Speaker
Description
Heart failure (HF) poses a significant health burden in high-income countries, affecting over 10% of individuals aged 70 and older. The progressive nature of HF leads to frequent hospital admissions and elevated healthcare costs. There is no easily-acquired biomarker available which allows to gain insight and derive predictions into HF dynamics with the current diagnosis being based on blood tests, X-ray or cardiac ultrasound. Wearable devices like the Apple Watch (Apple Inc., Cupertino, California, U.S.) offer a more unobtrusive monitoring of health parameters compared to these measures. In this feasibility study, we aim to determine in how far Apple Watch data combined with machine learning can be used to predict the course of HF in a well-defined cohort.
A observational clinical study is conducted at the University Medical Center Göttingen’s Department of Cardiology and Pneumology (IRB Ethics Approval No. 23/2/24; funding via German Centre for Cardiovascular Research, DZHK). N=32 HF patients with reduced ejection fraction hospitalized for acute decompensation (ejection fraction ≤40%, NTproBNP >1000 pg/ml, and at least one symptom such as edema, pleural effusion, or ascites) will be included with a 90 day follow-up period. Patients will wear an Apple Watch during their hospitalization and sensor data (e.g. single-lead electrocardiography, SpO2, Respiration, Step counter) will be acquired. This data is used to predict clinical parameters and risk assessment, e.g. prediction of HF course.
In this talk we present initial insights of our study with the data that is available at this point. Next to the main research question, we will present results regarding the patients’ opinion on using the Apple Watch and their adherence.
