Pharmacoepidemiology and Drug Safety最新文献

Background and objectives: Teriflunomide is a disease-modifying therapy (DMT) for multiple sclerosis (MS). This post authorisation safety study assessed risks of adverse events of special interest (AESI) associated with teriflunomide use.

Methods: Secondary use of individual data from the Danish MS Registry (DMSR), the French National Health Data System (SNDS), the Belgian national database of health care claims (AIM-IMA) and the Belgian Treatments in MS Registry (Beltrims). We included patients treated with a DMT at the date of teriflunomide reimbursement or initiating another DMT. Adjusted hazard rates (aHR) and 95% confidence intervals were derived from Cox models with time-dependent exposure comparing teriflunomide treatment with another DMT.

Results: Of 81 620 patients (72% women) included in the cohort, 22 324 (27%) were treated with teriflunomide. After a median follow-up of 4 years, teriflunomide use compared to other DMT was not associated with a risk of all-cause mortality, severe infection, pneumoniae, herpes zoster reactivation, pancreatitis, cardiovascular condition and cancers. For opportunistic infections, aHR for teriflunomide versus other DMT was 2.4 (1.2-4.8) in SNDS, which was not bound to a particular opportunistic agent. The aHR was 2.0 (1.1-3.7) for renal failures in the SNDS, but no association was found in other data sources. A total of 187 SNDS patients had a history of renal failure prior to cohort entry. None of these patients (0%) had a renal failure recurrence when treated with teriflunomide for 19 (13%) recurrences reported for patients on another DMT.

Discussion: We found no evidence that teriflunomide use would be associated with an increased risk of AESI. Trial Registration EUPAS register: EU PAS 19610.

Purpose: To compare the performance (covariate balance, effective sample size [ESS]) of stable balancing weights (SBW) versus propensity score weighting (PSW). Two applied cases were used to compare performance: (Case 1) extreme imbalance in baseline covariates between groups and (Case 2) substantial discrepancy in sample size between groups.

Methods: Using the Premier Healthcare Database, we selected patients who (Case 1) underwent a surgical procedure with one of two different bipolar forceps between January 2000 and June 2020, or (Case 2) a neurological procedure using one of two different nonabsorbable surgical sutures between January 2000 and March 2020. Average treatment effects on the treated (ATT) weights were generated based on selected covariates. SBW was implemented using two techniques: (1) "grid search" to find weights of minimum variance at the lowest target absolute standardized mean difference (SMD); (2) finding weights of minimum variance at prespecified SMD tolerance. PSW and SBW methods were compared on postweighting SMDs, the number of imbalanced covariates, and ESS of the ATT-weighted control group.

Results: In both studies, improved covariate balance was achieved with both SBW techniques. All methods suffered from postweighting ESS that was lower than the unweighted control group's original sample size; however, SBW methods achieved higher ESS for the control groups. Sensitivity analyses using SBW to apply variable-specific SMD thresholds increased ESS, outperforming PSW.

Conclusions: In this applied example, the optimization-based SBW method provided ample flexibility with respect to prespecification of covariate balance goals and resulted in better postweighting covariate balance and larger ESS as compared with PSW.

Background: Adverse drug events (ADEs) are a frequent cause of injury in patients. Our aim was to assess whether pharmacist interventions compared with no pharmacist intervention results in reduced ADEs and potential adverse drug events (PADEs).

Methods: We searched MEDLINE, Embase, and two other databases through September 19, 2022 for any RCT assessing the effect of a pharmacist intervention compared with no pharmacist intervention and reporting on ADEs or PADEs. The risk of bias was assessed using the Cochrane tool for RCTs. A random-effects model was used to pool summary results from individual RCTs.

Results: Fifteen RCTs met the inclusion criteria. The pooled results showed a statistically significant reduction in ADE associated with pharmacist intervention compared with no pharmacist intervention (RR = 0.86; [95% CI 0.80-0.94]; p = 0.0005) but not for PADEs (RR = 0.79; [95% CI 0.47-1.32]; p = 0.37). The heterogeneity was insignificant (I² = 0%) for ADEs and substantial (I² = 77%) for PADEs. Patients receiving a pharmacist intervention were 14% less likely for ADE than those who did not receive a pharmacist intervention. The estimated number of patients needed to prevent one ADE across all patient locations was 33.

Conclusions: To our knowledge, this is the first systematic review and meta-analysis of RCTs seeking to understand the association of pharmacist interventions with ADEs and PADEs. The risk of having an ADE is reduced by a seventh for patients receiving a pharmacist care intervention versus no such intervention. The estimated number of patients needed to be followed across all patient locations to prevent one preventable ADE across all patient locations is 33.

