Suppr超能文献

利用人工智能引导的超声心动图检测农村和偏远地区的心功能障碍和心脏瓣膜病:AGILE-echo 试验的原理和设计。

Use of artificial intelligence-guided echocardiography to detect cardiac dysfunction and heart valve disease in rural and remote areas: Rationale and design of the AGILE-echo trial.

机构信息

Imaging Research, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia.

Imaging Research, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.

出版信息

Am Heart J. 2024 Nov;277:11-19. doi: 10.1016/j.ahj.2024.08.004. Epub 2024 Aug 10.

Abstract

BACKGROUND

Transthoracic echocardiography (TTE) is essential in the diagnosis of cardiovascular diseases (CVD), including but not limited to heart failure (HF) and heart valve disease (HVD). However, its dependence on expert acquisition means that its accessibility in rural areas may be limited, leading to delayed management decisions and potential missed diagnoses. Artificial intelligence-guided (AI)-TTE offers a solution by permitting non-expert image acquisition. The impact of AI-TTE on the timing of diagnosis and early initiation of cardioprotection is undefined.

METHODS

AGILE-Echo (use of Artificial intelligence-Guided echocardiography to assIst cardiovascuLar patient managEment) is a randomized-controlled trial conducted in 5 rural and remote areas around Australia. Adults with CV risk factors and exercise intolerance, or concerns regarding HVD are randomized into AI-TTE or usual care (UC). AI-TTE participants may have a cardiovascular problem excluded, identified (leading to AI-guided interventions) or unresolved (leading to conventional TTE). UC participants undergo usual management, including referral for standard TTE. The primary endpoint is a composite of HVD or HF diagnosis at 12-months. Subgroup analysis, stratified based on age range and sex, will be conducted. All statistical analyses will be conducted using R.

RESULTS

Of the first 157 participants, 78 have been randomized into AI-TTE (median age 68 [IQR 17]) and 79 to UC (median age 65 [IQR 17], P = .034). HVD was the primary concern in 37 participants (23.6%) while 84.7% (n = 133) experienced exercise intolerance. The overall 10-year HF incidence risk was 13.4% and 20.0% (P = .089) for UC and AI-TTE arm respectively. Atrial remodeling, left ventricular remodeling and valvular regurgitation were the most common findings. Thirty-three patients (42.3%) showed no abnormalities.

CONCLUSIONS

This randomized-controlled trial of AI-TTE will provide proof-of-concept for the role of AI-TTE in identifying pre-symptomatic HF or HVD when access to TTE is limited. Additionally, this could promote the usage of AI-TTE in rural or remote areas, ultimately improving health and quality of life of community dwelling adults with risks, signs or symptoms of cardiac dysfunction.

摘要

背景

经胸超声心动图(TTE)在心血管疾病(CVD)的诊断中至关重要,包括但不限于心力衰竭(HF)和心脏瓣膜疾病(HVD)。然而,它对专家采集的依赖性意味着其在农村地区的可及性可能受到限制,导致管理决策延迟和潜在的漏诊。人工智能引导(AI)-TTE 通过允许非专家进行图像采集提供了一种解决方案。AI-TTE 对诊断时间和早期启动心脏保护的影响尚不清楚。

方法

AGILE-Echo(使用人工智能引导的超声心动图辅助心血管患者管理)是一项在澳大利亚五个农村和偏远地区进行的随机对照试验。有心血管危险因素和运动不耐受的成年人,或对 HVD 有顾虑的成年人,被随机分为 AI-TTE 或常规护理(UC)。AI-TTE 参与者的心血管问题可能被排除、识别(导致 AI 引导的干预)或未解决(导致常规 TTE)。UC 参与者接受常规管理,包括转介进行标准 TTE。主要终点是 12 个月时 HVD 或 HF 的复合诊断。将根据年龄范围和性别进行亚组分析。所有统计分析都将使用 R 进行。

结果

在前 157 名参与者中,78 名被随机分配到 AI-TTE(中位年龄 68 [IQR 17]),79 名到 UC(中位年龄 65 [IQR 17],P =.034)。37 名参与者(23.6%)的主要关注点是 HVD,而 84.7%(n = 133)经历了运动不耐受。UC 和 AI-TTE 组的总体 10 年 HF 发生率分别为 13.4%和 20.0%(P =.089)。心房重构、左心室重构和瓣膜反流是最常见的发现。33 名患者(42.3%)无异常。

结论

这项 AI-TTE 的随机对照试验将为 AI-TTE 在 TTE 受限时识别无症状 HF 或 HVD 的作用提供概念验证。此外,这可能会促进 AI-TTE 在农村或偏远地区的使用,最终改善有心脏功能障碍风险、迹象或症状的社区居住成年人的健康和生活质量。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验