Reference parameters for left ventricular wall thickness, thickening, and motion in stress myocardial perfusion CT: Global and regional assessment.

PURPOSE: To use adenosine-induced stress CT myocardial perfusion imaging (CT-MPI) to determine normal reference values for left ventricle (LV) wall thickness (WT) and motion parameters. METHODS: This study included 106 Korean subjects (52 men and 54 women) who underwent CT-MPI due to chest pain, but were not found to have any detectable mild or severe coronary artery disease or myocardial perfusion defect. The following quantitative parameters were assessed on the CT-MPI according to a 17-segment model: LV myocardial thickness at end-systolic (WTES) and end-diastolic (WTED) phases, systolic wall thickening (SWT), and wall motion (WM). The associations of the measured parameters with the subjects' demographic characteristics and comorbidities were also analyzed. RESULTS: Septal wall (7.2 mm) and basal-level (7.7 mm) LV myocardium demonstrated significantly higher WT (p < 0.001). SWT was highest in lateral (77.8%, p < 0.014) and apical (78.9%, p = 0.009) myocardium, while lateral (7.7 mm) and basal (6.7 mm) myocardium exhibited the greatest WM (p < 0.001). WT was significantly higher in men and younger (<60 years) subjects (all, p < 0.001). Hypertensive individuals presented with significantly higher SWT (79.9%, p = 0.024). LV WT exhibited statistically significant correlations (all positive, except for age) with age, height, weight, body surface area, body mass index, and systolic blood pressure (all, p < 0.010). CONCLUSIONS: The present study provides CT-MPI reference values for LV myocardial WT, SWT, and WM measured on an adult Korean population. Knowledge of such normal reference measurements would be beneficial for the efficient interpretation of CT-MPI examinations in populations of Asian ethnicity.

Ischemic burden assessment of myocardial perfusion CT, compared with SPECT using semi-quantitative and quantitative approaches.

BACKGROUND: We aimed to compare the myocardial ischemic burden assessed using semi-quantitative and quantitative analysis of computed tomography-myocardial perfusion imaging (CT-MPI) with that of single-photon emission computed tomography (SPECT). METHODS: From 2011 to 2013, 97 patients who underwent CT-MPI and SPECT were evaluated. The extent and severity of perfusion defects were assessed on a 5-point scale using a standard 16-segment model, and were expressed as summed stress score (SSS) and summed difference score (SDS). Receiver operating characteristic (ROC) curves for quantitative parameters were generated for the diagnosis of abnormal perfusion defect (SSS ≥ 4) and presence of ischemia (SDS ≥ 2) on SPECT. RESULTS: On CT-MPI, 298 (19.2%) of the 1552 segments showed perfusion abnormalities during stress, whereas perfusion abnormalities were shown in 179 (11.5%) segments on SPECT-MPI. On a per-person basis, there was good agreement, with intraclass correlation coefficients of 0.78 for SSS and 0.72 for SDS. A significant reduction of attenuation in stress and myocardial perfusion reserve index, along with an increase in % defect volume of CT-MPI, were demonstrated as the degree of perfusion defect or ischemia on SPECT increased. On the ROC curves, % defect volume on CT-MPI demonstrated the highest area under the curve: 0.91 for abnormal perfusion defect and 0.89 (all p < 0.001) for the presence of ischemia on SPECT. CONCLUSIONS: Semi-quantitative analysis of CT-MPI showed good accordance with SPECT. A quantitative approach for CT-MPI, especially % defect volume, may provide additional value in the identification of myocardial perfusion abnormalities. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT01696006.

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.

Impact of a Geometric Correction for Proximal Flow Constraint on the Assessment of Mitral Regurgitation Severity Using the Proximal Flow Convergence Method.

