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Yang M, Cheng H, Wang X, Ouyang M, Shajahan S, Carcel C, Anderson C, Kristoffersen ES, Lin Y, Sandset EC, Wang X, Yang J. Antithrombotics prescription and adherence among stroke survivors: A systematic review and meta-analysis. Brain Behav 2022; 12:e2752. [PMID: 36067030 PMCID: PMC9575604 DOI: 10.1002/brb3.2752] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/09/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES We aimed to investigate the prescription of antithrombotic drugs (including anticoagulants and antiplatelets) and medication adherence after stroke. METHODS We performed a systematic literature search across MEDLINE and Embase, from January 1, 2015, to February 17, 2022, to identify studies reporting antithrombotic medications (anticoagulants and antiplatelets) post stroke. Two people independently identified reports to include, extracted data, and assessed the quality of included studies according to the Newcastle-Ottawa scale. Where possible, data were pooled using random-effects meta-analysis. RESULTS We included 453,625 stroke patients from 46 studies. The pooled proportion of prescribed antiplatelets and anticoagulants among patients with atrial fibrillation (AF) was 62% (95% CI: 57%-68%), and 68% (95% CI: 58%-79%), respectively. The pooled proportion of patients who were treated according to the recommendation of guidelines of antithrombotic medications from four studies was 67% (95% CI: 41%-93%). It was reported that 11% (95% CI: 2%-19%) of patients did not receive antithrombotic medications. Good adherence to antiplatelet, anticoagulant, and antithrombotic medications was 78% (95% CI: 67%-89%), 71% (95% CI: 57%-84%), and 73% (95% CI: 59%-86%), respectively. CONCLUSION In conclusion, we found that less than 70% of patients were prescribed and treated according to the recommended guidelines of antithrombotic medications, and good adherence to antithrombotic medications is only 73%. Prescription rate and good adherence to antithrombotic medications still need to be improved among stroke survivors.
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Affiliation(s)
- Min Yang
- Department of Neurology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Hang Cheng
- Department of Neurology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Xia Wang
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, NSW, Australia
| | - Menglu Ouyang
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, NSW, Australia
| | - Sultana Shajahan
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, NSW, Australia
| | - Cheryl Carcel
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, NSW, Australia.,Department of Neurology, Royal Prince Alfred Hospital, The University of Sydney, NSW, Australia
| | - Craig Anderson
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, NSW, Australia.,Department of Neurology, Royal Prince Alfred Hospital, The University of Sydney, NSW, Australia.,The George Institute China at Peking University Health Science Centre, Beijing, PR China
| | - Espen Saxhaug Kristoffersen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Department of General Practice, Helsam, University of Oslo, Oslo, Norway
| | - Yapeng Lin
- Department of Neurology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.,International Clinical Research Center, Chengdu Medical College, Chengdu, China
| | - Else Charlotte Sandset
- Stroke Unit, Department of Neurology, Oslo University Hospital, Oslo, Norway.,The Norwegian Air Ambulance Foundation, Oslo, Norway
| | - Xiaoyun Wang
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jie Yang
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Viscogliosi G, Donfrancesco C, Lo Noce C, Vanuzzo D, Carle F, Giampaoli S, Palmieri L. Prevalence and Correlates of Statin Underuse for Secondary Prevention of Cardiovascular Disease in Older Adults 65-79 Years of Age: The Italian Health Examination Survey 2008-2012. Rejuvenation Res 2020; 23:394-400. [PMID: 32008438 DOI: 10.1089/rej.2019.2268] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Limited data are available on the prevalence and correlates of statin use for secondary cardiovascular (CV) prevention in the older adult population. We used data of older adults (65-79 years) with established atherosclerotic CV disease from the cross-sectional Italian Health Examination Survey 2008-2012 to address this issue. Lifestyles, CV risk factors, chronic diseases, and therapies were assessed using standardized procedures. A comprehensive geriatric assessment was performed to evaluate cognitive function, disability in basic activities of daily living/instrumental activities of daily living, mobility, and polypharmacy. Multiple regression analyses were performed to identify independent correlates of statin use. A total of 392 participants (mean age 72.1 ± 4.4 years, 61.5% men) were considered for this analysis. Coronary heart disease was identified in 67.1% of participants, cerebrovascular disease in 23.5%, and peripheral artery disease (PAD) in 18.1%. One hundred ninety (48.5%) were statin users. By multiple regression analysis, functional disability (odds ratio [OR] = 0.81; 95% confidence interval [CI] = 0.71-0.92; p = 0.002), cognitive impairment (OR = 0.87; 95% CI = 0.78-0.98; p = 0.018), and polypharmacy (OR = 0.86; 95% CI = 0.75-0.98; p = 0.035) predicted statin nonuse, whereas having hypertension (OR = 1.19; 95% CI = 1.05-1.34; p = 0.005), diabetes mellitus (OR = 1.14; 95% CI = 1.03-1.27; p = 0.013), or a previous myocardial revascularization (OR = 1.31; 95% CI = 1.16-1.48; p < 0.001) predicted statin use. Significant interaction terms were observed between cerebrovascular disease, PAD, cognitive impairment, and disability in predicting statin nonuse. Statin underuse in older adults aged 65-79 years with CV disease, and thus suboptimal secondary CV prevention, is highly prevalent despite current guidelines and recommendations. Common geriatric conditions are associated with statin nonuse. Such results support the need for improving the awareness of statin treatment for secondary CV prevention.
