Pharmacoepidemiology and Drug Safety最新文献

Purpose: To assess the impact of the 2018 European additional risk minimisation measures (aRMMs) regarding the use of valproate in women of childbearing potential (WCBP) and during pregnancy.

Methods: A cross-sectional, non-interventional survey conducted in six European countries among 1982 healthcare professionals (HCPs) (July-October 2020) and 779 WCBP treated with valproate for epilepsy, bipolar disorder or other indications (August 2020-February 2021). HCPs were prescribing physicians (neurologists, psychiatrists, paediatricians and GPs), gynaecologists and pharmacists. Prespecified criteria were defined for success in the dimensions of awareness, knowledge and behaviour (correct answers to ≥ 80% of questions at individual level) and overall success (≥ 90%/80% successful HCPs/patients respectively, in the behaviour dimension and one of the two other dimensions).

Results: HCPs and patients did not meet the success criteria either overall or in any dimension. Highest success rates were in the behaviour dimension for gynaecologists (71.7%), pharmacists (49.7%) and patients (51.2%), and in the awareness dimension for prescribing physicians (23.6%). HCPs reported being unfamiliar with some educational materials and lacked knowledge of detailed prescribing conditions for valproate and the need for contraception regardless of sexual activity. More than 50% of patients were aware of the relevant patient materials and knew about the teratogenic risks of valproate.

Conclusion: Self-reported levels of awareness, knowledge and behaviour varied considerably by HCP type and among patient respondents. Further investigation is needed into why certain measures of the pregnancy prevention programme are not well known and followed, to improve their effectiveness. This will be addressed in a qualitative study which will be based on interviews with HCPs and patients.

Purpose: We validated claims-based algorithms using the International Classification of Diseases, Tenth Revision (ICD-10) to identify patients with the first-ever coronavirus disease (COVID-19) onset between May 2020 and August 2022.

Methods: The study cohort was comprised of residents of one municipality enrolled in a public insurance program. This study used data provided by the municipality, including residents' insurer-based medical claims data linked to the Health Center Real-time Information-Sharing System (HER-SYS). The HER-SYS data included positive results from COVID-19 tests and were used as reference standards. Claims-based algorithms #1 and #2 were U07.1, B34.2, with and without suspicious diagnoses, respectively. Claims-based algorithms #3 and #4 were U07.1 with and without suspicious diagnoses, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm.

Results: The study cohort included 165 038 residents, including 13 402 residents were the reference standard. For the entire period, the sensitivity, specificity, PPV, and NPV were 55.7% (95% confidence interval: 54.8%-56.5%), 65.4% (65.2%-65.6%), 11.5% (11.3%-11.8%), and 98.9% (98.8%-99.0%) for Algorithm #1, and 67.0% (66.2%-67.8%), 88.1% (87.9%-88.3%), 31.6% (31.1%-32.2%), and 97.8% (97.7%-97.8%) for Algorithm #2, and 52.9% (52.0%-53.7%), 67.1% (66.9%-67.3%), 11.5% (11.2%-11.8%), and 98.3% (98.3%-98.4%) for Algorithm #3, 62.6% (61.8%-63.4%), 88.5% (88.3%-88.7%), 30.9% (30.3%-31.4%), and 97.3% (97.2%-97.4%) for Algorithm #4, respectively.

Conclusions: Our study showed that the validity of claims-based algorithms consisting of COVID-19-related ICD-10 codes to identify patients with first-onset COVID-19 is limited.

Purpose: Accurate identification of hepatic decompensation is essential for pharmacoepidemiologic research among patients with chronic liver disease.

Methods: An algorithm using ≥ 1 inpatient or ≥ 2 outpatient International Classification of Diseases, 10th revision (ICD-10) codes for hepatic decompensation was developed in Veterans Health Administration data from October 2015 through July 2019. Medical records were reviewed by hepatologists to confirm cases. The positive predictive value (PPV) of the coding algorithm for confirmed hepatic decompensation was calculated.

Results: Hepatic decompensation was confirmed in 149/185 records meeting the algorithm (PPV 81%; 95% CI, 70%, 90%). The most common hepatic decompensation diagnosis was ascites. Only 56% of confirmed cases had an accompanying diagnosis code for cirrhosis.

