Background: Studies have demonstrated increased risk of major atherothrombotic events in CYP2C19 loss‐of‐function (LOF) variant carriers versus non‐carriers treated with clopidogrel after percutaneous coronary intervention (PCI). We sought to evaluate real‐world outcomes with the clinical implementation of CYP2C19 ‐guided antiplatelet therapy after PCI. Methods and Results: Data from 9 medical centers where genotyping was performed in the setting of PCI were included. Alternative therapy with prasugrel or ticagrelor was recommended for patients with a CYP2C19 LOF variant. The primary outcome was the composite of major atherothrombotic events (all‐cause death, myocardial infarction, ischemic stroke, stent thrombosis, or hospitalization for unstable angina) within 12 months following PCI. Moderate or severe/life‐threatening bleeding within 12 months was a secondary outcome. Among 3342 patients, 1032 (31%) were LOF carriers, of whom 571/1032 (55%) were treated with alternative therapy. In LOF carriers, the rate of major atherothrombotic events was lower in patients treated with alternative therapy versus clopidogrel (adjusted HR, 0.56; 95% CI 0.39–0.82). In those without a LOF allele, no difference was observed (adjusted HR, 1.07; 95% CI 0.71–1.60). There was no difference in bleeding with alternative therapy versus clopidogrel in either LOF carriers or those without a LOF allele. Conclusions: Real‐world data demonstrate lower atherothrombotic risk in CYP2C19 LOF carriers treated with alternative therapy versus clopidogrel and similar risk in those without a LOF allele treated with clopidogrel or alternative therapy. These data suggest that PCI patients treated with clopidogrel should undergo genotyping so that CYP2C19 LOF carriers can be identified and treated with alternative therapy.

Concerns about the effects of propranolol on the central nervous system (CNS) in the infantile hemangioma (IH) population have been raised. We conducted a meta-analysis of the CNS and sleep-related effects of oral propranolol in IH patients. PubMed, Embase, Cochrance, Web of Science, and Clinicaltrials.gov were searched for relevant studies. We included clinical trials that compared oral propranolol with other treatments among IH patients under 6 years old and monitored and reported any adverse events. Study characteristics, types and number of adverse events were abstracted. Cochrane Collaboration Risk of Bias Tool was used to assess risk of bias. Our main outcomes were CNS and sleep-related effects. Random-effects models were used to estimate the pooled risk ratio. We did not observe statistically significant associations between oral propranolol and CNS or sleep-related effects. Oral propranolol appeared to have a safer profile of CNS effects than corticosteroids (RR = 0.27, 95% CI 0.02–3.00), but had an increased risk versus non-corticosteroids (for CNS effect, RR = 1.40, 95% CI 0.86–2.27; for sleep-related effects, RR = 1.63, 95% CI 0.88–3.03). Despite no statistically significant associations, there were suggestive findings of increased CNS effects and sleep-related risk of propranolol versus non-corticosteroids. In practice, CNS and sleep-related events should be monitored more closely among IH patients treated with oral propranolol.

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 with imprecise incidence proportions derived from sparse control samples. We varied parameters that have an impact on 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. This article is part of a Special Collection on Pharmacoepidemiology.

With Medicaid covering half of US pregnancies, Medicaid Analytic eXtract (MAX) provides a valuable data source to enrich understanding about stillbirth etiologies. We developed and validated a claims-based algorithm to predict GA at stillbirth. We linked the stillbirths identified in MAX 1999-2013 to Florida Fetal Death Records (FDRs) to obtain clinical estimates of GA (N=825). We tested several algorithms including using a fixed median GA, median GA at the time of specific prenatal screening tests, and expanded versions considering additional predictors of stillbirth within including linear regression and random forest models. We estimated the proportion of pregnancies with differences of ± 1, 2, 3 and 4 weeks between the predicted and FDR GA and the model mean square error (MSE). We validated the selected algorithms in two external samples. The best performing algorithm was a random forest model (MSE of 12.67 weeks²) with 84% of GAs within ± 4 weeks. Assigning a fixed GA of 28 weeks resulted in an MSE of 60.21 weeks² and proportions of GA within ± 4 weeks of 32%. We observed consistent results in the external samples. Our prediction algorithm for stillbirths can facilitate pregnancy research in the Medicaid population.

Composite major congenital malformation (MCM) outcomes are commonly used to assess teratogenic effects of prenatal medication exposure, but this approach dilutes effect estimates when the risk is confined to a specific MCM. Tree-based scan statistics address this by screening outcomes using a hierarchical tree, enabling detection of specific risks without predefined hypotheses. To apply this method across ICD-9-CM and ICD-10-CM eras, we developed a unified hierarchical outcomes tree for MCM. We selected ICD-9-CM and ICD-10-CM codes classified as congenital anomalies, removing minor malformations, chromosomal anomalies, and single-gene conditions. A multilevel tree was built based on the Multi-level Clinical Classification Software, General Equivalence Mappings, and expert review. We validated the tree using birth cohorts from MarketScan and Medicaid databases (2011-2013; 2016-2018), assessing the balance of MCM prevalences within 1 year of birth via standardized mean differences (SMDs). The final tree included 1023 codes, organized into 244 clinical MCM groups at the most granular level. We identified 572 107 (2011-2013) and 360 167 infants (2016-2018) in MarketScan and 362 820 and 3 500 589 infants in Medicaid. All SMDs were below 0.1, indicating consistency across coding eras. This hierarchical MCM tree bridges ICD-9-CM and ICD-10-CM, enabling consistent outcome definitions and enhancing the detection of specific teratogenic risks.