Drug Safety最新文献

Background: Adverse event reporting systems are an important source of safety signals for drug use in pregnancy, but their usefulness in the identification of potential drug-drug interactions (DDIs) remains unclear.

Objective: Our objective was to explore the reliability of signal detection for pharmacokinetic DDIs during pregnancy in adverse event reporting systems, focusing on potential interactions between antipsychotics (APs) or antidepressants (ADs) and drugs modifying cytochrome P450 (CYP450) activity, increasing the occurrence of gestational diabetes mellitus (GDM).

Methods: Reports related to the use of drugs during pregnancy were identified in VigiBase, the World Health Organization (WHO) global database of adverse event reports. Potential interacting drugs were selected based on WHO Drug Standardised Drug Groupings for CYP450 isoenzymes involved in the metabolic pathway of the AP or AD of interest. We conducted statistical interaction analysis using the omega disproportionality measure and including concomitant medication to identify potential DDIs, followed by a case series review for supporting evidence. Evaluation was subjective by author consensus.

Results: Of the 30 drug-drug-event combinations considered, statistical signals emerged for escitalopram, citalopram, and sertraline and the simultaneous use of CYP2D6 inhibitors with a higher relative reporting rate of GDM. However, case series review of reports did not support the existence of these DDIs because of uncertainties regarding the actual timing of medication use reported as concomitant.

Conclusion: Statistical signals of DDIs between ADs and potential interacting drugs during pregnancy were identified but not pursued further after case reviews. Uncertainty around medication use and event timing affected the reliability of the outcomes. These findings highlight the need to validate signals using detailed report data and stress the importance of accurate medication reporting.

Background: Problems with medication management are consistently identified as key concerns for the quality of residential aged care (RAC). Incident reports can provide valuable information on key issues related to medication management; however, few studies have explored medication incidents in RAC settings.

Objectives: To investigate the characteristics of medication incidents at different stages of medication management and identify the risk factors associated with incidents.

Methods: A retrospective longitudinal cohort study was conducted using medication incidence data from 25 RAC facilities in New South Wales, Australia. All medication incidents between 1 July 2014 and 31 August 2021 relating to 5709 aged care residents aged ≥ 65 years were included. The outcome measure was the medication incidence rate (IR), quantified as the number of medication incidents per 1000 resident days. A multilevel Poisson regression model was performed to identify risk factors associated with exposure to medication incidents.

Results: A total of 5708 medication incidents were analysed. The overall medication IR was 1.81 per 1000 resident days (95% CI 1.76, 1.86). Of 5709 residents, 35% (n = 2016) had at least one recorded medication incident, of which 1095 (> 50%) had more than one. The majority of the incidents were associated with medication administration (3023 incidents, 53%), followed by supply (n = 1546, 27%) and monitoring the response to the medication (n = 548, 9.6%). The outcome of the incident on residents was reported in 5165 (90%) incidents, with 724 (14%) requiring the resident to be monitored by the hospital, general practitioner (GP), or staff. Respite admissions were associated with a higher risk of medication incidents including potentially harmful incidents, compared with permanent admissions (rate ratio (RR) = 1.908, 95% CI 1.646, 2.211, p < 0.01). Residents with Parkinson's disease had a 1.5-fold greater risk of a medication incident (RR = 1.586, 95% CI 1.318, 1.908) compared with residents without Parkinson's. The administration of more than five medications (polypharmacy) was associated with an increased risk of medication incidents (RR = 2.019, 95% CI 1.930, 2.111).

Conclusions: Medication incidents affected more than one-third of older adults in RAC facilities. Improvement strategies should focus on medication administration, supply and monitoring, with particular attention given to respite residents and those with multimorbidity and polypharmacy.

VigiBase, the WHO global database of adverse event reports for medicines and vaccines, receives information on suspected adverse effects of medicinal products from countries, regions and territories that are members of the WHO Programme for International Drug Monitoring. The database serves as a global resource for pharmacovigilance signal management and scientific development. Its initial establishment through the programme in 1968 has also contributed to the international harmonisation of adverse event report data. As of 31 December 2024, the database includes over 40 million individual case reports coded in standard terminologies (WHODrug and MedDRA^®). These reports, of which ~ 80% relate to medicines and ~ 20% to vaccines, come from over 160 countries. This profile paper aims to provide an update on the database infrastructure including data capture, management and analysis tools. It also presents a summary of global patterns and trends of key report characteristics, as well as strengths and limitations of the data to offer context to users and the global pharmacovigilance community.

