasonality, and restriction to the pre-COVID-19 era (before December 1, 2019). RESULTS: A total of 461,323 individuals received ≥1 RZV dose; we included 302 individuals (mean age 72.5 years; 66 % male) with evidence of new-onset gout within 60 days in SCRI analyses. A total of 153 (50.7 %) individuals had gout events in the RW and 149 (49.3 %) in the CW (RR 1.03; 95 % confidence interval 0.81, 1.29). All sensitivity analyses had consistent results, with no association of RZV with incident gout. CONCLUSION: In a population of US adults aged ≥50 years, there was no statistically significant increase in the risk of gout during the 30 days immediately after RZV exposure, compared with a subsequent 30-day CW.

Triple challenges - Small sample size in both exposure and control groups to scan rare maternal outcomes in a signal identification approach: A simulation study.

There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS' ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with "true" expected incidence proportions and compared these to imprecise incidence proportions derived from sparse control samples. We varied parameters impacting Type I error and statistical power (exposure group size, outcome's incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of Type I error, and removal of very rare outcomes for TBSS analysis due to "zero" background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts.

Using Claims Data to Predict Pre-Operative BMI Among Bariatric Surgery Patients: Development of the BMI Before Bariatric Surgery Scoring System (B3S3).

Background: Lack of body mass index (BMI) measurements limits the utility of claims data for bariatric surgery research, but pre-operative BMI may be imputed due to existence of weight-related diagnosis codes and BMI-related reimbursement requirements. We used a machine learning pipeline to create a claims-based scoring system to predict pre-operative BMI, as documented in the electronic health record (EHR), among patients undergoing a new bariatric surgery. Methods: Using the Optum Labs Data Warehouse, containing linked de-identified claims and EHR data for commercial or Medicare Advantage enrollees, we identified adults undergoing a new bariatric surgery between January 2011 and June 2018 with a BMI measurement in linked EHR data ≤30 days before the index surgery (n=3226). We constructed predictors from claims data and applied a machine learning pipeline to create a scoring system for pre-operative BMI, the B3S3. We evaluated the B3S3 and a simple linear regression model (benchmark) in test patients whose index surgery occurred concurrent (2011-2017) or prospective (2018) to the training data. Results: The machine learning pipeline yielded a final scoring system that included weight-related diagnosis codes, age, and number of days hospitalized and distinct drugs dispensed in the past 6 months. In concurrent test data, the B3S3 had excellent performance (R2 0.862, 95% confidence interval [CI] 0.815-0.898) and calibration. The benchmark algorithm had good performance (R2 0.750, 95% CI 0.686-0.799) and calibration but both aspects were inferior to the B3S3. Findings in prospective test data were similar. Conclusion: The B3S3 is an accessible tool that researchers can use with claims data to obtain granular and accurate predicted values of pre-operative BMI, which may enhance confounding control and investigation of effect modification by baseline obesity levels in bariatric surgery studies utilizing claims data.

Pre-operative BMI is an important potential confounder in comparative effectiveness studies of bariatric surgeries.Claims data lack clinical measurements, but insurance reimbursement requirements for bariatric surgery often result in pre-operative BMI being coded in claims data.We used a machine learning pipeline to create a model, the B3S3, to predict pre-operative BMI, as documented in the EHR, among bariatric surgery patients based on the presence of certain weight-related diagnosis codes and other patient characteristics derived from claims data.Researchers can easily use the B3S3 with claims data to obtain granular and accurate predicted values of pre-operative BMI among bariatric surgery patients.