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McGilligan V, Watterson S, Rjoob K, Chemaly M, Bond R, Iftikhar A, Knoery C, Leslie SJ, McShane A, Bjourson A, Peace A. An exploratory analysis investigating blood protein biomarkers to augment ECG diagnosis of ACS. J Electrocardiol 2019; 57S:S92-S97. [PMID: 31519392 DOI: 10.1016/j.jelectrocard.2019.09.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/23/2019] [Accepted: 09/04/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND Acute Coronary Syndrome (ACS) is currently diagnosed using a 12‑lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12‑lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients. PURPOSE The aim of the present study was to undertake an exploratory analysis to compare protein expression profiles of ACS patients that may in the future augment ECG diagnosis. METHODS Patients were recruited consecutively at the cardiac catheterization laboratory at Altnagelvin Hospital over a period of 6 months. A low risk control group was recruited by advertisement. Blood samples were analysed using the multiplex proximity extension assays by OLINK proteomics. Support vector machine (SVM) learning was used as a classifier to distinguish between patient groups on training data. The ST segment elevation level was extracted from each ECG for a subset of patients and combined with the protein markers. Quadratic SVM (QSVM) learning was then used as a classifier to distinguish STEMI from NSTEMI on training and test data. RESULTS Of the 344 participants recruited, 77 were initially diagnosed with NSTEMI, 7 with STEMI, and 81 were low risk controls. The other participants were those diagnosed with angina (stable and unstable) or non-cardiac patients. Of the 368 proteins analysed, 20 proteins together could significantly differentiate between patients with ACS and patients with stable angina (ROC-AUC = 0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUC = 1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUC = 1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI n = 6 and NSTEMI n = 6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUC = 0.94). The 20 proteins without the ST elevation feature gave an accuracy of 80.6% (ROC-AUC 0.91), while the ST elevation feature without the protein biomarkers resulted in an accuracy of 69.3% (ROC-AUC = 0.81). CONCLUSIONS This preliminary study identifies panels of proteins that should be further explored in prospective studies as potential biomarkers that may augment ECG diagnosis and stratification of ACS. This work also highlights the importance for future studies to be designed to allow a comparison of blood biomarkers not only with ECG's but also with cardio angiograms.
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Affiliation(s)
- Victoria McGilligan
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK.
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK
| | - Khaled Rjoob
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; School of Computing, Ulster University, Jordanstown campus, Northern Ireland, UK
| | - Melody Chemaly
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK
| | - Raymond Bond
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; School of Computing, Ulster University, Jordanstown campus, Northern Ireland, UK
| | - Aleeha Iftikhar
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; School of Computing, Ulster University, Jordanstown campus, Northern Ireland, UK
| | - Charles Knoery
- Cardiac Unit, Raigmore Hospital, NHS Highland, Inverness IV2 3UJ, UK; Division of Rural Health and Wellbeing, University of Highlands and Islands, Inverness IV2 3JH, UK
| | - Stephen J Leslie
- Cardiac Unit, Raigmore Hospital, NHS Highland, Inverness IV2 3UJ, UK; Division of Rural Health and Wellbeing, University of Highlands and Islands, Inverness IV2 3JH, UK
| | - Anne McShane
- Emergency Department, Letterkenny University Hospital, Donegal, Ireland
| | - Anthony Bjourson
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK
| | - Aaron Peace
- Centre for Personalised Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry BT47 6SB, Northern Ireland, UK; Cardiology Department, Western Health and Social Care Trust, Altnagelvin Hospital, Londonderry, Northern Ireland, UK
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Bond RR, Novotny T, Andrsova I, Koc L, Sisakova M, Finlay D, Guldenring D, McLaughlin J, Peace A, McGilligan V, Leslie SJ, Wang H, Malik M. Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol 2018; 51:S6-S11. [DOI: 10.1016/j.jelectrocard.2018.08.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/06/2018] [Accepted: 08/09/2018] [Indexed: 11/17/2022]
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