CT Coronary Angiography identifies plaque characteristics and distinguishes Type 1 and 2 MI

Written By : Aditi |Medically Reviewed By : Dr. Kamal Kant Kohli Published On 2022-12-07T11:00:41+05:30 | Updated On 7 Dec 2022 12:51 PM IST

SCOTLAND: A recent study published in RADIOLOGY: CARDIOTHORACIC IMAGING, RSNA has concluded that quantitative CT Coronary Angiography (CTCA) aids in identifying important plaque characteristics and discriminates between type 1 and type 2 myocardial infarction.

The etiology of Type 1 myocardial infarction is related to the disruption of atherosclerotic plaque, coronary thrombosis, and vessel occlusion. In comparison, type 2 myocardial infarction occurs due to a mismatch between myocardial oxygen supply and demand. Differentiating both types is a common clinical problem and is challenging to resolve. It is essential to differentiate the two to implement correct diagnoses, successful treatment strategies, and improved patient outcomes.

Dual antiplatelet therapy is essential in type 1 myocardial infarction, while the same is harmful in type 2 myocardial infarction when occult bleeding and anemia are contributing factors. A significant clinical benefit could be achieved when a non-invasive test can differentiate type 1 and type 2 MI.

CT coronary angiography (CTCA) can assess the severity of coronary artery stenosis and plaque characteristics. CT can distinguish between type 1 and type 2 myocardial infarction by identifying and quantifying high-risk low-attenuation plaque.

Considering this, a study was conducted by a team of researchers led by Dr. Miah from the British Heart Foundation Centre of Cardiovascular Science to describe the differences in CT-defined coronary atherosclerosis in patients with acute chest pain. The study also aimed to determine whether quantitative measurement of plaque composition by CT angiographic measures can differentiate between type 1 and type 2 myocardial infarction.

The critical points of the study are:

• The study was a secondary analysis of two prospective studies.

• The study involved quantitative plaque analysis based on CTCA fndings in participants with type 1 and type 2 MI and chest pain without MI.

• Logistic regression analyses were performed to identify predictors of type 1 MI.

• In the RAPID-CTCA trial, 155 participants had type 1 MI, and 136 had chest pain without MI.

• From the DEMAND-MI trial, 36 participants had type 2 MI.

• Semiautomated software was used to perform plaque analysis by a trained observer.

• The volume measurement was done for total, non-calcified, calcified, and low-attenuation plaque subtypes.

• The total plaque type was more significant in Type 1 MI, 44 %, compared to type 1 MI, 35 %.

• The non-calcified plaque type in Type 1 MI was 39 %, and 34 % in Type 2 MI.

• The low-attenuation plaque type in Type 1 MI was 4.15 %, while in Type 2 MI was 1.64 %.

• The P value for all was < .001.

• Participants with Type 2 MI had similar low-attenuation plaque burden to those with chest pain without MI with a P value of 0 .4.

• Low-attenuation plaque was an independent predictor of type 1 MI with an adjusted odds ratio of 3.44 and a P value was < .001).

The study reported low-attenuation plaque burden as a vital distinction between type 1 and 2 Myocardial Infarction, independent of the severity of coronary stenosis or clinical characteristics, with an adjusted odds ratio of 3.44 and a P value was < .001.

Quantitative plaque analysis differentiates between type 1 and 2 MI, they wrote.

Dr. Anda said, "We demonstrated coronary artery plaque characteristics quantitatively at CTCA differing between Type 1 and 2 MI. They wrote, we suggest that quantitative CT plaque analysis promisingly discriminates between type 1 and type 2 MI and potentially informs the clinical management of MI patients of uncertain cause.

The study highlighted that coronary artery disease in Type 2 MI is predominantly stable and specificity of a high burden of low-attenuation plaque to Type 1 MI.

The researchers added although a semiautomated process quantifies plaque subtype in 20 minutes, this limit the clinical use of this approach. Automation and machine learning algorithms are rapid and valuable in plaque quantification.

Future prospective studies are required for the validation of the technique.

BDS, MDS in Periodontics and Implantology

Dr. Aditi Yadav is a BDS, MDS in Periodontics and Implantology. She has a clinical experience of 5 years as a laser dental surgeon. She also has a Diploma in clinical research and pharmacovigilance and is a Certified data scientist. She is currently working as a content developer in e-health services. Dr. Yadav has a keen interest in Medical Journalism and is actively involved in Medical Research writing.

Dr. Kamal Kant Kohli

Dr Kamal Kant Kohli-MBBS, DTCD- a chest specialist with more than 30 years of practice and a flair for writing clinical articles, Dr Kamal Kant Kohli joined Medical Dialogues as a Chief Editor of Medical News. Besides writing articles, as an editor, he proofreads and verifies all the medical content published on Medical Dialogues including those coming from journals, studies,medical conferences,guidelines etc. Email: drkohli@medicaldialogues.in. Contact no. 011-43720751