Conclusions: Our ICD-10-based coding algorithm identified hepatic decompensation with high PPV in Veterans Health Administration data.

Purpose: The Danish National Hospital Medicine Register (SMR) was established in 2018 to centralize and standardize medication use data across Danish hospitals. This task was previously managed by individual hospital registration systems across the five regions. This initiative addresses the need for a unified, detailed understanding of hospital medication use to monitor healthcare delivery, improve patient outcomes, and support research.

Methods: The SMR has comprehensive coverage of medication use in hospitals in Denmark, offering a national overview that was previously missing. It features high-quality data, with efforts to ensure completeness and accuracy. The data collected encompasses key categories such as types of medications, doses, administration times and practices, and specific treatment indications. The SMR facilitates collaboration among Danish regional health authorities and national health agencies, enhancing decision-making and planning across the regions.

Results: The establishment of the SMR has provided a centralized and standardized database for medication use across Danish hospitals. This unification replaces the previously fragmented systems, allowing for better monitoring of healthcare delivery and supporting improvements in patient outcomes and research.

Conclusion: The upcoming version of the SMR will include data on dispensed outpatient medications, covering most medication use across all hospitals. This expansion will further enhance the register's utility for health authorities, clinicians, and researchers by providing a more comprehensive understanding of medication use in Denmark.

Purpose: The generation of representative disease phenotypes is important for ensuring the reliability of the findings of observational studies. The aim of this manuscript is to outline a reproducible framework for reliable and traceable phenotype generation based on real world data for use in the Data Analysis and Real-World Interrogation Network (DARWIN EU). We illustrate the use of this framework by generating phenotypes for two diseases: pancreatic cancer and systemic lupus erythematosus (SLE).

Methods: The phenotyping process involves a 14-steps process based on a standard operating procedure co-created by the DARWIN EU Coordination Centre in collaboration with the European Medicines Agency. A number of bespoke R packages were utilised to generate and review codelists for two phenotypes based on real world data mapped to the OMOP Common Data Model.

Results: Codelists were generated for both pancreatic cancer and SLE, and cohorts were generated in six OMOP-mapped databases. Diagnostic checks were performed, which showed these cohorts had broadly similar incidence and prevalence figures to previously published literature, despite significant inter-database variability. Co-occurrent symptoms, conditions, and medication use were in keeping with pre-specified clinical descriptions based on previous knowledge.

Conclusions: Our detailed phenotyping process makes use of bespoke tools and allows for comprehensive codelist generation and review, as well as large-scale exploration of the characteristics of the resulting cohorts. Wider use of structured and reproducible phenotyping methods will be important in ensuring the reliability of observational studies for regulatory purposes.

Purpose: The oncology quality, characterization, and assessment of real-world data (Oncology QCARD) Initiative was formed to develop a set of minimum study design and data elements needed to evaluate the fitness of the real-world data (RWD) source(s) proposed in an initial study concept as part of early interaction with scientific reviewers.

Methods: A multidisciplinary executive committee (EC) was established to guide the Oncology QCARD Initiative. The EC conducted a landscape review of published literature, guidances, and guidelines to evaluate relevant dimensions of data quality measurement. Guided by the review and informed by expert feedback, the Oncology QCARD Initial Protocol Characterization (IPC) provides a summary of minimum elements needed to adequately describe an initial clinical study concept that involves RWD and is intended to support decision-making.

Results: Fit-for-use data and fit-for-purpose design emerged as themes from the landscape analysis. Data that are fit-for-use are both relevant (sufficiently capturing exposure, outcomes, and covariates) and reliable (understanding data accrual and quality control and whether the data represent the underlying concepts they are intended to represent) to answer a specific research question. A fit-for-purpose design takes appropriate steps to ensure internal and external validity and allows for transparency in reporting. The QCARD-IPC focuses on high-level characteristics of RWD sources and study design domains including data temporality, population, medical product exposure, comparators, and covariates, endpoints, statistical analysis, and data quality assurance plans.

