Low- or standard-dose edoxaban versus antiplatelet therapy for leaflet thrombus and cerebral thromboembolism after TAVR: A prespecified analysis of randomized ADAPT-TAVR trial.

BACKGROUND: The risks of leaflet thrombosis and the associated cerebral thromboembolism are unknown according to different anticoagulation dosing after transcatheter aortic valve replacement (TAVR). The aim was to evaluate the incidence of leaflet thrombosis and cerebral thromboembolism between low-dose (30 mg) or standard-dose (60 mg) edoxaban and dual antiplatelet therapy (DAPT) after TAVR. METHODS: In this prespecified subgroup analysis of the ADAPT-TAVR trial, the primary endpoint was the incidence of leaflet thrombosis on 4-dimensional computed tomography at 6-months. Key secondary endpoints were new cerebral lesions on brain magnetic resonance imaging and neurological and neurocognitive dysfunction. RESULTS: Of 229 patients enrolled in this study, 118 patients were DAPT group and 111 were edoxaban group (43 [39.1%] 60 mg vs 68 [61.3%] 30 mg). There was a significantly lower incidence of leaflet thrombosis in the standard-dose edoxaban group than in the DAPT group (2.4% vs 18.3%; odds ratio [OR] 0.11; 95% confidence interval [CI], 0.01-0.55; P = .03). However, no significant difference was observed between low-dose edoxaban and DAPT (15.0% vs 18.3%; OR 0.79; 95% CI, 0.32-1.81; P = .58). Irrespective of different antithrombotic regiments, the percentages of patients with new cerebral lesions on brain MRI and worsening neurological or neurocognitive function were not significantly different. CONCLUSIONS: In patients without an indication for anticoagulation after TAVR, the incidence of leaflet thrombosis was significantly lower with standard-dose edoxaban but not with low-dose edoxaban, as compared with DAPT. However, this differential effect of edoxaban on leaflet thrombosis was not associated with a reduction of new cerebral thromboembolism and neurological dysfunction.

Predictors of Late Adverse Events in Patients with Surgically Treated Type I Aortic Dissection.

OBJECTIVE: Residual aortic dissection (AD) following DeBakey type I AD repair is associated with a high rate of adverse events that need additional intervention or surgery. This study aimed to identify clinical and early post-operative computed tomography (CT) imaging factors associated with adverse events in patients with type I AD after ascending aorta replacement. METHODS: This single centre, retrospective cohort study included consecutive patients with type I AD who underwent ascending aorta replacement from January 2011 to December 2017 and post-operative CT within three months. The primary outcome was AD related adverse events, defined as AD related death and re-operation due to aortic aneurysm or impending rupture. The location and size of the primary intimal tears, aortic diameter, and false lumen status were evaluated. Regression analyses were performed to identify factors associated with AD related adverse events. A decision tree model was used to classify patients as high or low risk. RESULTS: Of 103 participants (55.43 ± 13.94 years; 49.5% male), 24 (23.3%) experienced AD related adverse events. In multivariable Cox regression analysis, connective tissue disease (hazard ratio [HR] 15.33; p < .001), maximum aortic diameter ≥ 40 mm (HR 4.90; p < .001), and multiple (three or more) intimal tears (HR 7.12; p < .001) were associated with AD related adverse events. The three year cumulative survival free from AD related events was lower in the high risk group with aortic diameter ≥ 40 mm and multiple intimal tears (41.7% vs. 90.9%; p < .001). CONCLUSION: Early post-operative CT findings indicating a maximum aortic diameter ≥ 40 mm and multiple intimal tears may predict a higher risk of adverse events. These findings suggest the need for careful monitoring and more vigilant management approaches in these cases.

