Drug Safety最新文献

{"title":"Large-scale Empirical Identification of Candidate Comparators for Pharmacoepidemiological Studies.","authors":"Justin Bohn, James P Gilbert, Christopher Knoll, David M Kern, Patrick B Ryan","doi":"10.1007/s40264-025-01569-y","DOIUrl":"https://doi.org/10.1007/s40264-025-01569-y","url":null,"abstract":"<p><strong>Background and objective: </strong>The new user cohort design has emerged as a best practice for the estimation of drug effects from observational data. However, despite its advantages, this design requires the selection and evaluation of comparators for appropriateness, a process that can be challenging. The objective of this work was to introduce an empirical approach to rank candidate comparators in terms of their similarity to a target drug in high-dimensional covariate space.</p><p><strong>Methods: </strong>We generated new user cohorts for each RxNorm ingredient and Anatomic Therapeutic Chemical level 4 class in five administrative claims databases then extracted aggregated pre-treatment covariate data for each cohort across five clinically oriented domains. We formed all pairs of cohorts with ≥ 1000 patients and computed a scalar similarity score, defined as the average of cosine similarities computed within each domain, for each pair. We then generated ranked lists of candidate comparators for each cohort.</p><p><strong>Results: </strong>Across up to 1350 cohorts forming 922,761 comparisons, drugs that were more similar in the Anatomic Therapeutic Chemical hierarchy had higher cohort similarity scores. The most similar candidate comparators for each of six example drugs corresponded to alternative treatments used in the target drug's indication(s), and choosing the top-ranked comparator for randomly selected drugs tended to produce balance on most covariates. This approach also ranked highly those comparators chosen in high-quality published new user cohort design studies.</p><p><strong>Conclusion: </strong>Empirical comparator recommendations may serve as a useful aid to investigators and could ultimately enable the automated generation of new user cohort design-derived evidence, a process that has previously been limited to self-controlled designs.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aggregation of Adverse Event Terms for Signal Detection and Labeling in Clinical Trials.","authors":"Richard C Zink, Rebecca Lyzinski, Geoffrey Mann","doi":"10.1007/s40264-025-01518-9","DOIUrl":"10.1007/s40264-025-01518-9","url":null,"abstract":"<p><p>The Medical Dictionary for Regulatory Activities (MedDRA) was developed in the mid-to-late 1990s to address the shortcomings of other medical dictionaries used for coding adverse events. Since that time, MedDRA has become the required coding dictionary for major regulatory authorities involved with the International Council for Harmonisation. Due to the increased specificity and significant increase in terminology over time, several approaches have been developed to aggregate terms for the purposes of signal detection and labeling. We present the approaches taken and suggested to date to aggregate preferred terms into meaningful medical concepts. We discuss the pros and cons of different methods in which to group terms, and illustrate that analyses performed for MedDRA preferred terms can also be conducted using aggregated terms. However, some features of adverse events available at the preferred term level, such as severity and relationship to study therapy, require additional consideration for analysis. In the last 25 years, the pendulum for medical coding is swinging in the other direction. Faced with a deluge of preferred terms, users of MedDRA are developing new ways in which to collapse terms into medical concepts. The ability to identify safety concerns and communicate important data in drug labels effectively and consistently are at risk, particularly with the introduction of new aggregations.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"595-606"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Diverging Perspectives: Reasoning, Evidence, and Decision-Making in Drug Safety.","authors":"Tarek A Hammad, Simon Davies","doi":"10.1007/s40264-025-01537-6","DOIUrl":"10.1007/s40264-025-01537-6","url":null,"abstract":"<p><p>Decision making in drug safety is a complex and iterative process that requires the integration of diverse evidence sources, scientific reasoning, and clinical judgment. Diverging opinions among stakeholders-including pharmacovigilance professionals, regulatory authorities, clinical researchers, statisticians, and epidemiologists-often stem from differences in data interpretation, methodological approaches, and thresholds for concern or action. This paper examines the key sources of these divergences and presents a structured framework to enhance alignment in drug safety decision making. The proposed framework outlines three core dimensions: evidence assessment, interpretation, and action. It distinguishes between quantitative