Background: The US Food and Drug Administration (FDA) requires pharmaceutical manufacturers to implement Risk Evaluation and Mitigation Strategy (REMS) programs for certain medications that carry serious safety risks. One possible REMS requirement is routine monitoring via laboratory tests. However, in March 2020, the FDA issued a temporary enforcement discretion policy enabling prescribers to apply medical judgment for completing REMS-required laboratory testing.

Objective: We aimed to assess whether physicians were aware of the FDA's temporary policy and if they changed their laboratory testing practices during the coronavirus disease 2019 pandemic.

Methods: We designed a survey of US physicians prescribing one of seven medications: ambrisentan, bosentan, clozapine, isotretinoin, lenalidomide, pomalidomide, and thalidomide. The study was conducted over two waves, May 2022 to October 2022 and October 2022 to January 2023. Multivariable logistic regression modeling was used to examine predictors of each outcome.

Results: The combined response rate between the two waves of survey administration was 21%. Among 949 physician respondents, 438 (47%; 927 question respondents) reported awareness and 192 (21%; 926 question respondents) reported changing practices. Among the 438 physicians who reported awareness, 176 (40%) reported changing practices. Characteristics associated with awareness included sex (female vs male, odds ratio [OR] = 1.92, 95% confidence interval [CI] 1.37-2.69); recency of medical school graduation (25-34 vs ≤ 15 years, OR = 0.50, 95% CI 0.31-0.81); practice setting (academic hospital vs outpatient group, OR = 0.58, 95% CI 0.37-0.88); prescribing experience (≥ 21 vs ≤10 patients, OR = 2.92, 95% CI 2.06-4.14); and timing of survey completion (wave 2 vs wave 1, OR = 0.47, 95% CI 0.34-0.66). Characteristics associated with practice changes included race (Asian vs White, OR = 0.62, 95% CI 0.38-0.99) and awareness of FDA's policy (yes vs no, OR = 14.07, 95% CI 7.93-24.96), in addition to sex (female vs male, OR = 1.99, 95% CI 1.31-3.01), recency of medical school graduation (≥  35 vs <  15 years: OR = 0.53, 95% CI 0.30-0.93), and timing of survey completion (wave 2 vs wave 1: OR = 0.58, 95% CI 0.34-0.99).

Conclusions: Although policy awareness was correlated with laboratory practice changes, fewer than half of physicians who were aware of the FDA's policy reported changing their practices.

Introduction: The safety and effectiveness of COVID-19 vaccination during pregnancy are commonly assessed using administrative health records, which may misclassify vaccine exposures.

Objective: The aim of this study was to compare the capture of COVID-19 vaccines in commercial insurance claims records and electronic health records (EHR) before and during pregnancy in a cohort in the United States and demonstrate the implications of observed misclassification on vaccine safety and effectiveness estimates.

Methods: Analysis of de-identified data from the Optum Labs Data Warehouse (OLDW) included pregnancies ending after December 11, 2020, and beginning before January 1, 2023, among people aged 15-54 years who were continuously enrolled in health insurance and had EHR data available. We compared the capture of COVID-19 vaccines from insurance claims and EHR databases to the combined and deduplicated doses using clinical procedure and drug codes, assessing records over time and by gestational age, dose number, vaccine manufacturer, and socioeconomic variables.

Results: For 29,663 eligible pregnancies, we identified 25,124 COVID-19 vaccine doses (87% in claims and 35% in EHR). About 44% had received one or more doses of a COVID-19 vaccine before or during pregnancy in the combined data, compared with 41% in the insurance claims data and 16% in the EHR data. Records of vaccination relative to the combined data improved in the claims data and declined in the EHR data over time. Vaccine manufacturer information from records without procedure codes in EHR data was unreliable, and a greater proportion of combined doses were recorded in EHR when the cohort was restricted to people with more frequent EHR encounters. The magnitudes of exposure misclassification observed, particularly in EHR data, could substantially bias estimates of vaccine effectiveness and safety.