Tailored Planning of Surgical Myectomy in Obstructive Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a genetic myocardial disease characterized by abnormal thickening of the myocardium caused by myocardial disarray and interstitial fibrosis. HCM is associated with sudden cardiac-related events, such as ventricular fibrillation, tachycardia, and syncope. Moreover, left ventricular or midcavity obstruction due to the thickened myocardium can result in severe heart failure and mortality in patients with HCM. Surgical myectomy is a standard treatment option for patients with symptomatic obstructive HCM; however, it is a complex procedure that requires careful planning and execution to avoid complications, such as residual flow obstruction, persistent obliteration of the left ventricular cavity in systole, or iatrogenic ventricular septal defects. Therefore, a thorough understanding of the mechanics of HCM and precise evaluation of the location and extent of the hypertrophic myocardium to be removed are crucial for preoperative planning. Multiphase cardiac CT postprocessing is important for preoperative evaluation and planning of surgical myectomy in patients with HCM. In this review, the authors highlight use of multiphase cardiac CT with step-by-step postprocessing methods to simulate successful surgical myectomy. The transaortic surgeon's view on end-diastolic phase images accurately represents the surgical field. Moreover, myocardial segmentation can be used to generate volume-rendered images and three-dimensional printing. CT evaluation can also assist in identifying concurrent abnormalities, such as mitral valve or papillary muscle abnormalities. In addition to CT, other imaging modalities for preoperative evaluation of HCM and postmyectomy evaluation methods are presented. ©RSNA, 2023 Test Your Knowledge questions in the supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

Edoxaban Versus Dual Antiplatelet Therapy for Leaflet Thrombosis and Cerebral Thromboembolism After TAVR: The ADAPT-TAVR Randomized Clinical Trial.

BACKGROUND: It is unknown whether the direct oral anticoagulant edoxaban can reduce leaflet thrombosis and the accompanying cerebral thromboembolic risk after transcatheter aortic valve replacement. In addition, the causal relationship of subclinical leaflet thrombosis with cerebral thromboembolism and neurological or neurocognitive dysfunction remains unclear. METHODS: We conducted a multicenter, open-label randomized trial comparing edoxaban with dual antiplatelet therapy (aspirin plus clopidogrel) in patients who had undergone successful transcatheter aortic valve replacement and did not have an indication for anticoagulation. The primary end point was an incidence of leaflet thrombosis on 4-dimensional computed tomography at 6 months. Key secondary end points were the number and volume of new cerebral lesions on brain magnetic resonance imaging and the serial changes of neurological and neurocognitive function between 6 months and immediately after transcatheter aortic valve replacement. RESULTS: A total of 229 patients were included in the final intention-to-treat population. There was a trend toward a lower incidence of leaflet thrombosis in the edoxaban group compared with the dual antiplatelet therapy group (9.8% versus 18.4%; absolute difference, -8.5% [95% CI, -17.8% to 0.8%]; P=0.076). The percentage of patients with new cerebral lesions on brain magnetic resonance imaging (edoxaban versus dual antiplatelet therapy, 25.0% versus 20.2%; difference, 4.8%; 95% CI, -6.4% to 16.0%) and median total new lesion number and volume were not different between the 2 groups. In addition, the percentages of patients with worsening of neurological and neurocognitive function were not different between the groups. The incidence of any or major bleeding events was not different between the 2 groups. We found no significant association between the presence or extent of leaflet thrombosis with new cerebral lesions and a change of neurological or neurocognitive function. CONCLUSIONS: In patients without an indication for long-term anticoagulation after successful transcatheter aortic valve replacement, the incidence of leaflet thrombosis was numerically lower with edoxaban than with dual antiplatelet therapy, but this was not statistically significant. The effects on new cerebral thromboembolism and neurological or neurocognitive function were also not different between the 2 groups. Because the study was underpowered, the results should be considered hypothesis generating, highlighting the need for further research. REGISTRATION: URL: https://www. CLINICALTRIALS: gov. Unique identifier: NCT03284827.

Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT.

OBJECTIVES: To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. METHODS: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. RESULTS: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. CONCLUSIONS: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. KEY POINTS: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.

Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness.

OBJECTIVES: To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). METHODS: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. RESULTS: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). CONCLUSIONS: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. KEY POINTS: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mm than with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mm than with LDCT2.5-mm.

Impact of new-onset arrhythmia on cardiac reverse remodeling following transcatheter aortic valve replacement: computed tomography-derived left ventricular and atrial strains.