aspects, such as effect magnitude and measurement error, and qualitative considerations, including contextual interpretation and risk thresholds. The framework also underscores the importance of multidisciplinary collaboration, as safety professionals must actively engage with other scientific and regulatory stakeholders to ensure a comprehensive evaluation of the evidence. A fundamental challenge in pharmacovigilance is the need to communicate the complexities of drug safety assessment to a broader audience, including those who may not be familiar with the nuances of safety decision making. This paper aims to serve not only as a resource for new pharmacovigilance professionals, but also as a tool to facilitate clearer communication between disciplines. By adopting a structured approach and fostering open dialogue, drug safety professionals can enhance transparency and improve regulatory and clinical decision-making processes.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"587-593"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pregnancy and Infant Outcomes in Women with Multiple Sclerosis Exposed to Glatiramer Acetate Therapy: An Extended 4-Year Safety Update.","authors":"Sigal Kaplan, Andra Ghimpeteanu, Claudia Florentina Dragut","doi":"10.1007/s40264-025-01523-y","DOIUrl":"10.1007/s40264-025-01523-y","url":null,"abstract":"<p><strong>Background and objectives: </strong>While glatiramer acetate (GA) is generally considered safe during pregnancy and breastfeeding, long-term data, particularly for the 40 mg/mL dose, are limited. Previous research found GA exposure rates and pregnancy outcomes comparable to the general population. This study evaluates pregnancy, fetal, and infant outcomes following maternal exposure to GA 20 and 40 mg/mL to provide a cumulative four-year update.</p><p><strong>Methods: </strong>Post-marketing pregnancy data reported between April 1, 2019 to March 31, 2023 were searched in Teva's Global Safety database and supplemented with 1- and 12-month post-delivery questionnaires. Prospective pregnancy data, collected prior to known pregnancy outcomes or congenital malformations, were used to estimate pregnancy and infant outcomes for GA 20 and 40 mg/mL exposure. Rates of major congenital malformations (MCM) and other pregnancy and infant outcomes were estimated.</p><p><strong>Results: </strong>Among 3514 pregnancies, multiple sclerosis (MS) was the primary indication (62.4%), with most exposure to GA 40 mg/mL (72.2%), in the first trimester (94.9%). Of these, 2455 (69.9%) had known pregnancy outcomes. Of 1211 prospective pregnancies (1239 fetuses) with known outcomes, 1138 (91.8%) resulted in live births. Fetal loss occurred in 101 cases (8.2%), including spontaneous abortion (6.7%), elective termination (0.8%), ectopic pregnancy (0.3%), stillbirth (0.2%), and other (0.2%). The prevalence of MCM was 1.5% overall (95% CI, 0.9-2.4) and 1.9% during organogenesis (95% CI, 1.1-3.1), comparable to background rates. Minor congenital malformations were less frequent (0.7%). Prospective pregnancies with completed questionnaires (n = 539) reported preterm birth (9.8%), low/very low birth weight (7.3%), neonatal intensive care unit (NICU) admission (8.8%), and adverse events (17.4%). Infant growth remained within normal ranges. Of 384 women completing the 12-month questionnaire, 146 reported breastfeeding with GA (average 8 months). Among these, 14/125 (11.2%) respondents reported infant hospitalization. Growth parameters for 55 breastfed infants were within normal limits. Overall, pregnancy and infant outcomes were similar across GA doses.</p><p><strong>Discussion: </strong>Despite limitations of post-marketing data, this four-year study found no increased risk of adverse pregnancy, fetal, or infant outcomes associated with GA exposure. The MCM rates aligned with the general population, and infant outcomes during breastfeeding were within normal ranges. These findings support the safety of both 20 and 40 mg/mL GA during pregnancy and breastfeeding.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"697-713"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.","authors":"Yen Ling Koon, Yan Tung Lam, Hui Xing Tan, Desmond Hwee Chun Teo, Jing Wei Neo, Aaron Jun Yi Yap, Pei San Ang, Celine Ping Wei Loke, Mun Yee Tham, Siew Har Tan, Sally Leng Bee Soh, Belinda Qin Pei Foo, Zheng Jye Ling, James Luen Wei Yip, Sreemanee Raaj Dorajoo","doi":"10.1007/s40264-025-01525-w","DOIUrl":"10.1007/s40264-025-01525-w","url":null,"abstract":"<p><strong>Introduction: </strong>Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancements in various text processing tasks. However, the applicability of these LLMs in identifying related drug-adverse event (AE) pairs within clinical context may be limited by the prevalent use of non-standard sentence structures and grammar.