Xu Jingwen, Zhao Xinyi, Li Fei, Xiao Yan, Li Kun
School of Nursing, Jilin University, Changchun, China.
Department of Endocrinology, The First Hospital of Jilin University, Changchun, China.
J Clin Nurs. 2025 May;34(5):1602-1612. doi: 10.1111/jocn.17577. Epub 2024 Dec 30.
To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.
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.
Systematic review.
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.
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.
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.
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.
This systematic review was conducted without patient or public participation.
总结目前已开发的用于预测慢性病患者用药依从性的风险预测模型,并评估其性能和适用性。
确保用药依从性对于有效管理慢性病至关重要。尽管众多研究致力于构建预测慢性病患者用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不确定。
系统评价。
我们在PubMed、Web of Science、Cochrane、CINAHL、Embase和Medline数据库中进行检索,检索时间跨度从建库至2023年7月16日。两位作者独立筛选符合预先设定纳入标准的用药依从性风险预测模型。采用预测模型偏倚风险评估工具(PROBAST)评估纳入研究的偏倚风险和临床适用性。本系统评价遵循2020年PRISMA清单。
该研究共纳入了来自11项研究的11个风险预测模型。用药方案和年龄是最常见的预测因素。使用PROBAST评估发现,这些模型中一些重要的方法学细节未得到充分报告。由于方法学上的局限性,所有模型均被评为具有高偏倚风险。
根据PROBAST评估,目前用于预测慢性病患者用药依从性的模型存在较高的偏倚风险。未来的研究应优先提高模型开发的方法学质量,并对现有模型进行外部验证。
基于综述结果,已提供建议以完善预测模型的构建方法,旨在识别慢性病中用药依从性低的高风险个体和关键因素。
本系统评价未涉及患者或公众参与。