Advancing Proactive Care in Advanced Heart Failure: Integrating AI and Continuous Remote Monitoring for Early Detection of Heart Failure Deterioration
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- UMC Utrecht
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Algorithm Performance Metrics
研究概览
简要总结
The goal of this observational study is to evaluate whether AI-based analyses of wearable sensor data can identify early signs of deterioration leading to hospitalization in patients with advanced heart failure.
The main questions it aims to answer are:
- Can AI-driven analysis of wearable data detect physiological or behavioral changes associated with impending hospital admissions?
- Does wearable-based remote monitoring influence daily exercise duration in patients with advanced heart failure.
- Is wearable-based remote monitoring usable and acceptable for patients with advanced heart failure in a real-world setting?
Participants will wear a wrist-worn (Fitbit) device continuously for one year and will use an eHealth app to answer question about their symptoms. Participant's physical activity, heart rate, heart rate variability, respiratory rate, sleep quality, and symptomatic status will be monitored remotely.
详细描述
Advanced heart failure (HF) is characterized by persistent and progressive symptoms despite optimal, guideline-directed medical therapy. Although improvements in care have been achieved, mortality remains high, and recurrent hospitalizations continue to significantly impact patients' morbidity and quality of life. Timely recognition of early signs of clinical deterioration remains a challenge. Innovative approaches that enable early identification of patients at increased risk of readmission may support proactive interventions and help reduce the need for hospitalization.
In the WAI-HF study, we will investigate whether AI-driven analysis wearable data can identify changes that precede hospital admission in patients with advanced heart failure. The wrist-worn device measures several physiological parameters including heart rate, heart rate variability, respiratory rate, skin temperature, 1-lead electrocardiogram, and sleep quality. Data collected in the remote monitoring including continuous data derived from the wearable device and symptomatic data collected in the eHealth app, will be used to develop a predictive model.
The study will be conducted according to the principles of the Declaration of Helsinki (64th WMA General Assembly, Fortaleza, Brazil, October 2013), to 'gedragscode gezondheidsonderzoek', and in accordance with the EU GDPR (General Data Protection Regulation).
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
- 否
入选标准
- •>18 years.
- •Diagnosis of advanced heart failure, including at least one of the following major criteria.
- •LVAD implanted
- •Included on the waiting list for Heart transplant
- •Meeting the European Society of CArdiology criteria for advanced HF:
- •Severe and persistent symptoms of heart failure [NYHA class III or IV].
- •Severe cardiac dysfunction: according to ESC guidelines definition
- •≥ 1 unplanned visit or hospitalization in the last 12 months requiring IV treatment.
- •Have access to a mobile phone or tablet with an operating system iSO 15 or Android 9 (or posterior versions of these systems).
排除标准
- •Impossibility to provide inform consent.
- •Impossibility to self-report data due to physical or mental disability.
结局指标
主要结局
Algorithm Performance Metrics
时间窗: From enrollment to the end of the monitoring period at 1 year.
Algorithm's performance to detect imminent admission in patients with advanced HF will be measured by means of the following parameters: Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, area under the ROC curve.
次要结局
- Change in daily exercise duration(From baseline to the end of the monitoring period at 1 year.)
- Perceived usability(At 1-year)
研究者
Pim van der Harst
Professor. Head of the department of Cardiology, UMC Utrecht.
UMC Utrecht