</p><p><strong>Method: </strong>Nine transformer-based LLMs pre-trained on biomedical domain corpora are fine-tuned on annotated data (n = 5088) to classify drug-AE pairs in unstructured discharge summaries as causally related or unrelated. These LLMs are then validated on text segments from deidentified hospital discharge summaries from Singapore (n = 1647). To assess generalisability, the models are validated on annotated segments (n = 4418) from the Medical Information Mart for Intensive Care (MIMIC-III) database. Performance of LLMs in identifying related drug-AE pairs is then compared against a prior benchmark set by traditional machine learning models on the same data.</p><p><strong>Results: </strong>Using an LLM-Bidirectional long short-term memory (LLM-BiLSTM) architecture, transformer-based LLMs improve F1 score as compared to prior benchmark with BioM-ELECTRA-Large-BiLSTM showing an average F1 score improvement of 16.1% (increase from 0.64 to 0.74). Applying additional rules on the LLM-based predictions, like ignoring drug-AE pairs when the AE is a known indication of the drug, results in a further reduction in false positive rates with precision increases of up to 5.6% (0.04 increment).</p><p><strong>Conclusion: </strong>Transformer-based LLMs outperform traditional machine learning methods in identifying causally related drug-AE pairs embedded within unstructured discharge summaries. Nonetheless the improvement in performance with rules indicates that LLMs still possess some degree of imperfection for this causal relation detection task.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"667-677"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143467319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delphi Method Consensus on Statistical Analysis and Reporting Recommendations for Single-Arm Pregnancy Medication Safety Studies Investigating Pregnancy, Birth and Neonatal Health Outcomes: A Contribution from IMI-ConcePTION.","authors":"Jonathan L Richardson, Alan Moore, Michael Stellfeld, Yvonne Geissbühler, Ursula Winterfeld, Guillaume Favre, Christina Chambers, Evelin Beck, Marlies Onken, Katarina Dathe, Michael Ceulemans, Orna Diav-Citrin, Svetlana Shechtman, Alison M Oliver, Kenneth K Hodson, Dee-Dee Shiller, Amalia Alexe, Eugène P van Puijenbroek, David J Lewis, Laura M Yates","doi":"10.1007/s40264-025-01521-0","DOIUrl":"10.1007/s40264-025-01521-0","url":null,"abstract":"<p><strong>Background and objective: </strong>Standardised procedures for performing and reporting safety monitoring studies investigating medications use in pregnancy may help improve data quality and the speed of data generation. The objective of this study was to provide recommendations on the statistical analysis and reporting of single-arm pregnancy medication safety studies using primary source datasets.</p><p><strong>Methods: </strong>A Delphi consensus-setting protocol was used to acquire agreement on recommendations from experts with extensive knowledge and experience in conducting studies investigating medication safety in pregnancy. A series of recommendations, along with their scientific justifications and examples of how to calculate and describe exposure and outcome incidences, were critiqued and improved through a series of online Delphi review rounds. Agreement to inclusion scoring was assessed using a five-point Likert scale. Recommendations with a median Likert-scale score of at least 4, where ≥ 80% of the expert panel scored the recommendation at level 4 or higher, was used as the threshold for inclusion.</p><p><strong>Results: </strong>The Delphi consensus methodology produced a set of 30 recommendations spread over five themes. These included descriptions of (1) study sample, (2) medication exposure, (3) maternal outcomes, (4) pregnancy and birth outcomes, and (5) fetal and neonatal outcomes. Of the 30 recommendations, 19 were strongly advised while 11 were included for consideration where their implementation may be beneficial for supplementing data communication.</p><p><strong>Conclusion: </strong>Use of the finalised set of recommendations should be encouraged to help standardise published evidence around medication use in pregnancy.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"643-654"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacogenetic Testing in the Outpatient Setting in Switzerland: A Descriptive Study Using Swiss Claims Data.","authors":"Nina L Wittwer, Christoph R Meier, Carola A Huber, Henriette E Meyer Zu Schwabedissen, Samuel Allemann, Cornelia Schneider","doi":"10.1007/s40264-025-01522-z","DOIUrl":"10.1007/s40264-025-01522-z","url":null,"abstract":"<p><strong>Background: </strong>In Switzerland, consumers are exposed to drugs with pharmacogenetic (PGx) recommendations in 78% of cases. Pre-emptive PGx testing for seven drugs (abacavir, carbamazepine, 6-mercaptopurine, azathioprine, 5-fluorouracil, capecitabine, and irinotecan) has been covered by basic health insurance since 2017. PGx testing for other drugs is only covered if it is reactive and prescribed by a clinical pharmacologist. No data are yet available on the implementation of PGx testing in the outpatient setting.