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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
阿撕匹林发布了新的文献求助10
2秒前
领导范儿应助gaoweixue采纳,获得10
2秒前
CipherSage应助aaaaaa采纳,获得10
4秒前
5秒前
ercong_604发布了新的文献求助30
5秒前
6秒前
香蕉觅云应助乐观小之采纳,获得10
6秒前
Jasper应助123采纳,获得10
6秒前
虚心的渊思完成签到,获得积分10
6秒前
6秒前
7秒前
Dawn发布了新的文献求助10
7秒前
hkym完成签到,获得积分10
8秒前
9秒前
10秒前
10秒前
10秒前
wanci应助Ysk采纳,获得10
11秒前
11秒前
12秒前
起气球发布了新的文献求助10
12秒前
中陆发布了新的文献求助10
12秒前
13秒前
烧番完成签到,获得积分10
14秒前
西瓜宝宝发布了新的文献求助10
14秒前
14秒前
123发布了新的文献求助10
15秒前
jwl发布了新的文献求助10
15秒前
aaa发布了新的文献求助10
15秒前
gaoweixue发布了新的文献求助10
16秒前
洋洋完成签到,获得积分10
16秒前
shiyaouao发布了新的文献求助10
17秒前
17秒前
JRF发布了新的文献求助10
19秒前
小二郎应助wm采纳,获得10
19秒前
19秒前
19秒前
深情安青应助jwl采纳,获得10
21秒前
21秒前
甜美月亮应助123采纳,获得10
22秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
模型平均及其应用 900
Nondestructive Testing Handbook: Vol. 4, Thermal and Infrared Testing (IR), 4th ed 800
作者名:Kristopher P. Plain,悉尼大学的,目前只能查到其四篇论文,想找到其博士论文 590
Évora na Idade Média 555
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Structural Analysis 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7353376
求助须知:如何正确求助?哪些是违规求助? 8964445
关于积分的说明 19045542
捐赠科研通 7001994
什么是DOI,文献DOI怎么找? 3221692
关于科研通互助平台的介绍 2386157
邀请新用户注册赠送积分活动 2202271