Abstract
Background
Machine learning (ML) models of risk prediction with coronary artery calcium (CAC)
and CAC characteristics exhibit high performance, but are not inherently interpretable.
Objectives
To determine the direction and magnitude of impact of CAC characteristics on 10-year
all-cause mortality (ACM) with explainable ML.
Methods
We analyzed asymptomatic subjects in the CAC consortium. We trained ML models on 80%
and tested on 20% of the data with XGBoost, using clinical characteristics + CAC
(ML 1) and additional CAC characteristics of CAC density and number of calcified vessels
(ML 2). We applied SHAP, an explainable ML tool, to explore the relationship of CAC
and CAC characteristics with 10-year all-cause and CV mortality.
Results
2376 deaths occurred among 63,215 patients [68% male, median age 54 (IQR 47–61), CAC
3 (IQR 0–94.3)]. ML2 was similar to ML1 to predict all-cause mortality (Area Under
the Curve (AUC) 0.819 vs 0.821, p = 0.23), but superior for CV mortality (0.847
vs 0.845, p = 0.03).
Low CAC density increased mortality impact, particularly ≤0.75. Very low CAC density
≤0.75 was present in only 4.3% of the patients with measurable density, and 75% occurred
in CAC1-100. The number of diseased vessels did not increase mortality overall when
simultaneously accounting for CAC and CAC density.
Conclusion
CAC density contributes to mortality risk primarily when it is very low ≤0.75, which
is primarily observed in CAC 1–100. CAC and CAC density are more important for mortality
prediction than the number of diseased vessels, and improve prediction of CV but not
all-cause mortality. Explainable ML techniques are useful to describe granular relationships
in otherwise opaque prediction models.
Keywords
Abbreviations:
CAC (Coronary Artery Calcium), ML (Machine Learning), SHAP (SHapley Additive exPlanations), AUC (Area under the receiver operating curve), NRI (Net reclassification index), IDI (Integrated discrimination index)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 11, 2022
Accepted:
October 28,
2022
Received in revised form:
September 15,
2022
Received:
May 13,
2022
Identification
Copyright
© 2022 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.
