Browsing by Author "Eisenberg, Evann"

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    Computed tomography angiography-derived extracellular volume fraction predicts early recovery of left ventricular systolic function after transcatheter aortic valve replacement
    (Oxford University Press, 2021) Han, Donghee; Tamarappoo, Balaji; Klein, Eyal; Tyler, Jeffrey; Chakravarty, Tarun; Otaki, Yuka; Miller, Robert; Eisenberg, Evann; Park, Rebekah; Singh, Siddharth; Shiota, Takahiro; Siegel, Robert; Stegic, Jasminka; Salseth, Tracy; Cheng, Wen; Dey, Damini; Thomson, Louise; Berman, Daniel; Makkar, Raj; Friedman, John; Radiation Oncology, School of Medicine
    Aims: Recovery of left ventricular ejection fraction (LVEF) after aortic valve replacement has prognostic importance in patients with aortic stenosis (AS). The mechanism by which myocardial fibrosis impacts LVEF recovery in AS is not well characterized. We sought to evaluate the predictive value of extracellular volume fraction (ECV) quantified by cardiac CT angiography (CTA) for LVEF recovery in patients with AS after transcatheter aortic valve replacement (TAVR). Methods and results: In 109 pre-TAVR patients with LVEF <50% at baseline echocardiography, CTA-derived ECV was calculated as the ratio of change in CT attenuation of the myocardium and the left ventricular (LV) blood pool before and after contrast administration. Early LVEF recovery was defined as an absolute increase of ≥10% in LVEF measured by post-TAVR follow-up echocardiography within 6 months of the procedure. Early LVEF recovery was observed in 39 (36%) patients. The absolute increase in LVEF was 17.6 ± 8.8% in the LVEF recovery group and 0.9 ± 5.9% in the no LVEF recovery group (P < 0.001). ECV was significantly lower in patients with LVEF recovery compared with those without LVEF recovery (29.4 ± 6.1% vs. 33.2 ± 7.7%, respectively, P = 0.009). In multivariable analysis, mean pressure gradient across the aortic valve [odds ratio (OR): 1.07, 95% confidence interval (CI): 1.03-1.11, P: 0.001], LV end-diastolic volume (OR: 0.99, 95% CI: 0.98-0.99, P: 0.035), and ECV (OR: 0.92, 95% CI: 0.86-0.99, P: 0.018) were independent predictors of early LVEF recovery. Conclusion: Increased myocardial ECV on CTA is associated with impaired LVEF recovery post-TAVR in severe AS patients with impaired LV systolic function.
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    Differences in Prognostic Value of Myocardial Perfusion SPECT using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multi-Center Study
    (American Heart Association, 2022) Tamarappoo, Balaji K.; Otaki, Yuka; Sharir, Tali; Hu, Lien-Hsin; Gransar, Heidi; Einstein, Andrew J.; Fish, Mathews B.; Ruddy, Terrence D.; Kaufmann, Philipp; Sinusas, Albert J.; Miller, Edward J.; Bateman, Timothy M.; Dorbala, Sharmila; Di Carli, Marcelo; Eisenberg, Evann; Liang, Joanna X.; Dey, Damini; Berman, Daniel S.; Slomka, Piotr J.; Medicine, School of Medicine
    Background: Semiquantitative assessment of stress myocardial perfusion defect has been shown to have greater prognostic value for prediction of major adverse cardiac events (MACE) in women compared with men in single-center studies with conventional single-photon emission computed tomography (SPECT) cameras. We evaluated sex-specific difference in the prognostic value of automated quantification of ischemic total perfusion defect (ITPD) and the interaction between sex and ITPD using high-efficiency SPECT cameras with solid-state detectors in an international multicenter imaging registry (REFINE SPECT [Registry of Fast Myocardial Perfusion Imaging With Next-Generation SPECT]). Methods: Rest and exercise or pharmacological stress SPECT myocardial perfusion imaging were performed in 17 833 patients from 5 centers. MACE was defined as the first occurrence of death or myocardial infarction. Total perfusion defect (TPD) at rest, stress, and ejection fraction were quantified automatically by software. ITPD was given by stressTPD-restTPD. Cox proportional hazards model was used to evaluate the association between ITPD versus MACE-free survival and expressed as a hazard ratio. Results: In 10614 men and 7219 women, with a median follow-up of 4.75 years (interquartile range, 3.7-6.1), there were 1709 MACE. In a multivariable Cox model, after adjusting for revascularization and other confounding variables, ITPD was associated with MACE (hazard ratio, 1.08 [95% CI, 1.05-1.1]; P<0.001). There was an interaction between ITPD and sex (P<0.001); predicted survival for ITPD<5% was worse among men compared to women, whereas survival among women was worse than men for ITPD≥5%, P<0.001. Conclusions: In the international, multicenter REFINE SPECT registry, moderate and severe ischemia as quantified by ITPD from high-efficiency SPECT is associated with a worse prognosis in women compared with men.
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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.