1
|
Yang Y, Wei S, Tian H, Cheng J, Zhong Y, Zhong X, Huang D, Jiang C, Ke X. Adverse event profile of memantine and donepezil combination therapy: a real-world pharmacovigilance analysis based on FDA adverse event reporting system (FAERS) data from 2004 to 2023. Front Pharmacol 2024; 15:1439115. [PMID: 39101151 PMCID: PMC11294921 DOI: 10.3389/fphar.2024.1439115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 06/24/2024] [Indexed: 08/06/2024] Open
Abstract
Background Donepezil in combination with memantine is a widely used clinical therapy for moderate to severe dementia. However, real-world population data on the long-term safety of donepezil in combination with memantine are incomplete and variable. Therefore, the aim of this study was to analyze the adverse events (AEs) of donepezil in combination with memantine according to US Food and Drug Administration Adverse Event Reporting System (FAERS) data to provide evidence for the safety monitoring of this therapy. Methods We retrospectively analyzed reports of AEs associated with the combination of donepezil and memantine from 2004 to 2023 extracted from the FAERS database. Whether there was a significant association between donepezil and memantine combination therapy and AEs was assessed using four disproportionality analysis methods, namely, the reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, and multi-item gamma Poisson shrinker methods. To further investigate potential safety issues, we also analyzed differences and similarities in the time of onset and incidence of AEs stratified by sex and differences and similarities in the incidence of AEs stratified by age. Results Of the 2,400 adverse drug reaction (ADR) reports in which the combination of donepezil and memantine was the primary suspected drug, most of the affected patients were female (54.96%) and older than 65 years of age (79.08%). We identified 22 different system organ classes covering 100 AEs, including some common AEs such as dizziness and electrocardiogram PR prolongation; fall, pleurothotonus and myoclonus were AEs that were not listed on the drug label. Moreover, we obtained 88 reports of AEs in men and 100 reports of AEs in women; somnolence was a common AE in both men and women and was more common in women, whereas pleurothotonus was a more common AE in men. In addition, we analyzed 12 AEs in patients younger than 18 years, 16 in patients between 18 and 65 years, and 113 in patients older than 65 years. The three age groups had distinctive AEs, but lethargy was the common AE among all age groups. Finally, the median time to AE onset was 19 days in all cases. In both men and women, most AEs occurred within a month of starting donepezil plus memantine, but some continued after a year of treatment. Conclusion Our study identified potential and new AEs of donepezil in combination with memantine; some of these AEs were the same as in the specification, and some of the AE signals were not shown in the specification. In addition, there were sex and age differences in some of the AEs. Therefore, our findings may provide valuable insights for further studies on the safety of donepezil and memantine combination therapy, which are expected to contribute to the safe use of this therapy in clinical practice.
Collapse
Affiliation(s)
- Yihan Yang
- The Institution of Rehabilitation Industry, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Sheng Wei
- Department of General Practice, The Second Affiliated Hospital of Wannan Medical College, Anhui, China
| | - Huan Tian
- Department of Rehabilitation Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
- School of Health and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jing Cheng
- The First Clinical Medical College, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Yue Zhong
- Department of Rehabilitation Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaoling Zhong
- Guangdong Provincial Hospital of Chinese Medicine, The Second Clinical School of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Dunbing Huang
- Department of Rehabilitation Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Cai Jiang
- Rehabilitation Medicine Center, Fujian Provincial Hospital, Fuzhou, China
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Xiaohua Ke
- Department of Rehabilitation Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China
| |
Collapse
|
2
|
Reyes C, Newby D, Raventós B, Verhamme K, Mosseveld M, Prieto-Alhambra D, Burn E, Duarte-Salles T. Trends of use and characterisation of anti-dementia drugs users: a large multinational-network population-based study. Age Ageing 2024; 53:afae106. [PMID: 38783756 PMCID: PMC11116820 DOI: 10.1093/ageing/afae106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/20/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. SETTING Primary care data (country/database) from the UK/CPRD-GOLD (2007-20), Spain/SIDIAP (2010-20) and the Netherlands/IPCI (2008-20), standardised to a common data model. METHODS Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). RESULTS We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI.In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891-32,862)) to 2010 (17,793 (17,083-18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021-25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199-52,855)) to 2020 (14,571 (14,109-15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967-29,031)) to 2020 (4763 (4176-5409)). Subjects aged ≥65-79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. CONCLUSIONS Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.
