Drug Safety最新文献

Background: Functional active safety surveillance is essential for post-authorization assessment and life-cycle safety evidence generation of vaccines and medicines. Data collected through active surveillance methods are routinely used for post-approval surveillance of novel vaccines and medicines. Implementing these methods in low- and middle-income countries remains challenging.

Objective: This systematic review identified and assessed existing active safety surveillance in low- and middle-income countries, including key features, strengths, and limitations.

Methods: A search of EMBASE and PubMed Google Scholar databases was conducted. The protocol was registered with PROSPERO and adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Findings were synthesized narratively and categorized by surveillance systems characteristics.

Results: Of 13,027 records identified, 423 publications met inclusion criteria. Articles spanned 96 low- and middle-income countries, with India (96), China (57), Brazil (30), Iran (26), Ethiopia (21), Indonesia (20), Uganda (18), Kenya (17), and Ghana (16), most represented. The majority focused on vaccines (211). A total of 127 articles utilized mobile technologies for follow-up, online data collection, and/or adverse event reporting. Fifteen percent of vaccine surveillance systems described in articles demonstrated flexibility to incorporate new vaccines, 34% reported multi-sectoral collaborations, and 10% involved multiple countries. Gaps identified include small sample sizes, lack of sustainability, limited flexible surveillance, staffing challenges, and limited use of standard case definitions and digital technologies.

Conclusions: Active safety surveillance in low- and middle-income countries has made progress but still faces challenges. The capture and management of safety data through harmonized digital tools that promote consistency in recording and reporting and cross-country collaborations are crucial in further strengthening active safety surveillance in low- and middle-income countries.

Background: Databases for safety monitoring of medicinal products contain records of a huge number of pairings of drugs and adverse events (AEs). Existing disproportionality methods for safety monitoring in such databases estimate background rates of AE occurrence in ways that may be susceptible to masking effects that can hinder signal detection, particularly in the context of large overall counts of AE or drug occurrence in the data.

Objectives: To develop a new statistical model for determining the background rate against which individual drug-AE pairs are to be evaluated, which is robust against masking effects, and to incorporate this into an algorithm which simultaneously estimates background rates and detects drug-AE pair counts that deviate significantly from these rates.

Methods: We constructed a hierarchical Bayesian model for background rates, and background rate samples were drawn from the model parameters using an iterative Markov Chain Monte Carlo (MCMC) method. At each iteration, any counts whose probability is low given current background rate estimation were removed from the computation that sampled the next set of background rates. The algorithm, called Markov Chain Signal Generation (MCSG), was implemented using a combination of Python and the probabilistic programming language Stan.

Results: The MCSG algorithm outperformed routinely used quantitative approaches for signal detection on both synthetic data designed to include a drug-AE pair with very strong masking effects and a reference set featuring 69 unique active substances and 792 unique AEs. On a synthetic dataset where selected pairs occurred at rates deviating from a constant background, MCSG accurately identified these pairs in the presence of strong masking signals. On a subset of some real data from the FDA Adverse Event Report System (FAERS), it effectively identified a reference set of positive and negative controls and was able to identify drug-AE pairs suggested in the literature.

Conclusion: We have demonstrated a new approach to signal generation, which avoids the confounding effect of masking more effectively than currently used methods. The algorithm is best used in a setting of multiple drug-AE pairs, the majority of which are expected to have counts at background rate, although with substantial datasets the algorithm can take minutes or hours to run. It is therefore particularly suitable for infrequent, large-scale analysis (for example, quarterly analysis of the entirety of a pharmacovigilance database).

