Study on Cardiac Output Evaluation Based on Wearable Monitoring Data
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 300
- 主要终点
- cardiac output
研究概览
简要总结
Based on the monitoring data of wearable devices, with cardiac output (CO) as the gold standard, this study intends to develop a non-invasive evaluation model of CO based on wearable data, and optimize the parameters to realize the cardiac capacity detection function in resting and exercise states on the wearable device.
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
- •Over 18 years old
- •Left ventricular ejection fraction (Left ventricular ejection fraction, LVEF) < 50%(200 subjects)
- •Left ventricular ejection fraction (Left ventricular ejection fraction, LVEF) ≥50% (100 subjects)
- •Able to use smart phones and operate wearable devices such as wristbands/watches
排除标准
- •Patients with pacemaker implantation
- •No smartphone
- •Currently participating in other clinical trials
- •Lactating women
- •Pregnant Women
- •Unable to run and ride due to personal physical and external reasons (subjects participating in the exercise state cardiac output model study)
- •Physical examination results in the past year have clear cardiovascular, metabolic, bone and joint related diseases that have exercise risk, or have diseases and related potential health risks confirmed by the self-examination form of physical status before exercise (participants in the exercise state cardiac output model study)
- •No informed consent was obtained
结局指标
主要结局
cardiac output
时间窗: From enrollment to the end of follow-up at 1 month
Taking cardiac function indicators such as cardiac output by echocardiography as the gold standard, using wearable device monitoring data（Photoplethysmographic pulse wave）, the resting state cardiac output artificial intelligence machine learning model was established, and the sensitivity, specificity, positive predictive value, negative predictive value, F1 score, diagnostic efficiency Area Under Curve (AUC), and the sensitivity, specificity, positive predictive value, negative predictive value, F1 score, diagnostic efficiency of the model were calculated. AUC), precision and precision-recall curves were used to evaluate the performance of the model.
次要结局
- Heart failure(From enrollment to the end of follow-up at 1 month)