Suppr超能文献

电子健康自我管理干预在心力衰竭患者中的有效性:系统评价和荟萃分析。

Effectiveness of eHealth Self-management Interventions in Patients With Heart Failure: Systematic Review and Meta-analysis.

机构信息

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.

West China School of Medicine, Sichuan University, Chengdu, China.

出版信息

J Med Internet Res. 2022 Sep 26;24(9):e38697. doi: 10.2196/38697.

Abstract

BACKGROUND

Heart failure (HF) is a common clinical syndrome associated with substantial morbidity, a heavy economic burden, and high risk of readmission. eHealth self-management interventions may be an effective way to improve HF clinical outcomes.

OBJECTIVE

The aim of this study was to systematically review the evidence for the effectiveness of eHealth self-management in patients with HF.

METHODS

This study included only randomized controlled trials (RCTs) that compared the effects of eHealth interventions with usual care in adult patients with HF using searches of the EMBASE, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and CINAHL databases from January 1, 2011, to July 12, 2022. The Cochrane Risk of Bias tool (RoB 2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence for each outcome of interest. Meta-analyses were performed using Review Manager (RevMan v.5.4) and R (v.4.1.0 x64) software.

RESULTS

In total, 24 RCTs with 9634 participants met the inclusion criteria. Compared with the usual-care group, eHealth self-management interventions could significantly reduce all-cause mortality (odds ratio [OR] 0.83, 95% CI 0.71-0.98, P=.03; GRADE: low quality) and cardiovascular mortality (OR 0.74, 95% CI 0.59-0.92, P=.008; GRADE: moderate quality), as well as all-cause readmissions (OR 0.82, 95% CI 0.73-0.93, P=.002; GRADE: low quality) and HF-related readmissions (OR 0.77, 95% CI 0.66-0.90, P<.001; GRADE: moderate quality). The meta-analyses also showed that eHealth interventions could increase patients' knowledge of HF and improve their quality of life, but there were no statistically significant effects. However, eHealth interventions could significantly increase medication adherence (OR 1.82, 95% CI 1.42-2.34, P<.001; GRADE: low quality) and improve self-care behaviors (standardized mean difference -1.34, 95% CI -2.46 to -0.22, P=.02; GRADE: very low quality). A subgroup analysis of primary outcomes regarding the enrolled population setting found that eHealth interventions were more effective in patients with HF after discharge compared with those in the ambulatory clinic setting.

CONCLUSIONS

eHealth self-management interventions could benefit the health of patients with HF in various ways. However, the clinical effects of eHealth interventions in patients with HF are affected by multiple aspects, and more high-quality studies are needed to demonstrate effectiveness.

摘要

背景

心力衰竭(HF)是一种常见的临床综合征,与较高的发病率、沉重的经济负担和较高的再入院风险相关。电子健康自我管理干预措施可能是改善 HF 临床结局的有效方法。

目的

本研究旨在系统评价电子健康自我管理在 HF 患者中的有效性证据。

方法

本研究仅纳入了比较电子健康干预措施与 HF 成人患者常规护理效果的随机对照试验(RCT),检索了 EMBASE、PubMed、CENTRAL(Cochrane 对照试验中心注册库)和 CINAHL 数据库,检索时间为 2011 年 1 月 1 日至 2022 年 7 月 12 日。采用 Cochrane 偏倚风险工具(RoB 2)评估每个研究的偏倚风险。采用推荐、评估、制定与评价分级(GRADE)标准对每个感兴趣结局的证据质量进行评级。使用 Review Manager(RevMan v.5.4)和 R(v.4.1.0 x64)软件进行荟萃分析。

结果

共有 24 项纳入 9634 名参与者的 RCT 符合纳入标准。与常规护理组相比,电子健康自我管理干预措施可显著降低全因死亡率(比值比 [OR] 0.83,95%CI 0.71-0.98,P=.03;GRADE:低质量)和心血管死亡率(OR 0.74,95%CI 0.59-0.92,P=.008;GRADE:中质量),以及全因再入院率(OR 0.82,95%CI 0.73-0.93,P=.002;GRADE:低质量)和 HF 相关再入院率(OR 0.77,95%CI 0.66-0.90,P<.001;GRADE:中质量)。荟萃分析还表明,电子健康干预措施可以提高患者对 HF 的认识并改善其生活质量,但无统计学意义。然而,电子健康干预措施可显著提高药物依从性(OR 1.82,95%CI 1.42-2.34,P<.001;GRADE:低质量)和改善自我护理行为(标准化均数差-1.34,95%CI -2.46 至 -0.22,P=.02;GRADE:极低质量)。关于纳入人群设置的主要结局的亚组分析发现,与门诊诊所设置相比,电子健康干预措施在出院后的 HF 患者中更有效。

结论

电子健康自我管理干预措施可以在多个方面有益于 HF 患者的健康。然而,电子健康干预措施在 HF 患者中的临床效果受到多个方面的影响,需要更多高质量的研究来证明其有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcad/9555330/69bb1739f9da/jmir_v24i9e38697_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验