</p><p><strong>Aim: </strong>The objective of this study was to determine the prevalence of ambulatory PGx testing in the Swiss population, to characterize PGx-tested individuals, and to identify the most commonly used drugs before and after PGx testing.</p><p><strong>Methods: </strong>We assessed the prevalence of PGx testing in Switzerland and characterized individuals who underwent PGx testing between 2017 and 2021 using claims data from a large health insurance company.</p><p><strong>Results: </strong>Of 894,748 individuals registered for the entire study period, only 817 (0.09%) underwent PGx testing. Those who underwent PGx testing were more frequently female and claimed more drugs and PGx drugs than those who did not undergo PGx testing. The drugs used before and after PGx testing differed, and fewer drugs with reimbursement for pre-emptive PGx testing were included before PGx testing.</p><p><strong>Conclusion: </strong>In Switzerland, personalized pharmacotherapy has the potential to be improved, as only 0.09% of the studied population underwent PGx testing, despite 77.4% claiming PGx drugs.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"689-696"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel.","authors":"Leihong Wu, Hong Fang, Yanyan Qu, Joshua Xu, Weida Tong","doi":"10.1007/s40264-025-01520-1","DOIUrl":"10.1007/s40264-025-01520-1","url":null,"abstract":"<p><strong>Background: </strong>Drug adverse events (AEs) represent a significant public health concern. US Food and Drug Administration (FDA) drug labeling documents are an essential resource for studying drug safety such as assessing a drug's likelihood to cause certain organ toxicities; however, the manual extraction of AEs is labor-intensive, requires specialized expertise, and is challenging to maintain, due to frequent updates of the labeling documents.</p><p><strong>Objective: </strong>To automate the extraction of AE data from FDA drug labeling documents, we developed a workflow based on AskFDALabel, a large language model (LLM)-powered framework, and its demonstration in drug safety studies.</p><p><strong>Methods: </strong>This framework incorporates a retrieval-augmented generation (RAG) component based on FDALabel to enhance standard LLM inference. Key steps include (1) selection of a task-specific template, (2) FDALabel database querying, and (3) content preparation for LLM processing. We evaluated the performance of the framework in three benchmark experiments, including drug-induced liver injury (DILI) classification, drug-induced cardiotoxicity (DICT) classification, and AE term recognition.</p><p><strong>Results: </strong>AskFDALabel achieved F1-scores of 0.978 for DILI, 0.931 for DICT, and 0.911 for AE annotation, outperforming other traditional methods. It also provided cited labeling content and detailed explanations, facilitating manual verification.</p><p><strong>Conclusion: </strong>AskFDALabel exhibited high consistency with human AE annotation, particularly in classifying and profiling DILI and DICT. Thus, it can significantly enhance the efficiency and accuracy of AE annotation, with promising potential for advanced AE surveillance and drug safety research.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"655-665"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143467320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are Causal Statements Reported in Pharmacovigilance Disproportionality Analyses Using Individual Case Safety Reports Exaggerated in Related Citations? A Meta-epidemiological Study.","authors":"Claire Bernardeau, Bruno Revol, Francesco Salvo, Michele Fusaroli, Emanuel Raschi, Jean-Luc Cracowski, Matthieu Roustit, Charles Khouri","doi":"10.1007/s40264-025-01524-x","DOIUrl":"10.1007/s40264-025-01524-x","url":null,"abstract":"<p><strong>Background: </strong>Previous meta-epidemiological surveys have found considerable misinterpretation of results of disproportionality analyses. We aim to explore the relationship between the strength of causal statements used in title and abstract conclusions of pharmacovigilance disproportionality analyses and the strength of causal language used in citing studies.</p><p><strong>Methods: </strong>On March 30, 2022, we selected the 30 disproportionality studies with the highest Altmetric Attention Scores. For each article, we extracted all citing studies using the Dimension database (n = 1434). In parallel, two authors assessed the strength of causal statements in the title and abstract conclusions of source articles and in the paragraph of citing studies. Based on previous studies, the strength of causal language was quantified based on a four-level scale (1-appropriate interpretation; 2-ambiguous interpretation; 3-conditionally causal; 4-unconditionally causal). Discrepancies were solved by discussion until consensus among the team. We assessed the association between the strength of causal statements in source articles and citing studies, separately for the title and abstract conclusions, through multinomial regression models.