Noninvasive Imaging for the Assessment of Coronary Artery Disease

Noninvasive cardiac imaging is a cornerstone of the diagnostic work-up in patients with suspected coronary artery disease (CAD), cardiomyopathy, heart failure, and congenital heart disease. It is essential for the assessment of CAD from functional and anatomical perspectives, and is considered the gate-keeper to invasive coronary angiography. Cardiac tests include exercise electrocardiography, single photon emission computed tomography myocardial perfusion imaging, positron emission tomography myocardial perfusion imaging, stress echocardiography, coronary computed tomography angiography, and stress cardiac magnetic resonance. The wide range of imaging techniques is advantageous for the detection and management of cardiac diseases, and the implementation of preventive measures that can affect the long-term prognosis of these diseases. However, clinicians face a challenge when deciding which test is most appropriate for a given patient. Basic knowledge of each modality will facilitate the decision-making process in CAD assessment

Quantitative analysis of dipyridamole-thallium images for the detection of coronary artery disease

To determine if the detection of coronary artery disease by dipyridamole-thallium imaging is improved by 1) quantitative versus qualitative analysis, and 2) combining quantitative variables, 80 patients with chest pain (53 with and 27 without coronary artery disease) who underwent cardiac catheterization were studied. Segmental thallium initial uptake, linear clearance, mono-exponential clearance and redistribution were measured from early, intermediate and delayed images acquired in three projections. Normal values were determined from 13 other clinically normal subjects.When five segments per view were used for quantitative analysis, sensitivity and specificity were 87 and 63%, respectively, for uptake, 77 and 67% for linear clearance, 60 and 60% for monoexponential clearance and 62 and 56% for redistribution. Of the four variables, uptake and linear clearance were the most sensitive (p < 0.01) and specificity did not differ significantly. Using three segments per view, the specificity of uptake increased (p < 0.05) to 78% without a significant change in sensitivity (85%). With this approach, sensitivity and specificity did not differ from those of qualitative analysis (85 and 78%, respectively).Stepwise logistic regression analysis demonstrated that the best quantitative thallium correlate of the presence of coronary artery disease was a combination variable of “either abnormal uptake or abnormal linear clearance, or both.” Using five segments per view, the model's specificity (85%) was greater than that of uptake alone (p < 0.02), with similar sensitivity (92%). Using three segments per view, the model's specificity (93%) was greater than that of uptake alone (p < 0.05) and of qualitative analysis (p < 0.05), with similar sensitivity (85%). Compared with qualitative analysis, the diagnostic accuracy of the model was greater using either five segments (90 versus 82%, p < 0.01) or three segments (88 versus 82%, p < 0.05) per view.Quantitative analysis of dipyridamole-thallium images using single individual variables provides results comparable with those of qualitative analysis and this can be further optimized when a combination of quantitative variables is used

An Initial Strategy of Intensive Medical Therapy Is Comparable to That of Coronary Revascularization for Suppression of Scintigraphic Ischemia in High-Risk But Stable Survivors of Acute Myocardial Infarction

ObjectivesThe purpose of this study was to determine the relative benefit of intensive medical therapy compared with coronary revascularization for suppressing scintigraphic ischemia.BackgroundAlthough medical therapies can reduce myocardial ischemia and improve patient survival after acute myocardial infarction, the relative benefit of medical therapy versus coronary revascularization for reducing ischemia is unknown.MethodsA prospective randomized trial in 205 stable survivors of acute myocardial infarction was made to define the relative efficacy of an intensive medical therapy strategy versus coronary revascularization for suppressing scintigraphic ischemia as assessed by serial gated adenosine Tc-99m sestamibi myocardial perfusion tomography. All patients at baseline had large total (≥20%) and ischemic (≥10%) adenosine-induced left ventricular perfusion defects and an ejection fraction ≥35%. Imaging was performed during 1 to 10 days of hospital admission and repeated in an identical fashion after optimization of therapy. Patients randomized to either strategy had similar baseline demographic and scintigraphic characteristics.ResultsBoth intensive medical therapy and coronary revascularization induced significant but comparable reductions in total (−16.2 ± 10% vs. −17.8 ± 12%; p = NS) and ischemic (−15 ± 9% vs. −16.2 ± 9%; p = NS) perfusion defect sizes. Likewise, a similar percentage of patients randomized to medical therapy versus coronary revascularization had suppression of adenosine-induced ischemia (80% vs. 81%; p = NS).ConclusionsSequential adenosine sestamibi myocardial perfusion tomography can effectively monitor changes in scintigraphic ischemia after anti-ischemic medical or coronary revascularization therapy. A strategy of intensive medical therapy is comparable to coronary revascularization for suppressing ischemia in stable patients after acute infarction who have preserved LV function

