Wearables and Artificial Intelligence in Advanced Heart Failure Care

Официальное название

Advancing Proactive Care in Advanced Heart Failure: Integrating AI and Continuous Remote Monitoring for Early Detection of Heart Failure Deterioration

Обзор

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.

Подробное описание

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).

Первичные конечные точки

• Algorithm Performance Metrics [Срок оценки: From enrollment to the end of the monitoring period at 1 year.]

Вторичные конечные точки (2)

• Change in daily exercise duration [Срок оценки: From baseline to the end of the monitoring period at 1 year.]
• Perceived usability [Срок оценки: At 1-year]

Критерии участия

Критерии включения

• >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).

Критерии исключения

• Impossibility to provide inform consent.
• Impossibility to self-report data due to physical or mental disability.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Нидерланды · 1 центр

• UMC Utrecht — Utrecht

Публикации

• Schots BBS, Pizarro CS, Arends BKO, Oerlemans MIFJ, Ahmetagic D, van der Harst P, van Es R. Deep learning for electrocardiogram interpretation: Bench to bedside. Eur J Clin Invest. 2025 Apr;55 Suppl 1(Suppl 1):e70002. doi: 10.1111/eci.70002. PMID 40191935
• Wang L, Zhou X. Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals. Sensors (Basel). 2019 Mar 28;19(7):1502. doi: 10.3390/s19071502. PMID 30925693
• Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. Sensors (Basel). 2022 Oct 20;22(20):8002. doi: 10.3390/s22208002. PMID 36298352
• Truby LK, Rogers JG. Advanced Heart Failure: Epidemiology, Diagnosis, and Therapeutic Approaches. JACC Heart Fail. 2020 Jul;8(7):523-536. doi: 10.1016/j.jchf.2020.01.014. Epub 2020 Jun 10. PMID 32535126

Идентификаторы

NCT: NCT07051356 · 24U-1521

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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