OBJECTIVE: Patients who undergo transcatheter aortic valve replacement (TAVR) are at risk for new-onset arrhythmia (NOA) that may require permanent pacemaker (PPM) implantation, resulting in decreased cardiac function. We aimed to investigate the factors that are associated with NOA after TAVR and to compare pre- and post-TAVR cardiac functions between patients with and without NOA using CT-derived strain analyses. METHODS: We included consecutive patients who underwent pre- and post-TAVR cardiac CT scans six months after TAVR. New-onset left bundle branch block, atrioventricular block, and atrial fibrillation/flutter lasting over 30 days after the procedure and/or the need for PPM diagnosed within 1 year after TAVR were regarded as NOA. Implant depth and left heart function and strains were analyzed using multi-phase CT images and compared between patients with and without NOA. RESULTS: Of 211 patients (41.7% men; median 81 years), 52 (24.6%) presented with NOA after TAVR, and 24 (11.4%) implanted PPM. Implant depth was significantly deeper in the NOA group than in the non-NOA group (- 6.7 ± 2.4 vs. - 5.6 ± 2.6 mm; p = 0.009). Left ventricular global longitudinal strain (LV GLS) and left atrial (LA) reservoir strain were significantly improved only in the non-NOA group (LV GLS, - 15.5 ± 4.0 to - 17.3 ± 2.9%; p < 0.001; LA reservoir strain, 22.3 ± 8.9 to 26.5 ± 7.6%; p < 0.001). The mean percent change of the LV GLS and LA reservoir strains was evident in the non-NOA group (p = 0.019 and p = 0.035, respectively). CONCLUSIONS: A quarter of patients presented with NOA after TAVR. Deep implant depth on post-TAVR CT scans was associated with NOA. Patients with NOA after TAVR had impaired LV reserve remodeling assessed by CT-derived strains. CLINICAL RELEVANCE STATEMENT: New-onset arrhythmia (NOA) following transcatheter aortic valve replacement (TAVR) impairs cardiac reverse remodeling. CT-derived strain analysis reveals that patients with NOA do not show improvement in left heart function and strains, highlighting the importance of managing NOA for optimal outcomes. KEY POINTS: • New-onset arrhythmia following transcatheter aortic valve replacement (TAVR) is a concern that interferes with cardiac reverse remodeling. • Comparison of pre-and post-TAVR CT-derived left heart strain provides insight into the impaired cardiac reverse remodeling in patients with new-onset arrhythmia following TAVR. • The expected reverse remodeling was not observed in patients with new-onset arrhythmia following TAVR, given that CT-derived left heart function and strains did not improve.

A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation.

OBJECTIVES: Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). METHODS: We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. RESULTS: CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. CONCLUSIONS: The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes. KEY POINTS: • A deep learning-based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated. • Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset. • All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs.

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. METHODS: To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. RESULTS: CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. CONCLUSIONS: CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

Triglyceride glucose index is a useful marker for predicting subclinical coronary artery disease in the absence of traditional risk factors.

BACKGROUND: Atherosclerotic cardiovascular (CV) events commonly occur in individuals with a low CV risk burden. This study evaluated the ability of the triglyceride glucose (TyG) index to predict subclinical coronary artery disease (CAD) in asymptomatic subjects without traditional CV risk factors (CVRFs). METHODS: This retrospective, cross-sectional, and observational study evaluated the association of TyG index with CAD in 1250 (52.8 ± 6.5 years, 46.9% male) asymptomatic individuals without traditional CVRFs (defined as systolic/diastolic blood pressure ≥ 140/90 mmHg; fasting glucose ≥126 mg/dL; total cholesterol ≥240 mg/dL; low-density lipoprotein cholesterol ≥160 mg/dL; high-density lipoprotein cholesterol < 40 mg/dL; body mass index ≥25.0 kg/m2; current smoking; and previous medical history of hypertension, diabetes, or dyslipidemia). CAD was defined as the presence of any coronary plaque on coronary computed tomographic angiography. The participants were divided into three groups based on TyG index tertiles. RESULTS: The prevalence of CAD increased with elevating TyG index tertiles (group I: 14.8% vs. group II: 19.3% vs. group III: 27.6%; P < 0.001). Multivariate logistic regression models showed that TyG index was associated with an increased risk of CAD (odds ratio [OR] 1.473, 95% confidence interval [CI] 1.026-2.166); especially non-calcified (OR 1.581, 95% CI 1.002-2.493) and mixed plaques (OR 2.419, 95% CI 1.051-5.569) (all P < 0.05). The optimal TyG index cut-off for predicting CAD was 8.44 (sensitivity 47.9%; specificity 68.5%; area under the curve 0.600; P < 0.001). The predictive value of this cut-off improved after considering the non-modifiable factors of old age and male sex. CONCLUSIONS: TyG index is an independent marker for predicting subclinical CAD in individuals conventionally considered healthy.

