Prediction Models of Medication Adherence in Chronic Disease Patients: Systematic Review and Critical Appraisal

批判性评价 医学 慢性病 药物依从性 重症监护医学 疾病 梅德林 替代医学 病理 内科学 政治学 法学
作者
Jingwen Xu,Xinyi Zhao,Fei Li,Yan Xiao,Kun Li
出处
期刊:Journal of Clinical Nursing [Wiley]
卷期号:34 (5): 1602-1612 被引量:6
标识
DOI:10.1111/jocn.17577
摘要

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.
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