Smartphone Based Digital Screening for Aortic Valve Stenosis
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Sensitivity and specificity of a smartphone-derived algorithm for detecting moderate-to-severe aortic stenosis (AVA ≤ 1.5 cm²), using echocardiography as the reference standard
研究概览
简要总结
Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today.
Up to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist.
The interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence.
For the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability.
If successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.
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
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •The following inclusion and
排除标准
- •will be used for training, validation and test sets:
- •Inclusion criteria for group I (moderate to severe AS):
- •Moderate to severe AS defined as AVA ≤ 1.5cm² in echocardiographic assessment
- •No other significant VHD, valvular prosthesis, pacemaker or congenital heart defect
- •Documented echocardiography as part of routine clinical practice no older than 90 days
- •Patient age ≥ 18 years
- •Provided written informed consent
- •Inclusion criteria for group II:
- •No significant VHD, valvular prosthesis, pacemaker or congenital heart defect
- •Documented echocardiography as part of routine clinical practice no older than 90 days
- •Patient age ≥ 18 years
- •Provided written informed consent
- •Exclusion criteria (applicable for all groups):
- •Informed consent form not signed.
结局指标
主要结局
Sensitivity and specificity of a smartphone-derived algorithm for detecting moderate-to-severe aortic stenosis (AVA ≤ 1.5 cm²), using echocardiography as the reference standard
时间窗: At the baseline study visit (after completion of smartphone and echocardiographic assessments)
Sensitivity and specificity will be calculated by comparing the classification produced by the smartphone-based algorithm with the diagnosis obtained from transthoracic echocardiography, which serves as the clinical reference standard. Aortic stenosis severity will be defined according to established guideline criteria, with moderate-to-severe aortic stenosis classified as an aortic valve area (AVA) of ≤ 1.5 cm². Smartphone recordings will be obtained during a single study visit using built-in microphones and motion sensors to capture heart sounds and chest wall vibrations. Echocardiographic measurements, performed by certified clinical personnel, will provide the comparator classification. The reported outcome will reflect how accurately the smartphone algorithm identifies participants with moderate-to-severe aortic stenosis at this time point.
次要结局
- Quality of smartphone-acquired cardiac signals, measured by signal-to-noise ratio (SNR)(At the baseline study visit)
- Agreement between smartphone-derived aortic stenosis classification and echocardiographic grading, measured by Cohen's kappa coefficient(At the baseline study visit)
- Area under the receiver operating characteristic curve (AUROC) of the smartphone-based algorithm for detecting moderate-to-severe aortic stenosis(At the baseline study visit)
- Incidence of major adverse cardiac and cerebrovascular events (MACCE)(Up to 12 months after the baseline study visit)