Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.
Eur Radiol
; 31(3): 1227-1235, 2021 Mar.
Article
em En
| MEDLINE
| ID: mdl-32880697
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Doença da Artéria Coronariana
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Estenose Coronária
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Reserva Fracionada de Fluxo Miocárdico
Tipo de estudo:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article