Conclusions: Insurance claims data captured a larger proportion of COVID-19 vaccine doses than EHR data, and this proportion increased over time. Combining data from multiple administrative health records sources can improve COVID-19 vaccine measurement before and during pregnancy and may be important to reduce bias in studies of vaccine effects.

Background: Previous evidence suggests a potential protective effect of warfarin against cancer, compared to non-users. However, it may be prone to immortal time bias and residual confounding.

Objective: We aimed to examine the association between cancer and warfarin, compared with active comparator (direct oral anticoagulants).

Methods: We conducted studies using population-based databases from England and Hong Kong to investigate the association between warfarin and hazard of cancer using a new-user active-comparator cohort design. People with atrial fibrillation aged ≥ 18 years who had first received anticoagulant treatment during 01/01/2011-31/12/2019 were involved.

Results: No evidence supported the association between warfarin and hazard of overall cancer, compared with direct oral anticoagulants in both settings (England: hazard ratio [HR] = 1.03, 95% confidence interval [CI] 0.94-1.13; Hong Kong: HR = 0.89, 95% CI 0.79-1.01). A lower hazard of female breast (HR = 0.49, 95% CI 0.30-0.79), ovarian (HR = 0.07, 95% CI 0.01-0.58), and pancreatic (HR = 0.46, 95% CI 0.22-0.96) cancers and a higher hazard of kidney cancer (HR = 3.57, 95% CI 1.64-7.76) were found in Hong Kong, comparing warfarin with direct oral anticoagulants, but these were not replicated in England.

Conclusions: This study does not find a protective effect of warfarin against cancer versus direct oral anticoagulants. The risks of site-specific cancers including pancreatic, kidney, and sex-specific cancers between oral anticoagulants shown in the Hong Kong setting only may require further investigation in other independent datasets.

A focus group organised by the Drug Safety Research Unit (DSRU) International Working Group (IWG) on New Developments in Pharmacovigilance discussed current challenges and opportunities in pharmacovigilance (PV), emphasising the need for a multimodal approach in data analysis and accessibility of diverse data sources for drug safety surveillance. Nine participants, selected purposefully for their multisectoral expertise in PV, discussed the value of various data types, including data from clinical trials and real-world data (RWD), each offering distinct strengths and limitations. Key challenges identified included data standardisation, quality variability, technological barriers and ethical concerns, particularly with data derived from social media. Emerging tools such as knowledge graphs were highlighted for their potential to enhance data integration and signal detection, however further research is required. The group also addressed disparities in data access, with particular attention to regulatory restrictions, limited infrastructure in low-resource settings and restricted access to industry-held data. Proposed solutions included fostering greater data transparency, establishing secure data-sharing platforms and forming collaborative consortia to facilitate responsible and ethical data use. Overall, the discussion underscored the need for improved integration, access and methodological rigour to strengthen PV practices and enhance global drug safety monitoring.

Background and hypothesis: The anticholinergic burden is the cumulative effect of drugs with anticholinergic properties and is typically measured using one of several anticholinergic scales. We hypothesised that these scales may not fully capture all the relevant adverse drug reactions (ADRs). By accessing the French national pharmacovigilance database (FPVD) and focusing on drug classes known to induce anticholinergic ADRs, the objectives of the present study were to describe the reported ADRs, characterise the drugs involved, and examine the drugs' classification within anticholinergic scales.

Methods: Cases were extracted from the FPVD (1985-2024) when the suspected drug (i) had a high anticholinergic score, according to one or more of 22 anticholinergic burden scales, or (ii) belonged to the same class as the drug identified in (i). The anticholinergic ADRs investigated were confusion, glaucoma, tachycardia, urinary retention, constipation, intestinal obstruction and mydriasis.