</p><p><strong>Results: </strong>Overall, 27% (n = 8) of source studies used unconditionally causal statements in their title, 30% (n = 9) in their abstract conclusion, and 17% (n = 5) in both. Only 20% (n = 6) used appropriate statements in their title and in their abstract's conclusions. Among the 622 citing studies analyzed, 285 (45.8%) used unconditionally causal statements when referring to the findings from disproportionality analysis, and only 164 (26.4%) used appropriate language. Multinomial models found that the strength of causal statements in citing studies was positively associated with the strength of causal language used in abstract conclusions of source articles (Likelihood Ratio Test (LogLRT) p < 0.00001) but not in the titles. In particular, among studies citing source articles with appropriate interpretation, 30.2% (95% confidence interval [CI] 22.8-37.6) contained unconditionally causal statements in their abstract conclusions, versus 56.4% (95% CI 48.7-64.2) for studies citing source articles with unconditionally causal statements.</p><p><strong>Conclusions: </strong>Nearly half of the studies citing pharmacovigilance disproportionality analyses results used causal claims, particularly when the causal language used in the source article was stronger. There is a need for higher caution when writing, interpreting, and citing disproportionality studies.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"679-688"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence-Based Design of Prescription Medication Information: An Updated Scoping Review.","authors":"Andrea M Russell, Rebecca Lovett, Abigail Vogeley, Denise A Nunes, Carolyn McKelvie, Wayne Middleton, Michael Wolf","doi":"10.1007/s40264-025-01527-8","DOIUrl":"10.1007/s40264-025-01527-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Well-designed prescription medication information (PMI), defined as materials which communicate the essential information needed for a patient to safely and accurately self-administer a medication at or near the time of prescribing, is important for patient education. A previous review identifying best practices in the design of PMI was last completed using studies published through 2015.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this review was to present an updated description of studies comparing one or more types of PMI, including details of if or how patients were involved in PMI design, and to consolidate design elements associated with positive outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Four databases (Ovid, Embase, CINAHL, and Cochrane) were searched for studies comparing one or more types of PMI using a specified literature search with follow-up citation searching of included articles. Eligible studies were (1) conducted in English-speaking countries, (2) randomized controlled trials, and (3) published in 2016 or later. Consistent findings from at least two well-conducted studies were deemed 'strong' evidence and inconsistency in study findings or quality were deemed 'moderate' evidence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Thirty-two articles were included and most had some risk (n = 24) or high risk of bias (n = 4). Two-thirds of articles (n = 21) reported on the details of PMI development, and over half (n = 14) conducted formal pilot testing or obtained feedback from patients. Findings suggested benefits when patients were involved in PMI development. Twelve studies examined written medication information (e.g., leaflets), ten examined pharmacy-generated contained labelling (e.g., instructions printed on pill bottles), two examined supplemental information (e.g., medication regimen charts), and fourteen examined PMI delivered using technology-supported tools (e.g., text message instructions). The most prevalent assessed outcomes were knowledge (n = 19), behaviors (n = 17), attitudes/beliefs (n = 11), and clinical outcomes, such as blood pressure (n = 3). Several studies demonstrated positive outcomes when PMI was designed according to health literacy principles of plain language, typographic cues, actionable instructions, large font, and white space. Multiple trials of pictograms and illustrations alongside paired text and text messages to deliver PMI also had positive outcomes. Although there were several studies that examined interactive websites, audio, and video delivery of PMI, mixed findings resulted in moderate evidence. Novel methods of PMI delivery were identified: a plain language label for as-needed medications, strategic memory training, inclusion of patient photos and quotes, Quick Response codes, and electronic health record strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Overall, a high proportion of studies included patients in the development of PMI and focused on behavioral outcomes. How","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"607-641"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}