Predicting heart failure deterioration

Chronic heart failure (HF) is a condition that affects more than 900,000 people in the UK. Mortality rates associated with the condition are high, with nearly 20% of patients dying within one year of diagnosis. Continuous monitoring and risk stratification can help identify patients at risk of deterioration and may consequently improve patients' likelihood of survival. Current repeated-measure risk stratification techniques for HF patients often rely on subjective perception of symptoms, such as breathlessness, and markers of fluid retention in the body (e.g. weight). Despite the common use of such markers, studies have shown that they offer limited effectiveness in predicting HF-related events. This thesis set out to identify and evaluate new markers for repeated-measure risk stratification of HF patients. It started with an exploration of traditional HF measurements, including weight, blood pressure, heart rate and symptom scores, and aimed to improve the performance of these measurements using a data-driven approach. A multi-variate model was developed from data acquired during a randomised controlled trial of remotely-monitored HF patients. The rare occurrence of HF-related adverse events during the trial required the developement of a careful methodology. This methodology helped identify the markers with most predictive ability, which achieved moderate performance at identifying patients at risk of HF-related adverse events, clearly outperforming commonly-used thresholds. Subsequently, this thesis explored the potential value of additional, accelerometer-derived physical activity (PA) and sleep markers. For this purpose, the ability of accelerometer-derived markers to differentiate between individuals with and without HF was evaluated. It was found that markers that summarise the frequency and duration of different PA intensities performed best at differentiating between the two groups and may therefore be most suitable for future use in repeated-measure applications. As part of the analysis of accelerometer-derived HF markers, a gap in the methodology of automated accelerometer processing was identified, namely the need for self-reported sleep-onset and wake-up information. As a result, Chapter 5 of this thesis describes the development and evaluation of a data-driven solution for this problem. In summary, this thesis explored both traditional and new, accelerometer-derived markers for the early detection of HF deterioration. It utilised sound methodology to overcome limitations faced by sparse and unbalanced datasets and filled a methodological gap in the processing of signals from wrist-worn accelerometers.