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Machine learning–based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography

  • Cardiac
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Abstract

Objectives

Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.

Methods

This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke.

Results

Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years’ follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35–3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618).

Conclusion

ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.

Clinical relevance statement

In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning–based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis.

Key Points

• The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods.

• We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification.

• The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography.

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Abbreviations

AUC:

Area under the curve

BNP:

B-type natriuretic peptide

CAD:

Coronary artery disease

CAC:

Coronary artery calcium

CONFIRM:

Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter

CI:

Confidence interval

CTA:

Computed tomography angiography

CT:

Computed tomography

CVD:

Cerebral vascular disease

eGFR:

Estimated glomerular filtration rate

HR:

Hazard ratio

ICA:

Invasive coronary angiography

IDI:

Integrated discrimination improvement

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

LVESD:

Left ventricular end-systolic diameter

ML:

Machine learning

MLP:

Multilayer perceptron

NRI:

Net reclassification improvement

RCS:

Restricted cubic splines

RBC:

Red blood cell

RF:

Random forest

ROC:

Receiver operating characteristic

SHAP:

SHapley Additive exPlanations

SVM:

Support vector machine

XGB:

Extreme gradient boosting

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Funding

This study was funded by National Natural Science Foundation of China (No. 81970291 and No. 82170344).

Author information

Authors and Affiliations

  1. Center for Coronary Artery Disease, Bei**g Anzhen Hospital of Capital Medical University, Bei**g, China

    Wen Jian, Xueqian Shen, Ze Zheng, Zheng Wu, Yuchen Shi & **ghua Liu

  2. Bei**g Anzhen Hospital of Capital Medical University and Bei**g Institute of Heart Lung and Blood Vessel Diseases, Bei**g, China

    Zhujun Dong, Yingchun Han & Jie Du

Authors
  1. Wen Jian
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  4. Ze Zheng
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  6. Yuchen Shi
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  7. Yingchun Han
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  9. **ghua Liu
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Corresponding author

Correspondence to **ghua Liu.

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Guarantor

The scientific guarantor of this publication is **ghua Liu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

No study subject or cohort overlap reported.

Methodology

• Retrospective

• Observational

• Performed at one institution

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Jian, W., Dong, Z., Shen, X. et al. Machine learning–based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10629-3

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