'Stems', which mark pharmacological relationships between substances, form the backbone of the International Nonproprietary Name (INN) system, developed by the WHO in the 1950s. In this paper, we propose using the INN stems to enhance pharmacovigilance signal detection. After analysis of historical cases and current pharmacovigilance practices, we discuss how stem-based classification could facilitate understanding of the adverse-effects profile of each stem, to be used as a benchmark for early identification of adverse drug reactions that deviate from expected class effects, in other words signals associated with newly marketed medicines or different uses of well-known medicines. We propose a potential framework for integrating stem-based analysis into existing pharmacovigilance databases, supplemented by artificial intelligence approaches, such as machine learning. While acknowledging limitations, such as stem variability and reporting bias, we suggest that this approach offers potential advantages for regulatory authorities and healthcare professionals in post-marketing surveillance. Implementation of stem-based post-marketing surveillance could enhance signal-detection efficiency and contribute to improved patient safety through earlier identification of unexpected adverse effects and adverse reactions.

Background: Medication-related adverse events in primary care are a leading cause of hospital admissions and mortality, commonly resulting from medication errors. Previous reviews have assessed interventions broadly across healthcare settings, but few have focused specifically on interventions targeting medication errors in primary care.

Objective: To evaluate the effectiveness of professional, organisational, and structural interventions in primary care settings in reducing medication-related hospital admissions, emergency department (ED) visits, and mortality.

Methods: We conducted a systematic review using the Cochrane methodology of systematic reviews and PRISMA guidelines for reporting. A comprehensive search of CENTRAL, MEDLINE, Embase, CINAHL, and trial registries up to October 2024 was undertaken. Randomised controlled trials conducted in primary care that assessed the impact of interventions on hospital admissions, ED visits, and mortality were included. Cochrane Risk of bias assessments and random-effects meta-analyses were performed.

Results: Interventions were classified according to the Cochrane Effective Practice and Organisation of Care criteria into Professional, Organisational and Structural. Sixty-two studies met the inclusion criteria. Professional interventions, including educational training and clinical decision tools, showed little to no effect on primary outcomes (risk ratio [RR] 1.01, 95% confidence interval [CI] 0.94-7.00 for hospital admissions; RR 1.00, 95% CI 0.98-1.02 for mortality; very-low to low certainty evidence). Organisational interventions, such as pharmacist-led medication reviews and multidisciplinary care models reduced the number of hospital admissions (RR 0.81, 95% CI 0.70-0.95; low-certainty evidence), but had uncertain effects on ED visits and mortality. Structural interventions, such as system-level support and quality monitoring, showed a reduction in hospital admissions (RR 0.90, 95% CI 0.83-0.97; moderate-certainty evidence), but evidence for other outcomes showed limited or very-low certainty.

Conclusion: Organisational and structural interventions in primary care may reduce medication-related hospital admissions and may help inform clinical practice through implementation of multidisciplinary care models and system-level quality monitoring approaches. However, the overall certainty of evidence is low to very low, highlighting the need for high-quality trials to better inform clinical practice and policy.

Introduction: Disproportionality analysis, finding associations in the co-reporting of drugs and events, is widely used in pharmacovigilance to detect potential safety signals of adverse drug reactions. However, inherent biases and unique data features often cause disproportionality to diverge from causation, and a comprehensive framework to address these issues is lacking.

Objective: We showcase how directed acyclic graphs (DAGs) can enhance disproportionality analysis-related inferences, better qualifying its limitations and catalysing its inclusion in the broader evidence landscape.

Methods: We introduce a DAG-based causal framework to systematically document and address biases in disproportionality analyses (e.g., confounding, colliders, measurement and reporting biases). We illustrate its application to case studies from the Food & Drug Administration (FDA) Adverse Event Reporting System-using the Information Component as a disproportionality metric and restriction as conditioning.

Results: Directed acyclic graphs facilitate the formalisation of existing knowledge and causal assumptions, optimise the design of disproportionality analysis to mitigate biases-thereby enhancing sensitivity and specificity-improve transparency, better enable the formulation of critiques, highlight limitations of disproportionality and guide follow-up studies to address residual confounding and broader evidence synthesis.