Purpose: In the early stages of the COVID-19 pandemic, preliminary results that later proved to be incorrect suggested the possible efficacy of anti-infective drugs such as azithromycin for the treatment of SARS-CoV-2 infection. These preliminary data may have influenced the prescription of azithromycin. However, no individual-level data linking the use of this antibiotic to acute SARS-CoV-2 infection are available. The present analysis aims to fill this gap.

Methods: A retrospective population-based cohort design was used including patients diagnosed with SARS-CoV-2 infection in the period ranging from February 2020 to February 2022. The data source for antibiotic consumption was the drug database of outpatient prescriptions of Emilia-Romagna Region (Italy). Antibiotics were classified according to the Anatomical Therapeutic Chemical (ATC) classification system. Consumption rates and percentages of azithromycin DDDs (defined daily doses) during the acute phase of the infection were compared with a previous control period and with the post-acute phase. Analyses were stratified by four groups according to the prevalent virus variant at time of diagnosis.

Results: Comparing the previous control period with the acute phase of infections, the rates of azithromycin consumption (DDD per 1000 individuals per day) increased from 1.17 to 23.11, from 0.80 to 33.03, from 0.81 to 21.01, and from 1.02 to 9.76, in the pre-Alpha, Alpha, Delta, and Omicron periods, respectively. Similarly, the percentages of individuals receiving azithromycin, and the azithromycin DDDs percentages over total systemic antibiotics DDDs increased in acute phases of infection compared with control periods. The consumption rates and percentages returned to preinfection levels in the post-acute phase. In the study period, 12.9% of the use of azithromycin in the entire adult population of Emilia-Romagna was attributable to acute SARS-CoV-2 infection.

Conclusions: Considering the low likelihood of bacterial coinfections, the increased azithromycin consumption in the acute phase of SARS-CoV-2 infection suggests inappropriate prescribing of this antibiotic.

Background: Antisecretory drugs are commonly prescribed with clopidogrel-based dual antiplatelet therapy (DAPT) to prevent gastrointestinal bleeding in high-risk patients after percutaneous coronary intervention (PCI). However, omeprazole and esomeprazole (inhibiting proton pump inhibitors [PPIs]) may increase cardiovascular event rates on co-administration with clopidogrel. This study aimed to examine trends in the use of antisecretory agents in patients administered clopidogrel-based DAPT and the concomitant use of clopidogrel and inhibiting PPIs.

Methods: We used National Inpatient Sample data compiled by the Health Insurance Review & Assessment Service from 2009 to 2020. Further, we identified patients who were prescribed clopidogrel-based DAPT after PCI and investigated the concomitant use of antisecretory agents with clopidogrel. To verify the annual trend of drug utilization, we used the Cochran-Armitage trend test.

Results: From 2009 to 2020, the percentage of H2 receptor antagonist users decreased steadily (from 82.5% in 2009 to 25.3% in 2020); instead, the percentage of PPI users increased (from 23.7% in 2009 to 82.0% in 2020). The use of inhibiting PPI also increased (from 4.2% in 2009 to 30.7% in 2020). Potassium competitive acid blockers (P-CABs) were rarely used before 2019; however, in 2020, it accounted for 7.8% of the antisecretory users.

Conclusions: Our study demonstrates that the use of inhibiting PPIs increased steadily in patients administered clopidogrel-based DAPT therapy. This is a major concern since the concomitant use of inhibiting PPIs with clopidogrel could increase the risk of cardiovascular events.

Purpose: It has been suggested that statins may exert thermo-protective effects that can reduce mortality on hot days. We aimed to examine the relationship between statin adherence and mortality in days with high temperature.

Methods: Utilizing data from a prior historical new-user cohort study, we analyzed a cohort of 229 918 individuals within a state-mandated health provider in Israel who initiated statin therapy between 1998 and 2006. Adherence to statins was assessed through the mean proportion of days covered (PDC) with statins during the follow-up period. The study's primary outcome was all-cause mortality during hot days.

Results: During the study follow-up period, a total of 13 165 individuals (5.7%) died. In a multivariable model, a 10% increase in PDC with statins was associated with an HR of (0.85; 95% CI: 0.72-1.00) for deaths (n = 16) in extremely hot days (≥39°C). This association was numerically stronger compared to HR = 0.94 (0.93-0.94) in cooler days and displayed a significant difference between sexes. In males, the fully-adjusted HR for a 10% increase in PDC with statins was 0.66 (0.45-0.95), while in women, it was 0.98 (0.78-1.23). In contrast, no such effect modification was observed for death in cooler days.

Conclusions: These findings align with earlier research, supporting the notion that adherence with statin treatment may be associated with a reduced risk of death during extremely hot days, particularly among men.

Purpose: Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources.

Methods: We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step.

Conclusions: Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.

Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI).

Materials and methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas.

Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases.

Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.