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Affiliation(s)
- Giovanni Viscogliosi
- Department of Cardiovascular, Dysmetabolic and Ageing-Associated Diseases, National Institute of Health, Rome, Italy.,Department of Epidemiology, Centre of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | - Chiara Donfrancesco
- Department of Cardiovascular, Dysmetabolic and Ageing-Associated Diseases, National Institute of Health, Rome, Italy
| | - Cinzia Lo Noce
- Department of Cardiovascular, Dysmetabolic and Ageing-Associated Diseases, National Institute of Health, Rome, Italy
| | - Diego Vanuzzo
- Department of Cardiology, National Association Hospital Cardiologists, Florence, Italy
| | - Flavia Carle
- Department of Epidemiology, Centre of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | - Simona Giampaoli
- Department of Cardiovascular, Dysmetabolic and Ageing-Associated Diseases, National Institute of Health, Rome, Italy
| | - Luigi Palmieri
- Department of Cardiovascular, Dysmetabolic and Ageing-Associated Diseases, National Institute of Health, Rome, Italy
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Zhang J, Gong Y, Zhao Y, Jiang N, Wang J, Yin X. Post-stroke medication adherence and persistence rates: a meta-analysis of observational studies. J Neurol 2019; 268:2090-2098. [PMID: 31792672 DOI: 10.1007/s00415-019-09660-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 11/26/2019] [Accepted: 11/27/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Following a doctor's prescribed medication regimen is the key to prevent recurrent stroke and adverse outcomes. Many studies have investigated post-stroke drug adherence and persistence in patients. However, a comprehensive analysis of the data is lacking. OBJECTIVES A meta-analysis of published literature was conducted to summarize the ratio of medication adherence and persistence in patients after stroke. METHODS Relevant studies were identified by conducting a literature search using PubMed, EMBASE and Web of Science up to April 2019. We also reviewed the reference list of the retrieved articles to identify additional studies. We included observational studies that reported data on patients' medication adherence or persistence status, or the rate of medication adherence or persistence among patients with stroke could be calculated based on the information provided. RESULTS The overall high medication adherence rate of patients with stroke was 64.1% (95% CI: 57.4%-70.8%), and the persistence rate of patients with stroke was 72.2% (95% CI: 69.1%-75.3%). The highest persistence rate was observed in cohort studies which was 80.1% (95% CI: 76.7%-83.4%). The medication adherence rate was the highest in cases where the rates were assessed through interviews or self-reports (77.7% (95% CI: 71.3%-84.1%)). CONCLUSIONS Medication adherence and persistence rates are low in patients after suffering a stroke. Patient medication adherence or persistence and their influencing factors should be considered for the treatment of stroke patients. More detailed disease prevention and management strategies need to be developed for stroke patients with different comorbidities.
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Affiliation(s)
- Jia Zhang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Yanhong Gong
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Yuxin Zhao
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Nan Jiang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Jing Wang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China
| | - Xiaoxv Yin
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China.
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