BACKGROUND: Overestimation of the severity of mitral regurgitation (MR) by the proximal isovelocity surface area (PISA) method has been reported. We sought to test whether angle correction (AC) of the constrained flow field is helpful to eliminate overestimation in patients with eccentric MR. METHODS: In a total of 33 patients with MR due to prolapse or flail mitral valve, both echocardiography and cardiac magnetic resonance image (CMR) were performed to calculate regurgitant volume (RV). In addition to RV by conventional PISA (RVPISA), convergence angle (α) was measured from 2-dimensional Doppler color flow maps and RV was corrected by multiplying by α/180 (RVAC). RV measured by CMR (RVCMR) was used as a gold standard, which was calculated by the difference between total stroke volume measured by planimetry of the short axis slices and aortic stroke volume by phase-contrast image. RESULTS: The correlation between RVCMR and RV by echocardiography was modest [RVCMR vs. RVPISA (r = 0.712, p < 0.001) and RVCMR vs. RVAC (r = 0.766, p < 0.001)]. However, RVPISA showed significant overestimation (RVPISA - RVCMR = 50.6 ± 40.6 mL vs. RVAC - RVCMR = 7.7 ± 23.4 mL, p < 0.001). The overall accuracy of RVPISA for diagnosis of severe MR, defined as RV ≥ 60 mL, was 57.6% (19/33), whereas it increased to 84.8% (28/33) by using RVAC (p = 0.028). CONCLUSION: Conventional PISA method tends to provide falsely large RV in patients with eccentric MR and a simple geometric AC of the proximal constraint flow largely eliminates overestimation.

In vivo assessment of aortic root geometry in normal controls using 3D analysis of computed tomography.

AIMS: Understanding normal asymmetry in the aortic root could aid in the development of new surgical repair techniques or devices with improved haemodynamic performance. The purpose of this study was to assess geometric asymmetry and age-related changes in the normal aortic root using 3D computed tomography. METHODS AND RESULTS: The institutional review board approved this retrospective study of 130 normal subjects (mean age, 51.4 years; 58 men). Specialized 3D software measured individual cusp sinus volumes (CSVs), cusp surface areas (CSAs), and intercommissural distances (ICDs). Age-related aortic root changes were evaluated with simple correlation, ANOVA test among age groups, and multivariable linear regression analyses. The CSV and CSA of left coronary cusp (LCC) were significantly smaller than those of right coronary cusp (RCC) and non-coronary cusp (NCC) (both, P < 0.001) in all age groups. The mean ratios of RCC or NCC-to-LCC were 1.38 and 1.36 for CSV, 1.19 and 1.20 for CSA, and 1.21 and 1.06 for ICD, respectively. The CSV and ICD increased in older age with weak-to-moderate correlation coefficients in both men and women. By multivariable linear regression, CSVs and ICDs of all cusps showed a positive correlation with age (P < 0.05), and the female gender was associated with a smaller size of the CSV and CSA. CONCLUSIONS: The LCC was significantly smaller than the other two cusps, and the aortic root size increased with age.

Risk score model for the assessment of coronary artery disease in asymptomatic patients with type 2 diabetes.

No model has been developed to predict significant coronary artery disease (CAD) on coronary computed tomographic angiography (CCTA) in asymptomatic type 2 diabetes. Therefore, we sought to develop a model for the prediction of significant CAD on CCTA in these patients. We analyzed 607 asymptomatic patients with type 2 diabetes who underwent CCTA. The cardiac event was defined as a composite of cardiac death, nonfatal myocardial infarction, acute coronary syndrome, and coronary revascularization. Significant CAD (diameter stenosis ≥50%) in at least one coronary artery on CCTA was observed in 188 (31.0%). During the follow-up period (median 4.3 [interquartile range, 3.7-4.8] years), 71 patients had 83 cardiac events. Clinical risk factors for significant CAD were age, male gender, duration of diabetes, hypertension, current smoking, family history of premature CAD, previous history of stroke, ratio of total cholesterol to high-density lipoprotein cholesterol, and neuropathy. Using these variables, we formulated a risk score model, and the scores ranged from 0 to 17 (area under the curve = 0.727, 95% confidence interval = 0.714-0.739, P < 0.001). Patients were categorized into low (≤3), intermediate (4-6), or high (≥7) risk group. There were significant differences between the risk groups in the probability of significant CAD (12.6% vs 29.4% vs 57.7%, P for all < 0.001) and 5-year cardiac event-free survival rate (96.6% ±â€Š1.5% vs 88.9% ±â€Š1.8% vs 73.8% ±â€Š4.1%, log-rank P for trend < 0.001). This model predicts significant CAD on CCTA and has the potential to identify asymptomatic type 2 diabetes with high risk.

