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