Trials Testing the Value of Imaging Use in Valve Disease and in Transcatheter Valvular Interventions

Cardiolog

Rationale and design of the dual-energy computed tomography for ischemia determination compared to “gold standard” non-invasive and invasive techniques (DECIDE-Gold) : a multicenter international efficacy diagnostic study of rest-stress dual-energy computed tomography angiography with perfusion

Background: Dual-energy CT (DECT) has potential to improve myocardial perfusion for physiologic assessment of coronary artery disease (CAD). Diagnostic performance of rest-stress DECT perfusion (DECTP) is unknown. Objective: DECIDE-Gold is a prospective multicenter study to evaluate the accuracy of DECT to detect hemodynamic (HD) significant CAD, as compared to fractional flow reserve (FFR) as a reference standard. Methods: Eligible participants are subjects with symptoms of CAD referred for invasive coronary angiography (ICA). Participants will undergo DECTP, which will be performed by pharmacological stress, and participants will subsequently proceed to ICA and FFR. HD-significant CAD will be defined as FFR\ua0 64\ua00.80. In those undergoing myocardial stress imaging (MPI) by positron emission tomography (PET), single photon emission computed tomography (SPECT) or cardiac magnetic resonance (CMR) imaging, ischemia will be graded by % ischemic myocardium. Blinded core laboratory interpretation will be performed for CCTA, DECTP, MPI, ICA, and FFR. Results: Primary endpoint is accuracy of DECTP to detect 651 HD-significant stenosis at the subject level when compared to FFR. Secondary and tertiary endpoints are accuracies of combinations of DECTP at the subject and vessel levels compared to FFR and MPI. Conclusion: DECIDE-Gold will determine the performance of DECTP for diagnosing ischemia

Aggregate Plaque Volume by Coronary Computed Tomography Angiography Is Superior and Incremental to Luminal Narrowing for Diagnosis of Ischemic Lesions of Intermediate Stenosis Severity

ObjectivesThis study examined the performance of percent aggregate plaque volume (%APV), which represents cumulative plaque volume as a function of total vessel volume, by coronary computed tomography angiography (CTA) for identification of ischemic lesions of intermediate stenosis severity.BackgroundCoronary lesions of intermediate stenosis demonstrate significant rates of ischemia. Coronary CTA enables quantification of luminal narrowing and %APV.MethodsWe identified 58 patients with intermediate lesions (30% to 69% diameter stenosis) who underwent invasive angiography and fractional flow reserve. Coronary CTA measures included diameter stenosis, area stenosis, minimal lumen diameter (MLD), minimal lumen area (MLA) and %APV. %APV was defined as the sum of plaque volume divided by the sum of vessel volume from the ostium to the distal portion of the lesion. Fractional flow reserve ≤0.80 was considered diagnostic of lesion-specific ischemia. Area under the receiver operating characteristic curve and net reclassification improvement (NRI) were also evaluated.ResultsTwenty-two of 58 lesions (38%) caused ischemia. Compared with nonischemic lesions, ischemic lesions had smaller MLD (1.3 vs. 1.7 mm, p = 0.01), smaller MLA (2.5 vs. 3.8 mm2, p = 0.01), and greater %APV (48.9% vs. 39.3%, p < 0.0001). Area under the receiver operating characteristic curve was highest for %APV (0.85) compared with diameter stenosis (0.68), area stenosis (0.66), MLD (0.75), or MLA (0.78). Addition of %APV to other measures showed significant reclassification over diameter stenosis (NRI 0.77, p < 0.001), area stenosis (NRI 0.63, p = 0.002), MLD (NRI 0.62, p = 0.001), and MLA (NRI 0.43, p = 0.01).ConclusionsCompared with diameter stenosis, area stenosis, MLD, and MLA, %APV by coronary CTA improves identification, discrimination, and reclassification of ischemic lesions of intermediate stenosis severity

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry.

