{"title":"Prediction Models of Medication Adherence in Chronic Disease Patients: Systematic Review and Critical Appraisal.","authors":"Jingwen Xu, Xinyi Zhao, Fei Li, Yan Xiao, Kun Li","doi":"10.1111/jocn.17577","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims and objectives: </strong>To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.</p><p><strong>Background: </strong>Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.</p><p><strong>Design: </strong>Systematic review.</p><p><strong>Methods: </strong>We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.</p><p><strong>Results: </strong>The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high-risk for bias.</p><p><strong>Conclusions: </strong>According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.</p><p><strong>Relevance to clinical practice: </strong>Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high-risk individuals and key factors associated with low medication adherence in chronic diseases.</p><p><strong>Patient or public contribution: </strong>This systematic review was conducted without patient or public participation.</p>","PeriodicalId":50236,"journal":{"name":"Journal of Clinical Nursing","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jocn.17577","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
Abstract
Aims and objectives: To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.
Background: Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.
Design: Systematic review.
Methods: We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.
Results: The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high-risk for bias.
Conclusions: According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.
Relevance to clinical practice: Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high-risk individuals and key factors associated with low medication adherence in chronic diseases.
Patient or public contribution: This systematic review was conducted without patient or public participation.
目的和目的：总结目前发展的慢性疾病患者药物依从性风险预测模型，并评价其性能和适用性。背景：确保药物依从性对于有效管理慢性疾病至关重要。尽管已有大量研究试图构建预测慢性疾病患者服药依从性的风险预测模型，但这些模型的可靠性和实用性仍然不确定。设计：系统回顾。方法：检索PubMed、Web of Science、Cochrane、CINAHL、Embase和Medline数据库，检索时间为2023年7月16日。两位作者独立筛选了符合预定义纳入标准的药物依从性风险预测模型。采用预测模型偏倚风险评估工具（PROBAST）对纳入研究的偏倚风险和临床适用性进行评估。本系统评价遵循2020年PRISMA检查表。结果：本研究共纳入11项研究的11个风险预测模型。药物治疗方案和年龄是最常见的预测因素。PROBAST的使用揭示了一些基本的方法细节在这些模型中没有被彻底报告。由于方法学的限制，所有模型都被评为具有偏倚高风险。结论：根据PROBAST，目前用于预测慢性疾病患者药物依从性的模型存在较高的偏倚风险。未来的研究应优先考虑提高模型开发的方法质量，并对现有模型进行外部验证。与临床实践的相关性：根据综述结果，提出了改进预测模型构建方法的建议，目的是识别慢性病患者的高危人群和与低药物依从性相关的关键因素。患者或公众贡献：本系统评价是在没有患者或公众参与的情况下进行的。
期刊介绍：
The Journal of Clinical Nursing (JCN) is an international, peer reviewed, scientific journal that seeks to promote the development and exchange of knowledge that is directly relevant to all spheres of nursing practice. The primary aim is to promote a high standard of clinically related scholarship which advances and supports the practice and discipline of nursing. The Journal also aims to promote the international exchange of ideas and experience that draws from the different cultures in which practice takes place. Further, JCN seeks to enrich insight into clinical need and the implications for nursing intervention and models of service delivery. Emphasis is placed on promoting critical debate on the art and science of nursing practice.
JCN is essential reading for anyone involved in nursing practice, whether clinicians, researchers, educators, managers, policy makers, or students. The development of clinical practice and the changing patterns of inter-professional working are also central to JCN''s scope of interest. Contributions are welcomed from other health professionals on issues that have a direct impact on nursing practice.
We publish high quality papers from across the methodological spectrum that make an important and novel contribution to the field of clinical nursing (regardless of where care is provided), and which demonstrate clinical application and international relevance.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