Conclusion: Using DAGs to map and mitigate biases requires caution and does not allow to obtain definitive answers to causal questions. Still, it results in more reliable and knowledge-based safety signals, reducing and mapping the gap between what we find (association) and what we look for (causation). Additional research should further tailor DAGs to pharmacovigilance challenges, map the generative mechanisms of pharmacovigilance data, and better integrate disproportionality analysis results into evidence-synthesis workflows.

Introduction: Sexual side effects of serotonergic drugs are well documented. However, observational evidence comparing specific antidepressants remains sparse.

Objective: The aim of this study was to compare the risk of developing healthcare-recorded sexual dysfunction among male patients after initiating treatment with citalopram, sertraline, venlafaxine, or mirtazapine.

Methods: We conducted a new-user, active comparator cohort study of adult males in Denmark who started treatment with any of the study drugs between 2001 and 2015, allowing multiple cohort entries per person. Data from Danish national healthcare registers included 170,580 new treatment episodes of citalopram, 67,721 of mirtazapine, 51,984 of sertraline, and 19,820 of venlafaxine. A separate analysis examined patients with prior depression-related hospital visits. Results were reported as incidence rates as well as unmatched and propensity score-matched hazard ratios.

Results: In our primary analysis with 1-year follow-up and washout periods, incidence rates ranged from 18.80 (95% CI 17.60-20.00) to 28.5 (95% CI 26.00-31.30) per 1000 person-years. Venlafaxine was associated with the highest risk of healthcare-recorded sexual dysfunction compared with citalopram (hazard ratio [HR] 1.27; 95% CI 1.02-1.46), which was particularly pronounced among those with previous hospital contact for depression (HR 1.93; 95% CI 1.14-1.27). No significant difference was observed for sertraline (HR 0.99; 95% CI 0.90-1.09), while mirtazapine demonstrated modest evidence of a lower risk relative to citalopram (HR 0.87; 95% CI 0.81-0.93). Findings remained consistent across all analyses.

Conclusions: The risk of developing sexual dysfunction varies across antidepressant treatment. Healthcare providers should discuss the potential for sexual side effects, particularly when multiple treatment options are available.

Background: Over the years, several observational studies and case reports have been published hypothesizing a potential association between the use of proton pump inhibitors (PPIs) and sexual dysfunctions in both male and female patients.

Objectives: We aimed to investigate the potential association between PPI use and sexual dysfunction onset using VigiBase, the World Health Organization international pharmacovigilance database.

Methods: All individual case safety reports of sexual dysfunctions containing PPIs (i.e. omeprazole, lansoprazole, rabeprazole, pantoprazole and esomeprazole) as suspected or interacting drugs until 4 September, 2024 were selected from VigiBase using the Standardised MedDRA Query "Sexual dysfunction". A descriptive analysis of the selected individual case safety reports was carried out. Potentially new safety signals were identified through a disproportionality analysis and calculated as a reporting odds ratio (ROR) along with a 95% confidence interval (CI), which were adjusted via Bonferroni correction for multiple testing. To account for age-related effects on sexual functions, a subgroup analysis was carried out by restricting the study population only to patients aged from 18 to 64 years.

Results: A total of 420,598 individual case safety reports concerning PPIs were collected in VigiBase during the study period. Of these, 841 containing at least one Preferred Term included in the "Sexual dysfunction" Standardised MedDRA Query reporting information on sex were retrieved. Overall, disproportionate reporting for omeprazole concerning erectile dysfunction (adjusted ROR, 1.76; 95% CI 1.54-2.01) was observed, while two statistically significant adjusted RORs for esomeprazole, i.e. genital discomfort (ROR, 3.66; 95% CI 1.34-10.04) and oestrogen deficiency (ROR, 3.80; 95% CI 1.03-13.99) in female patients were found. The subgroup analysis confirmed the statistically significant disproportionate reporting of erectile dysfunction for omeprazole (adjusted ROR: 1.80; 95% CI 1.49-2.16), and generated new potential safety signals including an omeprazole-induced libido decrease (adjusted ROR, 1.49; 95% CI 1.05-2.12) and esomeprazole-induced hypogonadism (adjusted ROR, 5.22; 95% CI 1.22-22.34) in male individuals, and omeprazole-induced genital discomfort (adjusted ROR, 3.55; 95% CI 1.13-11.09) in female individuals.