Collapse
Affiliation(s)
- Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Sardenya Primary Health Care Center, EAP Sardenya- Research Institute Sant Pau (EAP Sardenya-IR Sant Pau), Barcelona, Spain
| | - Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, UK
| | - Berta Raventós
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Katia Verhamme
- Department of Medical Informatics, Erasmus Medical Center University, Rotterdam, Netherlands
| | - Mees Mosseveld
- Department of Medical Informatics, Erasmus Medical Center University, Rotterdam, Netherlands
| | - Daniel Prieto-Alhambra
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, UK
- Department of Medical Informatics, Erasmus Medical Center University, Rotterdam, Netherlands
| | - Edward Burn
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, UK
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Department of Medical Informatics, Erasmus Medical Center University, Rotterdam, Netherlands
| |
Collapse
|
3
|
Perets O, Stagno E, Yehuda EB, McNichol M, Anthony Celi L, Rappoport N, Dorotic M. Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.09.24305594. [PMID: 38680842 PMCID: PMC11046491 DOI: 10.1101/2024.04.09.24305594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Objectives 1.1Biases inherent in electronic health records (EHRs), and therefore in medical artificial intelligence (AI) models may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature. Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases are further exacerbated, resulting in systems that perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs. Methods 1.2We queried PubMed and Web of Science on January 19th, 2023, for peer-reviewed sources in English, published between 2016 and 2023, using the PRISMA approach to stepwise scoping of the literature. To select the papers that empirically analyze bias in EHR, from the initial yield of 430 papers, 27 duplicates were removed, and 403 studies were screened for eligibility. 196 articles were removed after the title and abstract screening, and 96 articles were excluded after the full-text review resulting in a final selection of 116 articles. Results 1.3Systematic categorizations of diverse sources of bias are scarce in the literature, while the effects of separate studies are often convoluted and methodologically contestable. Our categorization of published empirical evidence identified the six main sources of bias: a) bias arising from past clinical trials; b) data-related biases arising from missing, incomplete information or poor labeling of data; human-related bias induced by c) implicit clinician bias, d) referral and admission bias; e) diagnosis or risk disparities bias and finally, (f) biases in machinery and algorithms. Conclusions 1.4Machine learning and data-driven solutions can potentially transform healthcare delivery, but not without limitations. The core inputs in the systems (data and human factors) currently contain several sources of bias that are poorly documented and analyzed for remedies. The current evidence heavily focuses on data-related biases, while other sources are less often analyzed or anecdotal. However, these different sources of biases add to one another exponentially. Therefore, to understand the issues holistically we need to explore these diverse sources of bias. While racial biases in EHR have been often documented, other sources of biases have been less frequently investigated and documented (e.g. gender-related biases, sexual orientation discrimination, socially induced biases, and implicit, often unconscious, human-related cognitive biases). Moreover, some existing studies lack causal evidence, illustrating the different prevalences of disease across groups, which does not per se prove the causality. Our review shows that data-, human- and machine biases are prevalent in healthcare and they significantly impact healthcare outcomes and judgments and exacerbate disparities and differential treatment. Understanding how diverse biases affect AI systems and recommendations is critical. We suggest that researchers and medical personnel should develop safeguards and adopt data-driven solutions with a "bias-in-mind" approach. More empirical evidence is needed to tease out the effects of different sources of bias on health outcomes.
Collapse
|
4
|
Watson J, Green MA, Giebel C, Darlington-Pollock F, Akpan A. Social and spatial inequalities in healthcare use among people living with dementia in England (2002-2016). Aging Ment Health 2023; 27:1476-1487. [PMID: 35959941 PMCID: PMC9612936 DOI: 10.1080/13607863.2022.2107176] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/18/2022] [Indexed: 11/01/2022]
Abstract
OBJECTIVES Healthcare services for people living with dementia (PLWD) are stretched, and government promises of increased funding remain undelivered. With the UK dementia population to surpass 1 million by 2024, and dementia care costs predicted to almost treble by 2040, it is essential we understand differences in healthcare use among PLWD. This study aimed to explore social and spatial variations in healthcare use among people diagnosed with dementia (2002-2016). METHODS Data were derived from Electronic Health Records of Clinical Practice Research Datalink GP patients in England (n = 142,302). To standardise healthcare contacts, rates of healthcare contacts per year were calculated for three primary (GP observations and medications) and three secondary healthcare types [Accident & Emergency (A&E) attendances and, emergency and elective hospital admissions]. Fully-adjusted generalised linear regression models were used to identify healthcare use variation by social and spatial groups. Twelve models were generated, one for each healthcare type in early- and late-onset populations separately. RESULTS This study highlights numerous social and spatial variations in healthcare use among PLWD. Among PLWD, several groups tended to have healthcare service use more closely associated with negative outcomes, including a greater likelihood of A&E attendances and emergency and elective hospital admissions. These groups include: men, people from White ethnicity groups and people from more deprived and rural areas. CONCLUSIONS Systemic and social measures are needed to reduce variations in healthcare use inequalities in PWLD. These include greater healthcare continuity, health checks and medicines reviews, culturally appropriate services, better and more accessible treatment and improved infrastructure.
Collapse
Affiliation(s)
- James Watson
- School of Environmental Sciences, The University of Liverpool, Liverpool, United Kingdom
| | - Mark A. Green
- School of Environmental Sciences, The University of Liverpool, Liverpool, United Kingdom
| | - Clarissa Giebel
- Department of Primary Care and Mental Health, University of Liverpool, Liverpool, United Kingdom
- NIHR ARC NWC, Liverpool, United Kingdom
| | | | - Asangaedem Akpan
- Department of Medicine for Older People and Stroke, Liverpool University Hospitals NHS FT, Liverpool, United Kingdom
- Healthy Ageing Group, University of Cumbria, Cumbria, United Kingdom
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
- NIHR CRN NWC, Liverpool, United Kingdom
| |
Collapse
|