Drug Safety最新文献

Background: Adverse drug events (ADEs) are understudied in the ambulatory care setting. We aim to estimate the prevalence and characteristics of ADEs in outpatient care using electronic health records (EHRs).

Methods: This cross-sectional study included EHR data for patients who had an outpatient encounter at an academic medical center from 1 October 2018 through 31 December 2019. We developed a stratified sampling strategy based on a comprehensive set of 994 ADE-related International Classification of Disease (ICD-10) codes to identify clinical encounters and notes likely to contain ADEs. Within each ICD-10 likelihood group, clinical notes were randomly sampled and annotated for present or possible ADE-drug relationships and severity. The overall estimated population prevalence of ADEs presenting in the outpatient setting was calculated. The generalizability of the findings was assessed by comparing ICD-10 code frequencies against a large commercial database.

Results: The study included 3126 notes (unique patient encounters) from 2882 unique patients. Of these, 1383 patient encounters (44.2%) had a present or possible ADE documented (6308 mentions). Of the 6038 ADEs mentioned, 14.1% were hypersensitivity reactions, 1.1% were life-threatening, 22.4% were serious, and 60.4% were significant. Main causal agents included anti-infectives (19.3%), central nervous system agents (12.8%), and cardiovascular agents (11.5%). The overall prevalence of present ADEs mentioned in the clinical notes was estimated to be 1.97 per 100 patient encounters (or 2.52 per 100 patient encounters when possible ADEs are included).

Conclusions: This study identified the overall population prevalence per encounter of ADEs in the outpatient population by leveraging ICD-10 codes and investigating ADEs documented in clinical notes. Understanding the ADE characteristics in a large corpus of outpatient documentation advances pharmacovigilance knowledge, enhancing the detection, monitoring, and prevention of ADEs in ambulatory care.

Background: Our microbiome is established during infancy, a time important for later health and long-term effects. Proton pump inhibitors and antibiotics are regularly prescribed during pregnancy. Both drugs cause microbiome disturbance and have been associated with increased cancer risk in adults, but effects of these drugs on the growing foetus and infant remain understudied.

Aim: The aim of this study is to study the association between prenatal and early life proton pump inhibitor and antibiotics exposure and the risk of childhood cancer.

Methods: This study is a retrospective population-based cohort design, using registry data on all births (n = 722,372) in Sweden between 2006 and 2016, according to the STROBE checklist. For women who had multiple children in the timeframe of the study, only the first child during the time period was included in the cohort. Exposure was defined as either ≥ 1 proton pump inhibitor or antibiotics prescription during pregnancy, or during the first 2 years of life. Outcome was defined as cancer at any time during the follow-up or cancer after the age of 2 years for early life exposure. Multivariable Cox proportional hazard models were used to calculate hazard ratios.

Results: In total, 1091 (0.2%) children were diagnosed with malignant cancer during the follow-up. Prenatal exposure to proton pump inhibitors and antibiotics were not associated with an increased risk of cancer. Regarding early life exposure, proton pump inhibitors were associated with an increased risk of cancer at age two or older (adjusted hazard ratio [aHR] 3.68, 95% confidence interval [CI] 2.24-6.06).

Conclusions: We did not find evidence that prenatal proton pump inhibitors and antibiotics were associated with overall childhood cancer. However, proton pump inhibitors during early life were associated with an increased risk of childhood cancer, but indication on drug use was not available and confounding by indication may be present.

Introduction: The Reporting Recommendation Intended for Pharmaceutical Risk Minimisation Evaluation Studies (RIMES) checklist is endorsed by the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) and is tailored for studies assessing Risk Minimisation Measures and Risk Evaluation and Mitigation Strategy (RMM/REMS) effectiveness; however, its awareness and usage remain unknown. We evaluated the implementation of the RIMES checklist in RMM/REMS effectiveness studies registered in the EUPAS register during 01 December 2017- 01 January 2024. Furthermore, the awareness and utilization of the RIMES checklist among researchers conducting RMM/REMS effectiveness studies was assessed.

Methods: The European Union Post-Authorisation Study (EUPAS) Register was reviewed to identify studies conducted in at least one European country within the specified timeframe. Data were extracted from the online EUPAS registrations, including uploaded study documents. Additionally, a survey was distributed through at International Society of Pharmacoepidemiology (ISPE) to assess awareness and checklist utilisation among researchers. Findings from both the review and the survey were reported descriptively.

Results: Among 44 studies included in the review, cross-sectional questionnaire-based surveys (n = 28, 64%), and retrospective cohort studies using secondary data (n = 13, 30%) were frequently used study designs. Oncology (n = 12, 27%) and pregnancy-related conditions (n = 7, 16%) were frequently reported therapeutic areas. Most studies were required by regulators and typically evaluated the additional RMM/REMS. The awareness and usage of the RIMES checklist was low, while the ENCePP checklist was used frequently. Some researchers considered the ENCePP checklist adequate for RMM/REMS studies, while few advocated for the RIMES checklist.

Conclusion: The awareness and utilisation of the specific RIMES checklist designed for studies evaluating RMM/REMS was limited, indicating a need for improving awareness and utilisation of RIMES and harmonisation of existing guidance and frameworks for RMM/REMS effectiveness studies.

