Pharmacoepidemiology

Objectives: Real-world evidence that supports decision-making must meet numerous criteria, including validated identification of clinical outcomes. This study aimed to develop and validate a method for identifying new cases of myocardial infarction (MI) and ischemic stroke (IS) within real-world clinical data in China. Methods: Algorithms to identify MI and IS events were developed using ICD-10-CM codes and Chinese diagnosis keywords within the Tianjin Regional Healthcare Database. Validation followed predefined criteria: MI required cardiac troponin elevation and ischemic symptoms or cardiac troponin elevation and electrocardiogram changes; IS required clinical symptoms and neuroimaging confirmation of cerebral Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) reports. Positive predictive value (PPV) with 95% confidence intervals (CI) was calculated for each outcome. Results: Among 304 MI and 302 IS cases randomly selected, approximately half were identified using ICD-10-CM codes and half through Chinese diagnosis keywords. Overall PPV for MI was 69% (95% CI: 63–74%), with similar PPVs across identification methods. PPV increased to 88% for inpatient MI and 97% for primary inpatient MI. For IS, overall PPV was 65% (95% CI: 58–71%), with higher PPV for cases identified by ICD-10-CM codes (76%) compared to keyword-only cases (56%). PPV increased to 76% for inpatient IS and 91% for primary inpatient IS. Conclusions: The use of ICD-10-CM codes and Chinese diagnosis keywords in primary inpatient diagnoses provides a validated approach for the identification of clinical outcomes of MI and IS within real-world clinical data in China.

Objectives: To evaluate reports of diabetic ketoacidosis (DKA) associated with antipsychotic drug (APD) use submitted to the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from January 2000 to December 2022. DKA cases were identified using the MedDRA preferred term “diabetic ketoacidosis” in reports listing antipsychotic drugs as suspect medications. Disproportionality analyses, including the proportional reporting ratio (PRR) and empirical Bayes geometric mean (EBGM), were used to assess reporting patterns. Multiple analyses were performed, including those restricted to primary suspect listed drugs only, expanded to incorporate secondary suspect drugs, and sensitivity analyses excluding reports submitted by legal professionals. Results: Among 19,961 DKA reports in FAERS, 2489 (12.5%) listed atypical antipsychotics as the primary suspect drug, whereas reports involving typical APDs were rare. The majority of reports were submitted by healthcare professionals (74.1%), and nearly half originated from the United States (45.4%). Hospitalization was a frequent outcome, reported in 74.3% of cases. Quetiapine and olanzapine were the most frequently reported atypical APDs, with disproportionality analyses demonstrating strong safety signals when compared to all other drugs in FAERS: olanzapine PRR 13.2 (95% CI: 12.4–14.2) and quetiapine PRR 11.8 (95% CI: 11.1–12.5). The findings remained consistent across multiple sensitivity analyses, including incorporating secondary suspect drugs, when the comparator group was restricted to only psychotropic drugs, and excluding reports submitted by lawyers. Conclusions: This pharmacovigilance analysis highlights a potential safety signal for DKA with atypical antipsychotic drugs, notably quetiapine and olanzapine. While these findings do not establish causality, they underscore the need for further investigation using clinical and epidemiological data.

Pharmacovigilance approaches have conventionally focused on the use of epidemiological data to detect emergent adverse drug reactions (ADRs). Recent advances in the use and availability of real-world data have expanded opportunities to detect ADR signals in medical records. We provide a limited review of pharmacovigilance practices and tools we have specifically considered implementing into our comprehensive medication management clinic and associated research programs. Use of pharmacogenomic variants has proven useful only on a limited scale as such data are reliant on low-dimensional approaches matching variants to drugs, often with small effect sizes. As such, most ADRs go unrecognized, undocumented, and unactionable. We posit that an idealized pharmacovigilance framework that relies on artificial-intelligence-assisted reporting with adjudication by pharmacovigilance experts and new models of ambulatory pharmaceutical practice would establish the following attributes: (1) all metadata relating to medication use would be available in the medical record in computable and interoperable data models, (2) digital surveillance tools would detect most ADR events with attributed pharmacological contributions, (3) all events would be characterized using standard adjudication rubrics, and (4) all events would iteratively inform an ADR knowledgebase and improve models to advance detection and prediction of ADR during the course of patient care with a focus on having the necessary tools for clinicians to prevent ADRs. This review provides a limited and focused framework for more systematic documentation of ADRs and tactics to mitigate the idiopathic nature of most ADRs.