Three-Dimensional Computed Tomographic Analysis of Normal and Aneurysmal Aortic Roots: Is There a Specific Geometric Pattern in the Aortic Root?

Current knowledge of the aortic root geometric anatomy and its surgical implications remain limited. We analyzed multiple predefined parameters of the aortic root to increase our understanding of the geometric changes that occur in normal and aneurysmal transformations. Between November 2003 and September 2015, the aortic roots of 107 healthy subjects (control group) and 105 annuloaortic ectasia (AAE) patients (AAE group) were analyzed using multiplanar reformatted computed tomographic images. The intercommissural distance (ICD), sinus width (SW), and sinus volume (SV) of the left (LCS), right (RCS), and noncoronary sinuses (NCS) of Valsalva were adopted as study parameters. In the control group, all study parameters of the LCS were smaller than those of the RCS and the NCS. In the AAE group, all parameters of the LCS were significantly smaller than those of the RCS or NCS, but the RCS and NCS parameters were similar. Proportionately less LCS enlargement relative to either the RCS or NCS was observed in root aneurysm(AAE group) than in the control group. We observed a distinct aortic root geometric pattern which was characterized by the LCS being smaller than either the RCS or NCS, while the latter were similar. This geometric configuration was significantly accentuated in AAE patients due to the greater disproportionate disparity in the LCS relative to either the RCS or NCS than in the roots of normal control subjects. Clin. Anat. 32:117-123, 2019. © 2019 Wiley Periodicals, Inc.

Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry.

OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFRML). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFRML. Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFRML. MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFRML. CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFRML in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFRML in relation to the invasively measured FFR value was ρ = 0.684, ρ = 0.622, and ρ = 0.669, respectively (p < 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p < 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p < 0.001 for all). CONCLUSION. CCTA-derived cFFRML is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.

BACKGROUND: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80. METHODS AND FINDINGS: A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84-0.87, 95% confidence intervals 0.71-0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application. CONCLUSION: We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study.

Association between non-alcoholic fatty liver disease and subclinical coronary atherosclerosis: An observational cohort study.

BACKGROUND & AIMS: There are limited data on the association between non-alcoholic fatty liver disease (NAFLD) and subclinical coronary atherosclerosis. This study investigated the influence of NAFLD on subclinical coronary atherosclerosis as detected by coronary computed tomography angiography (CCTA) in an asymptomatic population. METHODS: A total of 5,121 consecutive asymptomatic individuals with no prior history of coronary artery disease or significant alcohol intake voluntarily underwent abdominal ultrasonography and CCTA as part of a general health examination. Fatty liver was assessed by ultrasonography examination. The fatty liver index and NAFLD fibrosis score were also calculated. Coronary atherosclerotic plaques on CCTA were evaluated. The association between NAFLD and subclinical coronary atherosclerosis was determined by logistic regression analysis. RESULTS: Of the study participants, 1,979 (38.6%) had ultrasonography-diagnosed NAFLD. After adjustment for cardiovascular risk factors, there were no statistically significant differences in the adjusted odds ratios of NAFLD for calcified plaque (1.03; 95% CI 0.89-1.20; p = 0.673) and mixed plaque (1.15; 95% CI 0.93-1.42; p = 0.214). However, adjusted odds ratios for any atherosclerotic plaque (1.18; 95% CI 1.03-1.35; p = 0.016) and non-calcified plaque (1.27; 95% CI 1.08-1.48; p = 0.003) were significantly higher in NAFLD. In addition, there was a significant association of fatty liver index ≥30 with non-calcified plaque (1.37; 95% CI 1.14-1.65; p = 0.001) and NAFLD fibrosis score ≥-1.455 with non-calcified plaque (1.20; 95% CI 1.08-1.42; p = 0.030). CONCLUSIONS: In this large cross-sectional study of asymptomatic individuals undergoing CCTA, NAFLD was consistently associated with non-calcified plaque, suggesting an increased cardiovascular risk. LAY SUMMARY: In asymptomatic individuals, non-alcoholic fatty liver disease (NAFLD) was an independent risk factor for non-calcified plaque, which has been known as a vulnerable plaque associated with sudden and unexpected cardiac events. Therefore, appropriate medical therapy for NAFLD was required to reduce future cardiac events.

