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Risk predicting for acute coronary syndrome based on machine learning model with kinetic plaque features from serial
Yabin Wang1, Haiwei Chen2, Ting Sun1
1Department of Geriatric Cardiology & National Clinical Research Center for Geriatric Diseases, Second Medical Center of Chinese PLA General Hospital, 28# Fuxing road, Haidian district, Beijing 100853, China.
Insights
Dynamic changes in coronary artery plaque features, including CT-FFR and necrotic core, predict acute coronary syndrome (ACS) events. A machine learning model integrating these features enhances ACS risk prediction.
Area of Science:
- Cardiovascular Imaging
- Interventional Cardiology
- Machine Learning in Medicine
Background:
- Coronary computed tomography angiography (CCTA) is increasingly used to evaluate suspected coronary artery disease.
- The prospective relationship between CCTA-identified plaque characteristics and acute coronary syndrome (ACS) events remains underexplored.
Purpose of the Study:
- To investigate the prospective association of coronary plaque features, assessed by serial CCTA, with ACS events.
- To develop and evaluate a machine learning model for predicting ACS risk based on plaque characteristics.
Main Methods:
- A case-control study involving 101 ACS patients and 101 matched controls who underwent serial CCTA.
- Analysis of anatomical, compositional, and hemodynamic plaque parameters, including CCTA-derived fractional flow reserve (CT-FFR), between baseline and follow-up scans.
- Development of an XGBoost machine learning model using key plaque features to predict ACS events.
Main Results:
- No significant differences in baseline plaque parameters were observed between ACS and control groups.
- Culprit lesions in ACS patients showed significant increases in luminal stenosis, remodelling index, and necrotic core, alongside decreases in CT-FFR and calcium ratio on follow-up CCTA.
- The XGBoost model demonstrated high predictive ability for ACS events (AUC 0.918).
Conclusions:
- Dynamic changes in coronary plaque features are strongly associated with subsequent ACS events.
- Integrating plaque characteristics like CT-FFR, necrotic core, and remodelling index into a machine learning model significantly improves ACS risk prediction.
Aims:
More patients with suspected coronary artery disease underwent coronary computed tomography angiography (CCTA) as gatekeeper. However, the prospective relation of plaque features to acute coronary syndrome (ACS) events has not been previously explored.
Methods And Results:
One hundred and one out of 452 patients with documented ACS event and received more than once CCTA during the past 12 years were recruited. Other 101 patients without ACS event were matched as case control. Baseline, follow-up, and changes of anatomical, compositional, and haemodynamic parameters [e.g. luminal stenosis, plaque volume, necrotic core, calcification, and CCTA-derived fractional flow reserve (CT-FFR)] were analysed by independent CCTA measurement core laboratories. Baseline anatomical, compositional, and haemodynamic parameters of lesions showed no significant difference between the two cohorts (P > 0.05). While the culprit lesions exhibited significant increase of luminal stenosis (10.18 ± 2.26% vs. 3.62 ± 1.41%, P = 0.018), remodelling index (0.15 ± 0.14 vs. 0.09 ± 0.01, P < 0.01), and necrotic core (4.79 ± 1.84% vs. 0.43 ± 1.09%, P = 0.019) while decrease of CT-FFR (-0.05 ± 0.005 vs. -0.01 ± 0.003, P < 0.01) and calcium ratio (-4.28 ± 2.48% vs. 4.48 ± 1.46%, P = 0.004) between follow-up CCTA and baseline scans in comparison to that of non-culprit lesion. The XGBoost model comprising the top five important plaque features revealed higher predictive ability (area under the curve 0.918, 95% confidence interval 0.861-0.968).
Conclusions:
Dynamic changes of plaque features are highly relative with subsequent ACS events. The machine learning model of integrating these lesion characteristics (e.g. CT-FFR, necrotic core, remodelling index, plaque volume, and calcium) can improve the ability for predicting risks of ACS events.
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