Patient-Level Explainable Machine Learning to Predict Major Adverse Cardiovascular Events from SPECT MPI and CCTA Imaging

Alahdab, Fares; Shawi, Radwa El; Ahmed, Ahmed I.; Han, Yushui; Al-Mallah, Mouaz

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https://hdl.handle.net/2142/121863 Copy

Description

Title

Patient-Level Explainable Machine Learning to Predict Major Adverse Cardiovascular Events from SPECT MPI and CCTA Imaging

Author(s)

• Alahdab, Fares
• Shawi, Radwa El
• Ahmed, Ahmed I.
• Han, Yushui
• Al-Mallah, Mouaz

Issue Date

2023

Keyword(s)

• Coronary artery disease
• Coronary computed tomography angiography
• Single photon emission computed tomography
• Machine learning
• Explainable AI
• Automated machine learning

Abstract

Background: Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including single-photon emission computed tomography (SPECT) and coronary computed tomography angiography (CCTA). However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict major adverse cardiovascular events (MACE) using clinical, CCTA, and SPECT data. Methods: Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected coronary artery disease (CAD) were included and followed up for MACEs. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Results: A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CTA and ischemia on SPECT were observed in 14% of patients. ML prediction’s sensitivity, specificity, and diagnostic accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). Conclusion: ML can accurately predict risk of developing adverse events (MACE) in patients suspected of ischemic heart disease (CAD) undergoing SPECT and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.

Type of Resource

text

Language

eng

Handle URL

https://hdl.handle.net/2142/121863

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