Coronary CT angiography characteristics of OCT-defined thin-cap fibroatheroma: a section-to-section comparison study.

OBJECTIVES: To evaluate whether plaque characteristics as assessed by coronary computed tomography angiography (CCTA) were associated with the presence of a thin-cap fibroatheroma (TCFA)-a precursor of plaque rupture-defined by optical coherence tomography (OCT) in a section-to-section-level comparison. METHODS: From 28 symptomatic patients, 31 coronary lesions were evaluated on 727 cross-sections co-registered by both CCTA and OCT. CCTA plaque characteristics included low attenuation plaque (LAP, <30 HU), napkin ring sign (NRS), positive remodelling (PR, remodelling index ≥1.10), and spotty calcification and plaque area and plaque burden. By OCT, presence of TCFA, lumen area and arc of lipid were determined. RESULTS: OCT revealed a TCFA in 69 (9.4%) sections from 19 (61.2 %) lesions. In per-section analysis, OCT-TCFA showed higher frequency of CCTA-detected LAP (58.0% vs. 18.5%), NRS (31.9% vs. 8.8%) and PR (68.1% vs. 48.0%) and greater plaque burden (70.6% vs. 61.9%) as compared to sections without OCT-TCFA (all p < 0.05). In multivariable analysis, LAP (odds ratio [OR] 4.05, p < 0.001) and NRS (OR 2.47, p = 0.005) were associated with OCT-TCFA. CCTA-measured lumen area correlated well with OCT-measured lumen area (R = 0.859, limits of agreement -0.5 ± 3.7 mm2). CONCLUSIONS: LAP and NRS in CCTA were associated with the presence of OCT-defined TCFA in a section-to-section comparison. KEY POINTS: • CT-defined LAP and NRS were associated with OCT-defined TCFA • OCT-TCFA showed higher frequency of LAP, NRS, PR and greater plaque burden • Non-calcified plaque area was correlated with OCT-measured lipid arc.

Modified Bicaval Technique in Orthotopic Heart Transplantation　- Comparison With Conventional Bicaval Technique.

BACKGROUND: Orthotopic heart transplantation (HT) is the treatment of choice for patients with end-stage heart failure (HF). The bicaval technique was introduced as a safe alternative minimizing modification of atrial geometry. The modification of bicaval anastomosis is suggested to compensate for caliber mismatch and small donor. The present study was performed to compare these 2 techniques in terms of postoperative CT scan and clinical outcomes. Methods and Results: Retrospectively, 158 consecutive patients with end-stage HF underwent orthotopic HT between January 2009 and June 2013 were analyzed. Of these, we excluded 3 patients with total HT. The study group was divided into modified technique (n=37) or conventional technique (n=118). A total of 113 patients (modified: n=29, conventional: n=84) were examined with cardiac CT. Discrepancy in the size of the vena cava compared with that of the anastomosis site was assessed. There was no significant difference in the complication and survival rates. There was 1 incident of moderate-to-severe tricuspid valve regurgitation in the modified group (n=1, 2.7%). Both the SVC ratio (1.07±0.13 vs. 1.28±0.32, P=0.001) and IVC ratio (1.06±0.07 vs. 1.13±0.19, P=0.009) were higher in the conventional group, which meant more stenotic imaging findings were observed in the conventional group. CONCLUSIONS: Orthotopic HT with modified bicaval anastomosis is an attractive alternative with easy orientation and equivocal outcomes.

Intravascular ultrasound-derived morphological predictors of myocardial ischemia assessed by stress myocardial perfusion computed tomography.