Association between C-reactive Protein and type of coronary arterial plaque in asymptomatic patients: assessment with coronary CT angiography.

PURPOSE: To determine whether C-reactive protein is associated with the type of coronary plaque seen at computed tomographic (CT) angiography. MATERIALS AND METHODS: The institutional review board approved this retrospective study, and the need for informed consent was waived. C-reactive protein levels were measured in 2653 asymptomatic subjects (mean age ± standard deviation, 54.7 years ± 9.2; 1811 men) who underwent self-referred coronary CT angiography as part of a general health checkup. The presence of coronary plaque, plaque type (calcified, mixed calcified, or noncalcified), stenosis degree, and number of involved segments were evaluated. Subjects with one type of plaque (calcified plaque, mixed plaque, and noncalcified plaque groups) and two or more types of plaque (multiple lesions group) were analyzed separately. Multivariate logistic regression analysis was used to evaluate the association between increasing C-reactive protein levels and plaque type. RESULTS: Coronary plaque was found in 1150 of the 2653 subjects (43.3%): calcified plaque (n = 604, 22.8%), mixed plaque (n = 67, 2.5%), noncalcified plaque (n = 208, 7.8%), and multiple lesions (n = 271, 10.2%). The C-reactive protein cutoff value of the fourth quartile was 1.2 mg/L (11.4 nmol/L), and all types of coronary plaque were increased in the higher quartile of the C-reactive protein levels. Multivariate logistic regression analysis showed that a higher C-reactive protein level was an independent predictor for the presence of noncalcified plaque (fourth vs first quartile group, odds ratio = 1.70, P = .025) and significant (50% and higher) coronary stenosis (odds ratio = 1.76, P = .020) after adjustment for traditional risk factors for coronary artery disease. CONCLUSION: C-reactive protein is associated with noncalcified coronary arterial plaque, as seen at coronary CT angiography in asymptomatic patients after adjustment for traditional risk factors.

Late improvement in graft patency after coronary artery bypass grafting: Serial assessment with multidetector computed tomography in the early and late postoperative settings.

OBJECTIVE: Grafts initially showing poor patency after coronary artery bypass grafting have occasionally shown improvement on serial multidetector computed tomography. This study analyzed possible factors associated with this phenomenon. METHODS: Between September 2003 and July 2007, 512 patients underwent multidetector computed tomography within 1 month of isolated coronary artery bypass grafting. Among them, 1720 distal anastomoses were made with 1042 arterial and 302 venous conduits. Of these, 95 grafts (in 73 patients) were faint (n = 67) or nonvisualized (n = 28). Seventy-three of these grafts (in 56 patients) had follow-up multidetector computed tomographic evaluation 1 year after surgery and comprised the study group. RESULTS: Improvement in graft patency (faint to patent or nonvisualization to visualization) occurred in 44 grafts (60.3%). Multivariate analysis revealed proximal target vessel stenosis of at least 90% (relative risk, 3.81; P = .009), larger target coronary size (relative risk, 1.72; P = .002), and radial artery graft use (relative risk, 4.44; P = .003) to be significantly associated with the graft patency restoration. Graft patency restoration was most commonly observed in a group of 28 radial artery grafts that were anastomosed to target vessel with proximal stenosis of at least 90%; of these grafts, 24 (85.7%) showed improved graft patency on follow-up. CONCLUSIONS: A large proportion of radial artery grafts initially showing poor opacification after coronary artery bypass grafting demonstrated patency restoration on serial multidetector computed tomography. Larger target vessel size and target vessel stenosis of at least 90% were significant correlative factors.