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Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score

  • Cardiac
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Abstract

Objectives

The machine learning ischemia risk score (ML-IRS) is a machine learning–based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA.

Methods

This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined.

Results

The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4–34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1–55.0) versus 13.0% (4.5–29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65–0.72) to 0.78 (95% CI: 0.75–0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503–0.769; p < 0.0001).

Conclusions

ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization.

Key Points

• Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization.

• The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8.

• The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.

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Abbreviations

BMI:

Body mass index

CAD:

Coronary artery disease

CCS:

Coronary calcium score

CCTA:

Coronary CT angiography

CDD:

Contrast density difference

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

ML-IRS:

Machine learning ischemia risk score

NRI:

Continuous net reclassification improvement

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Acknowledgments

Alan Kwan acknowledges funding support provided by the National Institutes of Health (Grant T32HL116273).

Damini Dey acknowledges funding support provided by the National Institutes of Health (Grants 1R01HL148787-01A1 and 1R01HL133616).

Funding

The authors state that this work has received funding from 1R01HL148787-01A1.

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Correspondence to Damini Dey.

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Guarantor

The scientific guarantor of this publication is Damini Dey.

Conflict of interest

Sebastien Cadet, Piotr J. Slomka, Daniel S. Berman, and Damini Dey may receive software royalties from Cedars-Sinai Medical Center. Piotr J. Slomka, Daniel S. Berman, and Damini Dey hold a patent related to the plaque characterization.

Alan C. Kwan, Priscilla A. McElhinney, Balaji K. Tamarappoo, Cecilia Hurtado, Robert J.H. Miller, Donghee Han, Yuka Otaki, Evann Eisenberg, Joseph E. Ebinger, and Victor Y. Cheng 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.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• Post hoc analysis of a multicenter registry

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Kwan, A.C., McElhinney, P.A., Tamarappoo, B.K. et al. Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score. Eur Radiol 31, 1227–1235 (2021). https://doi.org/10.1007/s00330-020-07142-8

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  • DOI: https://doi.org/10.1007/s00330-020-07142-8

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