跳至主要内容
临床试验/NCT06478901
NCT06478901已完成不适用

Precision and RElevance of CardIac ultraSound Using Artificial Intelligence for Left Ventricle Ejection Fraction Assessment in the Elderly. ( PRECISE AI)

Hôpital Broca APHP1 个研究点 分布在 1 个国家目标入组 129 人开始时间: 2023年1月14日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
发起方
入组人数
129
试验地点
1
主要终点
evaluate the relevance and accuracy of echocardiography assisted by Artificial intelligence in elderly patients

研究概览

简要总结

Heart failure (HF) is common in older adults, especially those over 65. It is a leading cause of hospitalization and has high mortality rates. Diagnosing HF in elderly patients can be challenging due to atypical symptoms and multiple other health issues. Echocardiography, an ultrasound of the heart, is crucial for accurate diagnosis and treatment planning.

One problem in geriatric care is the difficulty of accessing echocardiography due to high demand and limited specialized doctors. Recent advancements show that AI-assisted portable ultrasound devices can reliably measure heart function, producing results comparable to traditional methods.

This study aims to evaluate the accuracy and relevance of AI-assisted echocardiography (AutoEF-AI) in elderly patients. It also assesses whether geriatricians, even without specialized training, can capture quality images for AI analysis.

In simple terms, this study investigates if portable ultrasound devices with AI can provide precise heart function diagnostics, making it easier for older adults with heart failure to get the care they need, even without specialists.

详细描述

Heart failure (HF) is a major chronic illness, particularly common in older adults. With advances in healthcare and an aging population, HF is increasingly affecting people over 65 years old. In fact, 80% of HF patients are over 65. HF is associated with high mortality rates and is the leading cause of hospitalization after age 80, and even after age 65 in some countries like France.

In older adults, HF symptoms are often atypical due to multiple other health conditions, increased frailty, and associated geriatric syndromes, making diagnosis difficult. In this context, echocardiography (an ultrasound of the heart) is essential for accurately diagnosing HF.

Evaluating the left ventricular ejection fraction (LVEF) through echocardiography is a fundamental step in diagnosing HF and deciding on treatment strategies. This evaluation helps refine the HF diagnosis, propose appropriate treatments, and monitor changes in heart function over time.

One major challenge in geriatric units and nursing homes (EHPADs) is the difficulty in accessing echocardiography due to growing demand and a limited number of specialized doctors.

Recent studies have shown that automated LVEF measurements assisted by artificial intelligence (AI) using portable ultrasound devices are reliable and produce results comparable to traditional methods. This AI-assisted automatic LVEF calculation (AutoEF-AI) could be a major advantage in geriatric departments, providing a credible alternative to conventional echocardiography for evaluating LVEF in HF patients.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

年龄范围
75 Years 至 —(Older Adult)
性别
All
接受健康志愿者

入选标准

  • At least 75 years and a clinical presentation of acute heart failure consistent with the criteria of the European Society of Cardiology guidelines

排除标准

  • unstable patient

结局指标

主要结局

evaluate the relevance and accuracy of echocardiography assisted by Artificial intelligence in elderly patients

时间窗: From enrollment to the end 48 hours

The correlation between LVEF measurements from standard echocardiography and AutoEF-AI echocardiography was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Weighted Kappa coefficient was calculated to determine agreement between measurements in classifying patients into different categories based on LVEF

次要结局

未报告次要终点

研究者

发起方
Hôpital Broca APHP
申办方类型
Other
责任方
Sponsor

研究点 (1)

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