髙橋 愛

BACKGROUND: Extracellular volume fraction (ECV) on magnetic resonance imaging can predict prognosis after aortic valve replacement in patients with aortic stenosis (AS). However, the usefulness of ECV on computed tomography (CT) for patients who have undergone transcatheter aortic valve replacement (TAVR) is unclear, so we investigated whether ECV analysis on CT is associated with clinical outcomes in TAVR candidates.Methods and Results: We analyzed 127 patients with severe AS who underwent preoperative CT for TAVR. We evaluated the utility of ECV analysis on single-energy CT for predicting patient prognosis after TAVR. The primary outcome was a composite of all-cause death and hospitalization due to heart failure (HF) after TAVR. 15 patients (12%) had composite outcomes: 4 deaths and 11 hospitalizations due to HF. In multivariate survival analysis using the Cox proportional hazard model, atrial fibrillation (AF) (hazard ratio (HR), 7.86; 95% confidence interval (CI), 2.57-24.03; P<0.001), history of congestive HF (HR, 4.91; 95% CI, 1.49-16.2; P=0.009) and ECV ≥32.6% on CT (HR, 6.96; 95% CI, 1.92-25.12; P=0.003) were independent predictors of composite outcomes. On Kaplan-Meier analysis, the higher ECV group (≥32.6%) had a significantly greater number of composite outcomes than the lower ECV group (P<0.001). CONCLUSIONS: ECV on CT is an independent predictor of prognosis after TAVR.

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accuracy of FFR-CT analysis may potentially be decreased by severely calcified coronary arteries because of blooming and beam hardening artifacts. The aim of this study was to evaluate the utility of deep learning (DL)-based coronary computed tomography (CT) data analysis in predicting invasive fractional flow reserve (FFR), especially in cases with severely calcified coronary arteries. We analyzed 184 consecutive cases (241 coronary arteries) which underwent coronary CT and invasive coronary angiography, including invasive FFR, within a three-month period. Mean coronary artery calcium scores were 963 ± 1226. We evaluated and compared the vessel-based diagnostic accuracy of our proposed DL model and a visual assessment to evaluate functionally significant coronary artery stenosis (invasive FFR < 0.80). A deep neural network was trained with consecutive short axial images of coronary arteries on coronary CT. Ninety-one coronary arteries of 89 cases (48%) had FFR-positive functionally significant stenosis. On receiver operating characteristics (ROC) analysis to predict FFR-positive stenosis using the trained DL model, average area under the curve (AUC) of the ROC curve was 0.756, which was superior to the AUC of visual assessment of significant (≥ 70%) coronary artery stenosis on CT (0.574, P = 0.011). The sensitivity, specificity, positive and negative predictive value (PPV and NPV), and accuracy of the DL model and visual assessment for detecting FFR-positive stenosis were 82 and 36%, 68 and 78%, 59 and 48%, 87 and 69%, and 73 and 63%, respectively. Sensitivity and NPV for the prediction of FFR-positive stenosis were significantly higher with our DL model than visual assessment (P = 0.0004, and P = 0.024). DL-based coronary CT data analysis has a higher diagnostic accuracy for functionally significant coronary artery stenosis than visual assessment.