Conclusions: Findings of this study suggest the presence of safety signals of PPI-induced sexual dysfunctions, such as erectile dysfunction, genital discomfort and oestrogen deficiency. However, further observational studies are required to validate and further characterise these potential safety signals.

Background: Sulfonylureas (SU) are widely used for diabetes management in older adults but can cause hypoglycemia, which may be worsened by drug interactions. We applied high-throughput data mining to identify medications that could increase hypoglycemia risk when taken with SU.

Methods: Using Medicare, MarketScan, and Optum Clinformatics (2003-2022), we identified patients aged ≥ 65 years who experienced a severe hypoglycemic event after at least 90 days on SU. We evaluated all medications dispensed in the 90 days before the event using a case-crossover (CCO) design. We adjusted for time-varying confounding and direct effect of the evaluated medications (precipitant) using a case-case time-control (CCTC) approach and metformin as control. We computed odds ratios (ORs) for its association with hypoglycemia. The false discovery rate (FDR) was controlled at 0.05 to adjust for multiple testing. To reduce confounding from other diabetes medications, we analyzed non-diabetes and diabetes medications separately.

Results: Among 1607 candidate drugs received before experiencing hypoglycemia, 86 non-diabetes medications showed a CCO OR ≥ 1.00. With metformin as control, sulfamethoxazole/trimethoprim (CCTC OR 1.76, p < 0.01, FDR q < 0.01) and metronidazole (CCTC OR 2.17, p < 0.01, FDR q = 0.04) were associated with severe hypoglycemia. Among 10 diabetes medications, insulin showed increased association (CCO OR 1.22, p < 0.01); however, once adjusted for the drug's direct effects, CCTC OR was 1.03 (p = 0.47, FDR q = 0.47).

Conclusions: Using a high-throughput data mining approach, we identified two antibiotics (sulfamethoxazole/trimethoprim and metronidazole) that may increase hypoglycemia risk in older adults on sulfonylureas. Given the exploratory nature of this study, these findings warrant further investigation.

Background: Incretin-based drugs, namely glucagon-like peptide 1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase 4 (DPP-4) inhibitors, may have neuroprotective effects. Thus, we assessed whether these drugs are associated with a decreased risk of dementia among patients with type 2 diabetes.

Methods: Using the Clinical Practice Research Datalink from the UK, we formed two new user cohorts of patients at least 50 years of age with type 2 diabetes starting incretin-based drugs or sulfonylureas between 2007 and 2021. Hazard ratios (HRs) and 95% confidence intervals (CIs) for dementia were estimated separately for GLP-1 RAs and DPP-4 inhibitors using Cox proportional hazards models with propensity score fine-stratification weighting and inverse probability of censoring weights.

Results: Among 275,144 initiators of DPP-4 inhibitors or sulfonylureas, followed for 750,846 person-years, DPP-4 inhibitors were associated with a reduced dementia risk compared with sulfonylureas (4.4 vs. 5.7 events per 1000 person-years; HR 0.77, 95% CI 0.71-0.85). HRs decreased with increasing cumulative duration of use and dose. Similar associations were observed across dementia subtypes and individual DPP-4 inhibitors molecules. Among 181,215 initiators of GLP-1 RAs or sulfonylureas, followed for 530,415 person-years, GLP-1 RAs were associated with a similar reduction in dementia risk compared with sulfonylureas, although with high uncertainty (2.3 vs. 3.1 events per 1000 person-years; HR 0.74, 95% CI 0.46-1.18). The magnitude of the association increased with cumulative duration of use and dose but with high uncertainty.

Conclusions: In this population-based study, DPP-4 inhibitors, and possibly GLP-1 RAs, were associated with a reduced dementia risk compared with sulfonylureas.