Drug-induced cognitive impairment (DICI) is a well-established, yet under-recognised, complication of many types of pharmacological treatment. While there is a large body of scientific literature on DICI, most papers are about drug-induced dementia in the elderly and one specific drug class. However, DICI also comprises subclinical symptoms, domain-specific forms of cognitive impairment as well as mild cognitive impairment (MCI), and delirium. Even mild forms of DICI, if not recognised as such, can have deleterious and life-long consequences. In addition, DICI also occurs in younger adults and in children, and has been reported with many different drug classes. The aim of this review is to raise awareness of DICI by providing an overview on the type(s) and symptoms of observed DICI and the suspected underlying mechanism(s) for various drug classes: antiseizure medications, antidepressants, antiparkinsonian drugs, antipsychotics, lithium, benzodiazepines/Z-drugs, opioids, first-generation antihistamines, drugs for urinary incontinence, proton pump inhibitors, glucocorticoids, NSAIDs, statins, antihypertensives, and chemotherapeutic agents.

Background: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.

Objective: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.

Methods: A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts.

Results: Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice.

Conclusions: Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.

Introduction: Individual case reports are essential to identify and assess previously unknown adverse effects of medicines. On these reports, information on adverse events (AEs) and drugs are encoded in hierarchical terminologies. Encoding differences may hinder the retrieval and analysis of clinically related reports relevant to a topic of interest. Recent studies have explored the use of data-driven semantic vector representations to support analysis of pharmacovigilance data.

Objective: This study aims to evaluate the stability and clinical relatedness of vigiVec, a semantic vector representation for codes of AEs and drugs.

Methods: vigiVec is a published adaptation to pharmacovigilance of the publicly available Word2Vec model, applied to structured data instead of free text. It provides vector representations for MedDRA^® Preferred Terms and WHODrug Global active ingredients, learned from reporting patterns in VigiBase, the WHO global database of adverse event reports for medicines and vaccines. For this study, a 20-dimensional Skip-gram architecture with window size 250 was used. Our evaluation focused on nearest neighbors identified by the cosine similarity of vigiVec vector representations. Clinical relatedness was measured through term intruder detection, whereby a medical doctor was tasked to identify a randomly selected term-the intruder-included among the four nearest neighbors to a specific AE or drug. Stability was measured as the average overlap in the ten nearest neighbors for each AE or drug, in repeated fittings of vigiVec.

Results: Among the ten nearest neighbors, 1.8 AEs on average belonged to the same MedDRA High Level Term (HLT; e.g., coagulopathies), and 1.3 drugs belonged to the same Anatomical Therapeutic Chemical level 3 (ATC-3; e.g., opioids). In the intruder detection task, when neighbors and intruders were both chosen from the same HLT, the intruder detection rate was 46%. When selected from different HLTs, it was 79%. By random chance, we should expect 20% (1 in 5). Corresponding rates for drugs were 42% in same ATC-3 and 65% in different ATC-3. The stability of nearest neighbors was 80% for AEs and 64% for drugs.

Conclusion: Nearest neighbors identified with vigiVec are stable and show high level of clinical relatedness. They are often from different parts of the existing hierarchies and complement these.

Background: Proton pump inhibitors (PPIs) are among the most popular drugs worldwide. Yet, there are concerns on long-term safety and poor adherence to prescription guidelines. Off-label use in children and increasing maintenance use in older adults may be particularly worrisome.

Objectives: To assess differences in PPI use by age, sex calendar year and PPI type, and to explore potential underlying indications (ulcerogenic drugs, and indications) in Sweden.

Methods: Proton pump inhibitor drug utilisation study based on the Swedish nationwide prescribed drug (2006-2023) and patient registries (2006-2022).

Results: Proton pump inhibitors were used by 14.4% (women) and 10.5% (men) of adults; and 1.0-1.5% of children and adolescents (aged < 20 years). Proton pump inhibitor use was higher in women in all age-groups except small children (aged < 5 years). Proton pump inhibitor use has increased in all age groups, especially in young children (aged < 10 years) and the oldest groups (aged > 65 years). Proton pump inhibitor users aged > 85 years filled most prescriptions with an annual average of 9.5 (men), 11.6 (women) prescriptions. Most prescriptions were for omeprazole and esomeprazole: 63.7% and 23.5% in adults; 23.5% and 44.7% in children (2023). Prescriptions for other drugs for peptic ulcers/reflux became rare, with 99% of prescriptions in this category being PPIs by 2023. Gastro-intestinal diagnoses were predominantly recorded in men, became less prevalent and only explained part of PPI use, while ulcerogenic drugs were common (particularly in women), suggesting PPIs are regularly used for gastroprotection.

Conclusion: Proton pump inhibitor use has doubled in children and increased 50% in adults over the study period, in both sexes, while recorded gastrointestinal indications decreased. Alternative therapies were rarely prescribed in Sweden.

Introduction: Pharmacovigilance signal report (PVSR) documents contain valuable condensed information published by drug monitoring organizations, typically in a free-text format. They provide initial insights into potential links between drugs and harmful effects. Still, their unstructured format prevents this valuable information from being integrated into data-processing pipelines (e.g., to support either the investigation of drug safety signals or decision-making in the clinical context).

Objective: OpenPVSignal is a data model designed specifically to publish PVSRs via a computationally exploitable format, compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles to promote ease of access and reusability of these valuable data.

Methods: This paper outlines the procedure for converting pharmacovigilance signals published by the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model, resulting in a Knowledge Graph (KG). It details each step of the process, including the technical validation by KG engineers and the qualitative verification by medical and pharmacovigilance experts, leading to the finalized KG.

Results: A total of 101 PVSRs from 2011 to 2019 were incorporated into the openly available KG.

Conclusion: The presented KG could be useful in various data-processing pipelines, including systems that support drug safety activities.