Browsing by Author "Han, Donghee"

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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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    Myocardial extracellular volume measurement using cardiac computed tomography
    (Springer, 2024) Muthalaly, Rahul G.; Abrahams, Timothy; Lin, Andrew; Patel, Kush; Tan, Sean; Dey, Damini; Han, Donghee; Tamarappoo, Balaji K.; Nicholls, Stephen J; Nerlekar, Nitesh; Medicine, School of Medicine
    Myocardial fibrosis is a common endpoint of many cardiac diseases and increasingly recognized as a predictor of heart failure, arrhythmia, and death. Recent studies have utilised cardiac computed tomography (CT) scans with delayed phase imaging to quantify diffuse fibrosis of the myocardium. CT extracellular volume (CT-ECV) measurement correlates well with CMR and histological myocardial fibrosis. Furthermore, CT-ECV predicts outcomes such as death, heart failure and arrhythmia in various disease states. This review summarizes the rationale and methodology behind CT-ECV measurement and provides a detailed summary of the current clinical evidence for the use of CT-ECV.
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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.
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    Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve
    (Elsevier, 2023) Han, Donghee; van Diemen, Pepijn; Kuronuma, Keiichiro; Lin, Andrew; Motwani, Manish; McElhinney, Priscilla; Flores Tomasino, Guadalupe; Park, Caroline; Kwan, Alan; Tzolos, Evangelos; Klein, Eyal; Grodecki, Kajetan; Shou, Benjamin; Tamarappoo, Balaji; Cadet, Sebastien; Danad, Ibrahim; Driessen, Roel S.; Berman, Daniel S.; Slomka, Piotr J.; Dey, Damini; Knaapen, Paul; Medicine, School of Medicine
    Background: Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). Methods: This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 ​± ​8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. Results: The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p ​< ​0.001). Women had a smaller burden of all plaque subtypes (all p ​< ​0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p ​= ​0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06-1.24, p ​= ​0.001; Women, OR: 1.96, 95%CI: 1.11-3.46, p ​= ​0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p ​= ​0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. Conclusions: In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.