Pulse Wave Velocity and Machine Learning for Prediction of Coronary Artery Disease by Coronary CT Angiography - The Heart Waves Study
试验速览
- 阶段
- 不适用
- 状态
- 终止
- 发起方
- 入组人数
- 156
- 试验地点
- 1
- 主要终点
- Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3
研究概览
简要总结
This study will evaluate the ability of device-estimated pulse wave velocity and machine learning methods to improve the prediction of potential symptomatic coronary artery disease
详细描述
In stable patients with suspected symptomatic coronary artery disease, an estimation of pre-test probability (PTP) and a clinical assessment are used to decide who should be investigated further. PTP has historically been based on age, sex, the nature of chest pain or dyspnea as angina equivalent. It is recommended to continue investigation of all with PTP ≥15%, but also to consider investigation at PTP 5-15% (low-intermediate risk) which is the majority of patients. Despite updates to PTP estimations in the 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes, it has been shown that they overestimate the risk of coronary artery disease.
In 2024, the ESC Guidelines were updated to recommend an updated clinical assessment method, the risk factor-weighted clinical likelihood (RF-CL), which is based on symtoms and number of risk factors. It has been shown to have better predictive ability compared to PTP alone, but is still largely based on epidemiological data, which may not be valid for all individuals.
Coronary computer tomography angiography (CCTA) is the method becoming increasingly established at low-intermediate risk. An initial, non-invasive strategy with CCTA compared to invasive or more advanced examinations is safe and simple. At the same time, CCTA is resource-intensive, with limited availability, and the examination involves both contrast, radiation and incidental findings. Thus, there is a need to improve the risk estimation.
Arterial stiffness assessed by pulse wave velocity is an independent marker for cardiovascular events and has been shown to be independently associated with the degree of coronary artery disease. Arterial stiffness is, however, rarely measured in the clinic as it traditionally has required cumbersome procedures. Newer methods include the brachial single cuff-based Arteriograph and the optical technique photoplethysmography (PPG), widely available in healthcare pulse oximeters, but increasingly also in different consumer devices, often complemented by single-lead ECG.
The main aim of this study is to evaluate arterial stiffness and its possible role to improve risk stratification of patients undergoing CCTA for potential coronary artery disease.
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
- 30 Years 至 70 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients undergoing coronary computer tomography angiography to investigate stable suspected symptomatic coronary artery disease.
- •Age 30 to <70 years of age.
排除标准
- •Known coronary artery disease (prior myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft or any angiographic evidence of coronary artery disease ≥50% lesion in a major epicardial vessel).
- •Known significant cardiac (> moderate valvular disease, heart failure with reduced ejection fraction, hypertrophic cardiomyopathy, or congenital heart disease), or pulmonary condition which could explain symptoms.
- •Known ongoing atrial fibrillation/flutter.
- •No Swedish social security number.
- •Unable to provide written informed consent.
结局指标
主要结局
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3
时间窗: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 when adding photoplethysmography (PPG) estimated arterial stiffness to the standard model (including age, sex, symtom score \[0-3\] and number of risk factors \[0-5\]). Coronary artery disease reporting and data system (CAD-RADS) ≥3 refers to the classification of coronary artery disease with at least moderate stenosis as identified on coronary computer tomography angiography. The classification follows the CAD-RADS 2.0 definition. Stenosis is graded in severity from 0-5.
次要结局
- Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2(Typically within 1 month of enrollment)
- Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by Arterigraph(Typically within 1 month of enrollment)
- Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 by Arterigraph(Typically within 1 month of enrollment)
- Improved area under receiver operating curve (ROC) to predict Coronary artery calcium score(Typically within 1 month of enrollment)
- Number of patients diagnosed with acute or chronic coronary artery disease(1 year after enrollment)
- Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by adding ECG(Typically within 1 month of enrollment)
- Machine learning analysis of photoplethysmography to predict CAD-RADS ≥2(Typically within 1 month of enrollment)
- Machine learning analysis of photoplethysmography to predict aortic stenosis(Typically within 1 month of enrollment)
- Machine learning analysis of photoplethysmography to predict cardiac function(Typically within 1 month of enrollment)