BACKGROUND: Although ischemia-guided revascularization improves clinical outcomes, morphological determinants of clinically relevant myocardial ischemia have not been studied. To identify intravascular ultrasound (IVUS)-derived anatomical parameters for predicting myocardial perfusion defect and its extent. METHODS: A total of 103 patients (88 stable and 15 unstable angina) with 153 lesions (angiographic diameter stenosis of 30-80%) underwent stress myocardial perfusion computed tomography (CT) and IVUS pre-procedure. The volume of CT perfusion defect and %CT perfusion defect in the target vessel territories were measured. RESULTS: The CT perfusion defect was seen in 76 (50%) lesions. The independent determinants for the presence of CT perfusion defect were IVUS-minimal lumen area (MLA) (adjusted OR = 0.56, 95% CI = 0.38-0.82), plaque burden (adjusted OR = 1.07, 95% CI = 1.02-1.11) and involvement of left main or left anterior descending artery (adjusted OR = 4.13, 95% CI = 1.75-9.78, all P < 0.05). The CT perfusion defect was predicted by IVUS-MLA <2.28mm2 (sensitivity 74%, specificity 82%) and plaque burden >77% (sensitivity 79%, specificity 78%) as thresholds. The independent determinants for the volume of CT perfusion defect were the involvement of left main or left anterior descending artery (ß = 16.43, standard errors = 4.387, P = 0.020) and a greater plaque burden (ß = 0.56, standard errors = 0.163, P = 0.026). CONCLUSIONS: IVUS-derived morphological parameters were useful to predict the presence of CT perfusion defect and the size of myocardial ischemia that were primarily determined by lesion severity and subtended myocardial territory. © 2016 Wiley Periodicals, Inc.

Myocardial segmentation based on coronary anatomy using coronary computed tomography angiography: Development and validation in a pig model.

OBJECTIVES: To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA). METHODS: Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns. RESULTS: CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011). CONCLUSIONS: CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA. KEY POINTS: • CAMS is a feasible method for identifying corresponding coronary territory using CTA • CAMS is more accurate in predicting coronary territory than the AHA method • The AHA method may underestimate the ischaemic territory of LAD stenosis.

Multi-VENC acquisition of four-dimensional phase-contrast MRI to improve precision of velocity field measurement.

PURPOSE: The present study aims to improve precision of four-dimensional (4D) phase-contrast (PC) MRI technique by using multiple velocity encoding (VENC) parameters. THEORY AND METHODS: The 3D flow fields in an in vitro stenosis phantom and an in vivo ascending aorta were determined using a 4D PC-MRI sequence with multiple VENC values. The velocity field obtained for large VENC was combined with that from small VENC, unless velocity data were lost by phase aliasing and phase dispersion. Noise levels of the combined velocity fields were compared with the increasing overlapping number of VENC parameters. RESULTS: The phantom measurement showed that the multi-VENC acquisition reduced the noise levels in radial and axial velocities (> 24 cm/s at VENC = 300 cm/s) down to 0.80 ± 0.45 cm/s and 5.60 ± 2.63 cm/s, respectively. This increased the velocity-to-noise ratio (VNR) by approximately two-fold to six-fold depending on the locations. As a result, the multi-VENC measurement could visualize the low-velocity recirculating flows more clearly. CONCLUSION: The multi-VENC measurement of 4D PC-MRI sequence increased the VNR distribution by reducing velocity noise. The improved VNR can be beneficial for investigating blood flow structures in a flow field with a high velocity dynamic range.

Four-dimensional flow MRI for evaluation of post-stenotic turbulent flow in a phantom: comparison with flowmeter and computational fluid dynamics.

OBJECTIVES: To validate 4D flow MRI in a flow phantom using a flowmeter and computational fluid dynamics (CFD) as reference. METHODS: Validation of 4D flow MRI was performed using flow phantoms with 75 % and 90 % stenosis. The effect of spatial resolution on flow rate, peak velocity and flow patterns was investigated in coronal and axial scans. The accuracy of flow rate with 4D flow MRI was evaluated using a flowmeter as reference, and the peak velocity and flow patterns obtained were compared with CFD analysis results. RESULTS: 4D flow MRI accurately measured the flow rate in proximal and distal regions of the stenosis (percent error ≤3.6 % in axial scanning with 1.6-mm resolution). The peak velocity of 4D flow MRI was underestimated by more than 22.8 %, especially from the second half of the stenosis. With 1-mm isotropic resolution, the maximum thickness of the recirculating flow region was estimated within a 1-mm difference, but the turbulent velocity fluctuations mostly disappeared in the post-stenotic region. CONCLUSION: 4D flow MRI accurately measures the flow rates in the proximal and distal regions of a stenosis in axial scan but has limitations in its estimation of peak velocity and turbulent characteristics. KEY POINTS: • 4D flow MRI accurately measures the flow rate in axial scan. • The peak velocity was underestimated by 4D flow MRI. •4D flow MRI demonstrates the principal pattern of post-stenotic flow.