Hybrid Imaging: Calcium Score and Myocardial Perfusion Imaging.

Coronary heart disease (CHD) remains the top cause of death due to cardiovascular conditions worldwide, with someone suffering a myocardial infarction every 40 seconds. This highlights the importance of non-invasive imaging technologies like myocardial perfusion imaging (MPI), which are crucial for detecting coronary artery disease (CAD) early, even before symptoms appear. However, the reliance solely on MPI has shifted due to its limitations in definitively ruling out atherosclerosis, leading to the adoption of hybrid imaging techniques. Hybrid imaging combines computed tomography (CT) with MPI techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT). This integration, often within a single gantry system, enhances the diagnostic accuracy by allowing for attenuation correction (AC), acquisition of the coronary artery calcium score (CACS), and more precise tracing of radiotracer uptake. The built-in CT in modern MPI systems assists in these functions, which is essential for better diagnosis and risk assessment in patients. The addition of CACS to MPI, a method involving the assessment of calcified plaque in coronary arteries, notably enhances diagnostic and prognostic capabilities. CACS helps in identifying atherosclerosis and predicting potential cardiac events, facilitating personalized risk management and the initiation of tailored interventions like statins and aspirin. Such comprehensive imaging strategies not only improve the accuracy of detecting CAD but also help in stratifying patient risk more effectively. In this paper, we discuss how the incorporation of CAC into MPI protocols enhances the diagnostic sensitivity for detecting obstructive CAD, as evidenced by several studies where the addition of CAC to MPI has led to improved outcomes in diagnosing CAD. Moreover, CAC has been shown to unmask silent coronary atherosclerosis in patients with normal MPI results, highlighting its incremental diagnostic value. We will discuss the evolving role of hybrid imaging in guiding therapeutic decisions, particularly the use of statins for cardiovascular prevention. The integration of CAC assessment with MPI not only aids in the early detection and management of CAD but also optimizes therapeutic strategies, enhancing patient care through a more accurate and personalized approach. Such advancements underscore the need for further research to fully establish the benefits of combining CAC with MPI in the clinical assessment of cardiovascular risk.

Rapid Plaque Progression Is Independently Associated With Hyperglycemia and Low HDL Cholesterol in Patients With Stable Coronary Artery Disease: A PARADIGM Study.

BACKGROUND: We assessed whether combinations of cardiometabolic risk factors independently predict coronary plaque progression (PP) and major adverse cardiovascular events in patients with stable coronary artery disease. METHODS: Patients with known or suspected stable coronary artery disease (60.9±9.3 years, 55.4% male) undergoing serial coronary computed tomography angiographies (≥2 years apart), with clinical characterization and follow-up (N=1200), were analyzed from the PARADIGM study (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging). Plaque volumes measured in coronary segments (≥2 mm in diameter) were summed to provide whole heart plaque volume (mm3) and percent atheroma volume (plaque volume/vessel volume×100; %) per patient at baseline and follow-up. Rapid PP was defined as a percent atheroma volume increase of ≥1.0%/y. Major adverse cardiovascular events included nonfatal myocardial infarction, death, and unplanned coronary revascularization. RESULTS: In an interscan period of 3.2 years (interquartile range, 1.9), rapid PP occurred in 341 patients (28%). At multivariable analysis, the combination of cardiometabolic risk factors defined as metabolic syndrome predicted rapid PP (odds ratio, 1.51 [95% CI, 1.12-2.03]; P=0.007) together with older age, smoking habits, and baseline percent atheroma volume. Among single cardiometabolic variables, high fasting plasma glucose (diabetes or fasting plasma glucose >100 mg/dL) and low HDL-C (high-density lipoprotein cholesterol; <40 mg/dL in males and <50 mg/dL in females) were independently associated with rapid PP, in particular when combined (odds ratio, 2.37 [95% CI, 1.56-3.61]; P<0.001). In a follow-up of 8.23 years (interquartile range, 5.92-9.53), major adverse cardiovascular events occurred in 201 patients (17%). At multivariable Cox analysis, the combination of high fasting plasma glucose with high systemic blood pressure (treated hypertension or systemic blood pressure >130/85 mm Hg) was an independent predictor of events (hazard ratio, 1.79 [95% CI, 1.10-2.90]; P=0.018) together with family history, baseline percent atheroma volume, and rapid PP. CONCLUSIONS: In patients with stable coronary artery disease, the combination of hyperglycemia with low HDL-C is associated with rapid PP independently of other risk factors, baseline plaque burden, and treatment. The combination of hyperglycemia with high systemic blood pressure independently predicts the worse outcome beyond PP. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02803411.

Nuclear imaging techniques for cardiac amyloidosis.

PURPOSE OF REVIEW: Cardiac amyloidosis is a condition marked by the misfolding of precursor proteins into insoluble amyloid fibrils, leading to restrictive cardiomyopathy and heart failure symptoms. This review discusses advancements in nuclear imaging techniques that enhance the diagnosis and guide the management of cardiac amyloidosis, addressing the critical need for early and accurate detection in clinical practice. RECENT FINDINGS: Recent studies and guidelines emphasizes the pivotal role of nuclear imaging techniques in diagnosing cardiac amyloidosis. Cardiac scintigraphy, using bone-avid tracers like 99mTc-PYP, 99mTc-DPD, and 99mTc-HMDP, is instrumental in distinguishing between transthyretin amyloidosis and light chain amyloidosis. PET, with tracers such as 11C-Pittsburgh Compound B (11C-PiB) and 18F-Florbetapir, offers significant potential in measuring amyloid burden and monitoring disease progression, providing detailed insights into the myocardial involvement. SUMMARY: The advancements in nuclear imaging techniques significantly impact the management of cardiac amyloidosis. These methods allow for a more accurate diagnosis, detailed assessment of disease extent, and better differentiation between amyloidosis types, which are crucial for tailoring treatment approaches. The integration of these techniques into clinical practice is essential for improving patient outcomes and advancing research in cardiac amyloidosis.