AimsIn patients without obstructive coronary artery disease (CAD), we examined the prognostic value of risk factors and atherosclerotic extent.Methods and resultsPatients from the long-term CONFIRM registry without prior CAD and without obstructive (≥50%) stenosis were included. Within the groups of normal coronary computed tomography angiography (CCTA) (N = 1849) and non-obstructive CAD (N = 1698), the prognostic value of traditional clinical risk factors and atherosclerotic extent (segment involvement score, SIS) was assessed with Cox models. Major adverse cardiac events (MACE) were defined as all-cause mortality, non-fatal myocardial infarction, or late revascularization. In total, 3547 patients were included (age 57.9 ± 12.1 years, 57.8% male), experiencing 460 MACE during 5.4 years of follow-up. Age, body mass index, hypertension, and diabetes were the clinical variables associated with increased MACE risk, but the magnitude of risk was higher for CCTA defined atherosclerotic extent; adjusted hazard ratio (HR) for SIS &gt;5 was 3.4 (95% confidence interval [CI] 2.3-4.9) while HR for diabetes and hypertension were 1.7 (95% CI 1.3-2.2) and 1.4 (95% CI 1.1-1.7), respectively. Exclusion of revascularization as endpoint did not modify the results. In normal CCTA, presence of ≥1 traditional risk factors did not worsen prognosis (log-rank P = 0.248), while it did in non-obstructive CAD (log-rank P = 0.025). Adjusted for SIS, hypertension and diabetes predicted MACE risk in non-obstructive CAD, while diabetes did not increase risk in absence of CAD (P-interaction = 0.004).ConclusionAmong patients without obstructive CAD, the extent of CAD provides more prognostic information for MACE than traditional cardiovascular risk factors. An interaction was observed between risk factors and CAD burden, suggesting synergistic effects of both

Neo-LVOT and Transcatheter Mitral Valve Replacement: Expert Recommendations

With the advent of transcatheter mitral valve replacement (TMVR), the concept of the neo-left ventricular outflow tract (LVOT) was introduced and remains an essential component of treatment planning. This paper describes the LVOT anatomy and provides a step-by-step computed tomography methodology to segment and measure the neo-LVOT while discussing the current evidence and outstanding challenges. It also discusses the technical and hemodynamic factors that play a major role in assessing the neo-LVOT. A summary of expert-based recommendations about the overall risk of LVOT obstruction in different scenarios is presented along with the currently available methods to reduce the risk of LVOT obstruction and other post-procedural complications

Coronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients.

AIMS:We aimed to compare semiquantitative coronary computed tomography angiography (CCTA) risk scores - which score presence, extent, composition, stenosis and/or location of coronary artery disease (CAD) - and their prognostic value between patients with and without diabetes mellitus (DM). Risk scores derived from general chest-pain populations are often challenging to apply in DM patients, because of numerous confounders. METHODS:Out of a combined cohort from the Leiden University Medical Center and the CONFIRM registry with 5-year follow-up data, we performed a secondary analysis in diabetic patients with suspected CAD who were clinically referred for CCTA. A total of 732 DM patients was 1:1 propensity-matched with 732 non-DM patients by age, sex and cardiovascular risk factors. A subset of 7 semiquantitative CCTA risk scores was compared between groups: 1) any stenosis ≥50%, 2) any stenosis ≥70%, 3) stenosis-severity component of the coronary artery disease-reporting and data system (CAD-RADS), 4) segment involvement score (SIS), 5) segment stenosis score (SSS), 6) CT-adapted Leaman score (CT-LeSc), and 7) Leiden CCTA risk score. Cox-regression analysis was performed to assess the association between the scores and the primary endpoint of all-cause death and non-fatal myocardial infarction. Also, area under the receiver-operating characteristics curves were compared to evaluate discriminatory ability. RESULTS:A total of 1,464 DM and non-DM patients (mean age 58 ± 12 years, 40% women) underwent CCTA and 155 (11%) events were documented after median follow-up of 5.1 years. In DM patients, the 7 semiquantitative CCTA risk scores were significantly more prevalent or higher as compared to non-DM patients (p ≤ 0.022). All scores were independently associated with the primary endpoint in both patients with and without DM (p ≤ 0.020), with non-significant interaction between the scores and diabetes (interaction p ≥ 0.109). Discriminatory ability of the Leiden CCTA risk score in DM patients was significantly better than any stenosis ≥50% and ≥70% (p = 0.003 and p = 0.007, respectively), but comparable to the CAD-RADS, SIS, SSS and CT-LeSc that also focus on the extent of CAD (p ≥ 0.265). CONCLUSION:Coronary atherosclerosis scoring with semiquantitative CCTA risk scores incorporating the total extent of CAD discriminate major adverse cardiac events well, and might be useful for risk stratification of patients with DM beyond the binary evaluation of obstructive stenosis alone