Predicting Heart Failure Recovery by Wearables and Machine Learning

Wearable devices for measuring vital functions, known as "wearables" from the consumer sector, such as the Apple Watch, have gained significant popularity. Increasingly, they are also being used for cardiovascular assessments. For example, a previous study at the Department of Cardiology and Pulmonology demonstrated that the Apple Watch is well-accepted by HF patients and that the average daily step count correlates significantly with the 6-minute walk test. Research has since shifted from simple correlation analyses to more complex tasks, such as predicting clinical laboratory measurements or events, like decompensation in HF patients.

Machine learning methods have proven to be suitable for various predictions in the field of heart failure. For instance, it has been shown that ECG data can be used to predict HF risk surrogates or comorbidities, such as NT-proBNP levels, age or gender, anemia, or renal insufficiency. Beyond ECG, multimodal approaches that combine multiple measurements have demonstrated the feasibility of data-driven HF risk assessment. Examples include combining cardiac MRI with clinical information to predict time to hospitalization or HF incidence rates in atrial fibrillation.

Since ECG alone does not provide sufficient prognostic value for heart failure (HF) assessment, this study aims to advance the state of the art by incorporating additional sensor data. The Apple Watch will be utilized as the device of choice. Extracted parameters include respiratory rate, oxygen saturation, nighttime temperature, acceleration data, and automatically provided derived parameters (e.g., step count, sleep times).

Specific Objectives:

• Collect data from HF patients using the Apple Watch.
• Extract and integrate the data into a unified format.
• Perform correlation analysis with clinical parameters.
• Develop an algorithm/model to predict clinical parameters.
• Statistically evaluate the predictive power of the developed model.