Conclusions: Evaluation of studies including RWD requires understanding the data source, study design, and potential biases to preliminarily evaluate whether selected RWD are fit-for-use for the research question. The Oncology QCARD-IPC provides a structured, transparent approach to facilitate early review and enhanced communication between study sponsors and scientific reviewers of initial study proposals including RWD.

Aim: Benzodiazepine prescription is a growing phenomenon among the elderly population. However, information related to the frequency of these drugs among the elderly population attending in emergency departments (ED) and its impact over prognosis is scarce. The aim of this study is to assess the prevalence of benzodiazepine prescription and to analyze its association with short-term prognosis in elderly patients attended in ED.

Methods: A retrospective analysis of the EDEN (Emergency Department Elderly in Need) cohort was conducted. This registry included all elderly patients attending in 52 Spanish EDs for any condition, between April 1st and 7th in 2019. Socio-demographic data, comorbidities, and medication were recorded by consulting the patient's electronic health records. The assessed outcomes consisted on new ED visit, hospitalization, and mortality at 30 days after the first ED visit, associated with the use of benzodiazepines at baseline in comparison with no prescription of benzodiazepines. Crude and adjusted logistic regression analyses including patient's comorbidities were performed. Two sensitivity analyses were performed considering concomitant prescription of other central nervous system depressants as well as direct discharge from the ED.

Results: 25 557 patients were evaluated (mean age 78 [IQR: 71-84]). 7865 (30.8%) patients were taken benzodiazepines at admission. After adjustment for comorbidities and other central nervous system drugs, benzodiazepine prescription was associated with ED revisit [OR: 1.10 (95%CI: 1.03-1.18)]. Similar results were found in the sensitivity analysis, eliminating patients with central nervous depressors [OR: 1.11 (1.03-1.25)] and patients discharged to home [OR: 1.13 (1.04-1.23)]. No association was found between the use of these drugs and new hospitalizations [OR: 0.90 (0.77-1.05)] or mortality 30 days after discharge [OR: 1.01 (0.88-1.18)]. The results held for all three outcomes in the sensitivity analyses.

Conclusion: The use of benzodiazepines is a frequent phenomenon among the elderly population attended in the ED, being associated with an increased risk of new visits to the emergency room, but not with an increased risk of 30-day hospitalization or mortality.

Purpose: To examine associations of prenatal and early-life anti-infective exposures with obesity at 7 years.

Methods: In this nationwide, registry-based, prevalence study, we included all children with an anthropometric assessment at age 7 years from the Children's Database and linked their data with Danish population-based registries from 2001 to 2018. We defined exposure to anti-infectives (anti-bacterials, anti-virals, and anti-fungals) by outpatient dispensings or by infection diagnoses at hospital encounters. The earliest date defined the exposure timing category: prenatal (-9 months- < 0 months), infancy (0- < 2 years), and early childhood (2- < 5 years). We computed prevalence ratios (aPRs) for associations of anti-infective exposure with obesity prevalence at 7 years of age, adjusting for maternal and perinatal factors.

Results: We included 460 363 children (51% boys). Prevalence of obesity at 7 years of age was 38% higher (aPR = 1.38, 95% confidence interval (CI): 1.27-1.49) among children exposed to any anti-infective, 21% higher (aPR = 1.21, 95% CI: 1.12-1.31) among children exposed to anti-infectives in infancy, and 14% higher (aPR = 1.14, 95% CI: 1.03-1.26) among children exposed to anti-infectives in early childhood. Exposure to anti-bacterials was associated with obesity in a similar time-dependent pattern [prenatal: aPR = 1.39 (95% CI: 1.29-1.50), infancy: aPR = 1.21 (95% CI: 1.12-1.30), and early childhood: aPR = 1.14 (95% CI: 1.03-1.25)]. For anti-virals and anti-fungals, exposure during infancy and early childhood was associated with larger aPRs than prenatal exposure. Furthermore, obesity prevalence increased monotonically with number of the anti-infective prescriptions.

Conclusion: These findings suggest that prenatal and early-life exposure to anti-infectives increases the risk of childhood obesity and that the magnitude of the associations depends on anti-infective type, timing, and dose.