Can novel technologies improve the accuracy of pre-hospital diagnosis in patients with a suspected occlusion myocardial infarction?

Background<br/>Complete occlusion of a coronary artery, known as occlusion myocardial infarction (OMI), is an underdiagnosed condition, with approximately 25% of patients with non-ST elevation myocardial infarction (NSTEMI) having an OMI and a higher mortality than non-OMI patients.<br/>Aim<br/>This thesis aimed to define the current understanding of OMI through the literature and investigate the potential of novel methods that could improve the accuracy of pre-hospital OMI diagnosis.<br/>Method<br/>The local extent of OMI was analysed by identifying emailed electrocardiograms (ECG) with OMI at a regional district general hospital. To identify components in pre-existing decision systems for MI identification, a systematic review was performed. To identify any clinical features associated with OMI, latent class analysis was used. To evaluate a simulated certainty index (a percentile confidence rating of an automated ECG analysis) an online questionnaire was answered by healthcare professionals. Blood derived biomarkers were investigated using proximity extension assays to identify if circulating proteins were associated with OMI with machine learning methods used to combine clinical features and biomarkers to distinguish OMI.

Name dc:type.qualificationname

Doctor of Philosophy (awarded by UHI)

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

University of the Highlands and Islands

Year dc:date.issued

2025

Author dc:creator

• Knoery, Charles

Advisor dc:contributor.advisor

• Pritchard, Antonia

Language dc:language

eng

Identifier

oai:pure.atira.dk:studenttheses/ab8b77f0-ad43-4151-938e-865e0914efa9

OAI identifier oai:identifier

oai:pure.atira.dk:studenttheses/ab8b77f0-ad43-4151-938e-865e0914efa9