PIDCO-ACS version 5
Research type
Research Study
Full title
PIDCO-ACS: Proteomics In Early Diagnosis of Coronary artery Occlusion in acute coronary syndrome
IRAS ID
260656
Contact name
Frances Hines
Contact email
Sponsor organisation
NHS Highland
Duration of Study in the UK
2 years, 0 months, 2 days
Research summary
Research Summary
Heart attacks are a common cause of death worldwide and especially in Scotland. Heart attacks are usually caused by a sudden blockage of arteries supplying blood to the heart muscle. Patients with a sudden blocked artery need rapid diagnosis, to receive treatment quickly such as being taken directly to hospital. Patients with a blocked artery are therefore ‘critical’ while other patients with a partial blocked arteries are ‘non-critical’ and can safely wait for evaluation of their blocked arteries.
Currently, there is a reliance on older technology to help make the diagnosis such as Heart attacks are a common cause of death worldwide and especially in Scotland. Heart attacks are often caused by a sudden blockage of arteries supplying blood to the heart muscle. Patients with a sudden blocked artery need rapid diagnosis to receive quick treatment and to be taken directly to a treatment centre to help save their life. Patients with a blocked artery are therefore ‘critical’ while other patients with a partial blocked arteries are ‘non-critical’ and can safely wait for evaluation of their blocked arteries. Currently, there is a reliance on older technology to help make the diagnosis such as a tracing of the heart (electrocardiogram or ECG).
The ECG cannot reliably differentiate between patients with blocked arteries and partially blocked arteries. During coronary artery blockage, various biomarkers (substances in the blood) are released and could differentiate between critical and non-critical heart attacks. However, a range of biomarkers could be more accurate than a single biomarker.In this study we will measure a wide range of biomarkers that may help diagnose critical heart attacks. Our aim is to perform blood tests on patients that will be undergoing tests at the cardiac unit. The overall aim is to improve the accuracy of pre-hospital heart attack diagnosis and management.
Summary of Results
Background: Prompt recognition and treatment of complete blockage of arteries supplying the heart (i.e.larger heart attack) is essential, yet current pathways miss a proportion of patients who have larger heart attacks as not all have changes on their heart tracing. This study aimed to determine if analysis of a large number of proteins combined with patient history, examination and other bedside tests could improve diagnostic accuracy in patients with larger heart attacks.
Methods: In this study 368 proteins were analysed from patients having a heart attack and controls with patients not having a heart attack. Data from pictures looking at the arteries (angiography) and clinical features were recorded. Proteins were analysed using a special test called the proximity extension assay. Machine-learning techniques of hybrid and forward feature selection algorithms followed by comparing decision tree and logistical regression analysis were used to indicate the optimal combination proteins and clinical factors to increase diagnostic yield of larger heart attacks.
Results: Blood samples were obtained from 130 patients, 41 (31.5%) had a non-larger heart attack and 16 (12.3%) had a larger heart attack. The other 73 (56.2%) had stable angina with no evidence of a heart attack. A combination of 19 clinical features and 87 biomarkers for a larger heart attacked gave a detection of rate of 0.90 which was higher than identification of a larger heart attack by clinical features alone (0.84) although similar to biomarkers alone (AUC=0.91). The decision tree machine learing technique that included combination of biomarkers and clinical factors reached statistical significance for detection for larger heart attack (p<0.001) compared to the logistical regression machine learning method.
Conclusion: In this study we created a technique for the diagnosis of larger heart attacks through a combination of clinical factors and proteins following proteomic analysis. Further refinement with larger number of patients and focused with the findings from this study are required for validation.
REC name
East of Scotland Research Ethics Service REC 2
REC reference
19/ES/0091
Date of REC Opinion
24 Sep 2019
REC opinion
Further Information Favourable Opinion