F-18-Fluorodeoxyglucose Positron Emission Tomography Imaging-Assisted Management of Patients With Severe Left Ventricular Dysfunction and Suspected Coronary Disease A Randomized, Controlled Trial (PARR-2)

ObjectivesWe conducted a randomized trial to assess the effectiveness of F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)-assisted management in patients with severe ventricular dysfunction and suspected coronary disease.BackgroundSuch patients may benefit from revascularization, but have significant perioperative morbidity and mortality. F-18-fluorodeoxyglucose PET can detect viable myocardium that might recover after revascularization.MethodsIncluded were patients with severe left ventricular (LV) dysfunction and suspected coronary disease being considered for revascularization, heart failure, or transplantation work-ups or in whom PET was considered potentially useful. Patients were stratified according to recent angiography or not, then randomized to management assisted by FDG PET (n = 218) or standard care (n = 212). The primary outcome was the composite of cardiac death, myocardial infarction, or recurrent hospital stay for cardiac cause, within 1 year.ResultsAt 1 year, the cumulative proportion of patients who had experienced the composite event was 30% (PET arm) versus 36% (standard arm) (relative risk 0.82, 95% confidence interval [CI] 0.59 to 1.14; p = 0.16). The hazard ratio (HR) for the composite outcome, PET versus standard care, was 0.78 (95% CI 0.58 to 1.1; p = 0.15); for patients that adhered to PET recommendations for revascularization, revascularization work-up, or neither, HR = 0.62 (95% CI 0.42 to 0.93; p = 0.019); in those without recent angiography, for cardiac death, HR = 0.4 (95% CI 0.17 to 0.96; p = 0.035).ConclusionsThis study did not demonstrate a significant reduction in cardiac events in patients with LV dysfunction and suspected coronary disease for FDG PET-assisted management versus standard care. In those who adhered to PET recommendations and in patients without recent angiography, significant benefits were observed. The utility of FDG PET is best realized in this subpopulation and when adherence to recommendations can be achieved

A Clinical Tool to Identify Candidates for Stress-First Myocardial Perfusion Imaging

Objectives: This study sought to develop a clinical model that identifies a lower-risk population for coronary artery disease that could benefit from stress-first myocardial perfusion imaging (MPI) protocols and that can be used at point of care to risk stratify patients. Background: There is an increasing interest in stress-first and stress-only imaging to reduce patient radiation exposure and improve patient workflow and experience. Methods: A secondary analysis was conducted on a single-center cohort of patients undergoing single-photon emission computed tomography (SPECT) and positron emission tomography (PET) studies. Normal MPI was defined by the absence of perfusion abnormalities and other ischemic markers and the presence of normal left ventricular wall motion and left ventricular ejection fraction. A model was derived using a cohort of 18,389 consecutive patients who underwent SPECT and was validated in a separate cohort of patients who underwent SPECT (n = 5,819), 1 internal cohort of patients who underwent PET (n=4,631), and 1 external PET cohort (n = 7,028). Results: Final models were made for men and women and consisted of 9 variables including age, smoking, hypertension, diabetes, dyslipidemia, typical angina, prior percutaneous coronary intervention, prior coronary artery bypass graft, and prior myocardial infarction. Patients with a score ≤1 were stratified as low risk. The model was robust with areas under the curve of 0.684 (95% confidence interval [CI]: 0.674 to 0.694) and 0.681 (95% CI: 0.666 to 0.696) in the derivation cohort, 0.745 (95% CI: 0.728 to 0.762) and 0.701 (95% CI: 0.673 to 0.728) in the SPECT validation cohort, 0.672 (95% CI: 0.649 to 0.696) and 0.686 (95% CI: 0.663 to 0.710) in the internal PET validation cohort, and 0.756 (95% CI: 0.740 to 0.772) and 0.737 (95% CI: 0.716 to 0.757) in the external PET validation cohort in men and women, respectively. Men and women who scored ≤1 had negative likelihood ratios of 0.48 and 0.52, respectively. Conclusions: A novel model, based on easily obtained clinical variables, is proposed to identify patients with low probability of having abnormal MPI results. This point-of-care tool may be used to identify a population that might qualify for stress-first MPI protocols

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone