The University of Southampton
University of Southampton Institutional Repository

Risk stratification in sudden cardiac death: engineering novel solutions in heart failure

Risk stratification in sudden cardiac death: engineering novel solutions in heart failure
Risk stratification in sudden cardiac death: engineering novel solutions in heart failure
Sudden cardiac death (SCD) risk is reduced by implantable cardioverter defibrillator (ICD) use in appropriately selected patients. Established markers such as impairment of left ventricular function and QRS duration are non specific for arrhythmic death and therefore many patients receive ICD therapy from which they gain no benefit, either due to survival without arrhythmia or death from pump failure. Both myocardial scar and serum protein biomarkers have potential as SCD risk stratifiers, but novel solutions are needed to deliver non invasive tests that are suitable for point of care testing. The aims of this thesis were to explore novel assessment methods for the risk stratification of SCD, with particular focus on heart failure.

Several approaches were chosen to explore these concepts: (i) meta-analysis to assess the utility of fragmented QRS, (ii) retrospective evaluation of ECG and CMR to assess ECG markers of repolarisation and (iii) QRS scoring, (iv) prospective evaluation of an automated QRS scoring algorithm to predict myocardial scar, (v) artificial intelligence machine learning techniques to develop and validate an algorithm capable to classifying ECG scar, and (vi) a novel high resolution proteomic technique to propose biomarkers of SCD risk, validated using ELISA (vii). The hypothesis is that novel clinical tools, encompassing technologies and techniques which could stretch across the clinical landscape from primary to specialised care services, can be identified as indicators of ICD benefit in patients at risk of SCD.

My results indicate that simpler ECG markers such as T-peak-end, fQRS and QRS scoring have a significant association with myocardial scar, although the strength of association varies according to scar characteristics, and is not specific. The specificity of these markers for mode of death is also weak. Computerised algorithms can serve to speed up manual ECG scoring, whilst maintaining overall accuracy, but greatest potential is seen in using a novel marker, custom developed using artificial intelligence techniques. I also found that candidate serum biomarkers, predictive of death or ventricular arrhythmia, could be identified through high resolution proteomic techniques. Clinical and technical validation with ELISA is possible.

Novel non invasive markers, such as serum proteins and computer ECG analysis may be valuable tools to improve risk prediction. The incremental benefit of these tools to determine prognosis, and select those who will most benefit from ICD therapy, can now be addressed by future prospective studies.
University of Southampton
Rosengarten, James A.
3ccf8397-ca9e-4b04-864f-5c2515db8965
Rosengarten, James A.
3ccf8397-ca9e-4b04-864f-5c2515db8965
Hanson, Mark
1952fad1-abc7-4284-a0bc-a7eb31f70a3f
Morgan, John
7bd04ada-ca61-4a2c-b1cf-1750ffa9d89c

Rosengarten, James A. (2014) Risk stratification in sudden cardiac death: engineering novel solutions in heart failure. University of Southampton, Doctoral Thesis, 232pp.

Record type: Thesis (Doctoral)

Abstract

Sudden cardiac death (SCD) risk is reduced by implantable cardioverter defibrillator (ICD) use in appropriately selected patients. Established markers such as impairment of left ventricular function and QRS duration are non specific for arrhythmic death and therefore many patients receive ICD therapy from which they gain no benefit, either due to survival without arrhythmia or death from pump failure. Both myocardial scar and serum protein biomarkers have potential as SCD risk stratifiers, but novel solutions are needed to deliver non invasive tests that are suitable for point of care testing. The aims of this thesis were to explore novel assessment methods for the risk stratification of SCD, with particular focus on heart failure.

Several approaches were chosen to explore these concepts: (i) meta-analysis to assess the utility of fragmented QRS, (ii) retrospective evaluation of ECG and CMR to assess ECG markers of repolarisation and (iii) QRS scoring, (iv) prospective evaluation of an automated QRS scoring algorithm to predict myocardial scar, (v) artificial intelligence machine learning techniques to develop and validate an algorithm capable to classifying ECG scar, and (vi) a novel high resolution proteomic technique to propose biomarkers of SCD risk, validated using ELISA (vii). The hypothesis is that novel clinical tools, encompassing technologies and techniques which could stretch across the clinical landscape from primary to specialised care services, can be identified as indicators of ICD benefit in patients at risk of SCD.

My results indicate that simpler ECG markers such as T-peak-end, fQRS and QRS scoring have a significant association with myocardial scar, although the strength of association varies according to scar characteristics, and is not specific. The specificity of these markers for mode of death is also weak. Computerised algorithms can serve to speed up manual ECG scoring, whilst maintaining overall accuracy, but greatest potential is seen in using a novel marker, custom developed using artificial intelligence techniques. I also found that candidate serum biomarkers, predictive of death or ventricular arrhythmia, could be identified through high resolution proteomic techniques. Clinical and technical validation with ELISA is possible.

Novel non invasive markers, such as serum proteins and computer ECG analysis may be valuable tools to improve risk prediction. The incremental benefit of these tools to determine prognosis, and select those who will most benefit from ICD therapy, can now be addressed by future prospective studies.

Text
Risk stratification in sudden cardiac death- Engineering novel solutions in heart failure - Version of Record
Available under License University of Southampton Thesis Licence.
Download (6MB)

More information

Published date: July 2014
Organisations: University of Southampton, Human Development & Health

Identifiers

Local EPrints ID: 407449
URI: http://eprints.soton.ac.uk/id/eprint/407449
PURE UUID: 3bb74bf4-d1a8-462d-b01a-a7f9a5ccd3f1
ORCID for Mark Hanson: ORCID iD orcid.org/0000-0002-6907-613X

Catalogue record

Date deposited: 08 Apr 2017 01:03
Last modified: 14 Mar 2019 01:47

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×