Browsing by Subject "Coronary revascularization"

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    Association Between Race/Ethnicity and Income on the Likelihood of Coronary Revascularization Among Postmenopausal Women with Acute Myocardial Infarction: Women’s Health Initiative Study
    (Elsevier, 2022) Tertulien, Tarryn; Roberts, Mary B.; Eaton, Charles B.; Cene, Crystal W.; Corbie-Smith, Giselle; Manson, JoAnn E.; Allison, Matthew; Nassir, Rami; Breathett, Khadijah; Medicine, School of Medicine
    Background: Historically, race, income, and gender were associated with likelihood of receipt of coronary revascularization for acute myocardial infarction (AMI). Given public health initiatives such as Healthy People 2010, it is unclear whether race and income remain associated with the likelihood of coronary revascularization among women with AMI. Methods: Using the Women's Health Initiative Study, hazards ratio (HR) of revascularization for AMI was compared for Black and Hispanic women vs White women and among women with annual income <$20,000/year vs ≥$20,000/year over median 9.5 years follow-up(1993-2019). Proportional hazards models were adjusted for demographics, comorbidities, and AMI type. Results were stratified by revascularization type: percutaneous coronary intervention and coronary artery bypass grafting(CABG). Trends by race and income were compared pre- and post-2010 using time-varying analysis. Results: Among 5,284 individuals with AMI (9.5% Black, 2.8% Hispanic, and 87.7% White; 23.2% <$20,000/year), Black race was associated with lower likelihood of receiving revascularization for AMI compared to White race in fully adjusted analyses [HR:0.79(95% Confidence Interval:[CI]0.66,0.95)]. When further stratified by type of revascularization, Black race was associated with lower likelihood of percutaneous coronary intervention for AMI compared to White race [HR:0.72(95% CI:0.59,0.90)] but not for CABG [HR:0.97(95%CI:0.72,1.32)]. Income was associated with lower likelihood of revascularization [HR:0.90(95%CI:0.82,0.99)] for AMI. No differences were observed for other racial/ethnic groups. Time periods (pre/post-2010) were not associated with change in revascularization rates. Conclusion: Black race and income remain associated with lower likelihood of revascularization among patients presenting with AMI. There is a substantial need to disrupt the mechanisms contributing to race, sex, and income disparities in AMI management.
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    Prediction of Revascularization by Coronary CT Angiography using a Machine Learning Ischemia Risk Score
    (Springer, 2021) Kwan, Alan C.; McElhinney, Priscilla A.; Tamarappoo, Balaji K.; Cadet, Sebastien; Hurtado, Cecilia; Miller, Robert J. H.; Han, Donghee; Otaki, Yuka; Eisenberg, Evann; Ebinger, Joseph E.; Slomka, Piotr J.; Cheng, Victor Y.; Berman, Daniel S.; Dey, Damini; Radiation Oncology, School of Medicine
    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.