Results: Of the 101,365 cases reported in the FPVD, regarding the selected drugs, 9629 (9.5%) involved at least one anticholinergic ADR investigated. Patients who experienced at least one anticholinergic ADR had a median age of 61 years (interquartile range: 38-79), and the majority were women (58%). Confusion was the most frequently reported anticholinergic ADR (4603 cases, of which 81% were classified as serious), followed by tachycardia (n = 1541 cases, 70% serious), and urinary retention (1061 cases, 75% serious). It is noteworthy that 98% of the 561 reported cases of intestinal obstruction were classified as serious. The drug classes with the highest number of reports were (by far) anxiolytics, antidepressants, and antipsychotics. Some drugs linked to anticholinergic ADRs in the FPVD were not present in (or were assigned a low score by) commonly used anticholinergic scales, such as the Anticholinergic Cognitive Burden.

Conclusions: Anticholinergic ADRs affect both older and younger adults. The existing scoring systems might not fully capture the range of medications involved in real-world anticholinergic-related events.

Background: The COVID-19 mass vaccination led to a substantial increase in spontaneous reports submitted to pharmacovigilance (PV) databases, potentially introducing masking effects that could conceal safety signals.

Objectives: To examine the masking effect of COVID-19 vaccines on disproportionality analyses and to compare two unmasking interventions in the Dutch (Lareb database) and Spanish (Farmacovigilancia Española, Datos de Reacciones Adversas, FEDRA) national PV databases: removal of all drug-event combinations (DEC) involving a COVID-19 vaccine versus excluding influential outliers DECs only.

Methods: The masking effect was explored retrospectively on the basis of the number of signals of disproportionate reporting (SDR). DECs involving a COVID-19 vaccine were excluded using crude and outlier techniques, and reporting odds ratios were recalculated. Subsets of important medical events (IME) were analysed in both databases.

Results: Both crude and influential outlier removal methods led to reductions in the number of reports, DECs and SDRs. Both in the Lareb database and FEDRA, crude removal excluded 2.1% of DECs, while the outlier method excluded 0.1%. Crude removal had a greater impact on SDRs, reducing them by 9.8% in the Lareb database and 3.9% in FEDRA, compared with 5.7% and 1.1% with the outlier method. In the Lareb database, 1301 SDRs (20 IME-related) were unmasked using crude removal, and 1942 (95 IME-related) with the outlier method. FEDRA showed 1453 and 1226 SDRs unmasked, including 41 and 70 IME-related.

Conclusions: COVID-19 vaccines caused substantial masking in both databases. Both strategies effectively revealed new SDRs, though their impact varied. The choice of approach should be tailored to the database context.

Background: Potential drug-drug interactions (pDDIs) are frequent in clinical care, particularly among older patients. Accurate identification and management of pDDIs are essential for patient safety. Prescribers often rely on interaction checkers (ICs) to screen for pDDIs. However, these tools may provide inconsistent recommendations, potentially leading to suboptimal clinical decisions.

Objective: This study aimed to develop a standardized approach for classifying and prioritizing pDDIs based on the clinical relevance of their management recommendations and to compare how these pDDIs are categorized across ICs.

Methods: A scale was developed through a structured iterative process to classify pDDIs into four management categories (high priority, intermediate priority, low priority, data unavailable), based on the management recommendations extracted from six ICs. This scale was applied to 218 real-world pDDIs identified from 1923 patients, and the agreement was primarily assessed using Gwet's AC1.

Main results: Overall agreement among the ICs was moderate (Gwet's AC1 = 0.44; 95% CI 0.39-0.50), with values ranging from 0.58 (0.51, 0.65) to 0.38 (0.31, 0.44) in leave-one-out analyses. The agreement was higher in binary analyses dichotomizing the scale into high- and intermediate-priority versus low-priority pDDIs (AC1 = 0.72; 95% CI 0.65-0.79), and in the classification of high-priority versus all other pDDIs (AC1 = 0.62; 95% CI 0.54-0.69).

Conclusion: This study developed and tested a structured approach to systematically compare pDDI management across ICs and prioritize clinically relevant interactions. Its application revealed a generally limited agreement between ICs, pointing to the need for harmonized approaches and further studies to support more consistent, evidence-aligned pDDI management.