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Title: Novel concepts for non-invasive telemonitoring in chronic heart failure
Author: Mabote, Thato
ISNI:       0000 0004 5369 0367
Awarding Body: University of Hull and the University of York
Current Institution: University of Hull
Date of Award: 2015
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Background: The morbidity and mortality from chronic heart failure (HF) remains alarmingly high, in part due to failure to apply substantial disease modifying strategies to halt disease progression. Telemonitoring has been proposed as a potential disease management strategy to deal with the burden posed by HF. While treatment decisions guided by invasive telemonitoring data have shown early promise, it is unclear whether non-invasively derived surrogates of haemodynamics could be reliable enough to guide therapeutic interventions. Aims: The principal aim of this thesis is to investigate whether non-invasive “smart technologies” could accurately detect and track subtle changes in surrogates of cardiovascular haemodynamics in response to challenges posed by activities of daily living and non-adherence to therapy. Methodology: A series of prospective clinical studies were conducted in stable patients with chronic heart failure, on optimum tolerated guideline directed therapy for heart failure. Studies were performed under clinically adapted conditions to mimic the patient’s own habitat. Results: Significant systemic haemodynamic perturbations were detected non-invasively with variations in environmental temperature. Additionally, music, which modulates the sympathetic tone, led to modest changes in systemic blood pressure and heart rate, although the changes did not reach statistical significance. Non-adherence to cardiovascular therapy led to striking adverse changes in systemic haemodynamics. Smart technologies demonstrated a remarkable consistency in detecting haemodynamic perturbations. Conclusion: Non-invasive detection and tracking of changes in haemodynamics is feasible with smart technologies. The results need to be validated in larger multicenter clinical trials, with particular emphasis on using the data to guide therapeutic decisions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Medicine