The role of cardiac PET in diagnosis and prognosis of patients with ischemia with no obstructive coronary arteries (INOCA).

Chest pain, a common symptom in cardiovascular care, often leads to the investigation of obstructive coronary artery disease (CAD). However, many patients experience chest pain without obstructive CAD, termed INOCA (Ischemia with Non-Obstructive Coronary Arteries) or CMD (Coronary Microvascular Dysfunction). INOCA can be attributed to endothelial dysfunction, vascular smooth muscle dysfunction, or both, affecting about 20-30 % of patients with nonobstructive CAD. The diagnostic approach for INOCA includes both invasive and non-invasive methods, with cardiac PET (Positron Emission Tomography) playing a significant role in risk stratification and management. PET evaluates various parameters like myocardial blood flow under stress and rest, myocardial flow reserve, and myocardial ischemia. Such comprehensive assessment is essential in accurately diagnosing and managing INOCA, considering the complexity of this condition.

Myocardial Blood Flow Reserve, Microvascular Coronary Health, and Myocardial Remodeling in Patients With Aortic Stenosis.

BACKGROUND: Coronary microvascular function and hemodynamics may play a role in coronary circulation and myocardial remodeling in patients with aortic stenosis (AS). We aimed to evaluate the relationship between myocardial blood flow and myocardial function in patients with AS, no AS, and aortic valve sclerosis. METHODS AND RESULTS: We included consecutive patients who had resting transthoracic echocardiography and clinically indicated positron emission tomography myocardial perfusion imaging to capture their left ventricular ejection fraction, global longitudinal strain (GLS), and myocardial flow reserve (MFR). The primary outcome was major adverse cardiovascular event (all-cause mortality, myocardial infarction, or late revascularization). There were 2778 patients (208 with aortic sclerosis, 39 with prosthetic aortic valve, 2406 with no AS, and 54, 49, and 22 with mild, moderate, and severe AS, respectively). Increasing AS severity was associated with impaired MFR (P<0.001) and GLS (P<0.001), even when perfusion was normal. Statistically significant associations were noted between MFR and GLS, MFR and left ventricular ejection fraction, and MFR and left ventricular ejection fraction reserve. After a median follow-up of 349 (interquartile range, 116-662) days, 4 (7.4%), 5 (10.2%), and 6 (27.3%) patients experienced a major adverse cardiovascular event in the mild, moderate, and severe AS groups, respectively. In a matched-control analysis, patients with mild-to-moderate AS had higher rates of impaired MFR (52.9% versus 39.9%; P=0.048) and major adverse cardiovascular event (11.8% versus 3.0%; P=0.002). CONCLUSIONS: Despite lack of ischemia, as severity of AS increased, MFR decreased and GLS worsened, reflecting worse coronary microvascular health and myocardial remodeling. Positron emission tomography-derived MFR showed a significant independent correlation with left ventricular ejection fraction and GLS. Patients with prosthetic aortic valve showed a high prevalence of impaired MFR.

FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data.

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertension, mortality, and diabetes risks, utilizing only nine patient features. They achieved an average AUC of 0.90 ± 0.001 on the hypertension dataset, 0.90 ± 0.002 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset through tenfold cross-validation. Additionally, the models demonstrated strong performance with an average AUC of 0.89 ± 0.001 on the hypertension dataset, 0.90 ± 0.001 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset using bootstrap evaluation with 1000 resamples.

Epidemiology and Prognostic Implications of Coronary Artery Calcium in Asymptomatic Individuals With Prediabetes: A Multicohort Study.

OBJECTIVE: To describe the epidemiology and prognostic value of coronary artery calcium (CAC) in individuals with prediabetes. RESEARCH DESIGN AND METHODS: We pooled participants free of clinical atherosclerotic cardiovascular disease (ASCVD) from four prospective cohorts: the Multi-Ethnic Study of Atherosclerosis, Heinz Nixdorf Recall Study, Framingham Heart Study, and Jackson Heart Study. Two definitions were used for prediabetes: inclusive (fasting plasma glucose [FPG] ≥100 to <126 mg/dL and hemoglobin A1c [HbA1c] ≥5.7% to <6.5%, if available, and no glucose-lowering medications) and restrictive (FPG ≥110 to <126 mg/dL and HbA1c ≥5.7% to <6.5%, if available, among participants not taking glucose-lowering medications). RESULTS: The study included 13,376 participants (mean age 58 years; 54% women; 57% White; 27% Black). The proportions with CAC ≥100 were 17%, 22%, and 37% in those with euglycemia, prediabetes, and diabetes, respectively. Over a median (25th-75th percentile) follow-up time of 14.6 (interquartile range 7.8-16.4) years, individuals with prediabetes and CAC ≥100 had a higher unadjusted 10-year incidence of ASCVD (13.4%) than the overall group of those with diabetes (10.6%). In adjusted analyses, using the inclusive definition of prediabetes, compared with euglycemia, the hazard ratios (HRs) for ASCVD were 0.79 (95% CI 0.62, 1.01) for prediabetes and CAC 0, 0.70 (0.54, 0.89) for prediabetes and CAC 1-99, 1.54 (1.27, 1.88) for prediabetes and CAC ≥100, and 1.64 (1.39, 1.93) for diabetes. Using the restrictive definition, the HR for ASCVD was 1.63 (1.29, 2.06) for prediabetes and CAC ≥100. CONCLUSIONS: CAC ≥100 is frequent among individuals with prediabetes and identifies a high ASCVD risk subgroup in which the adjusted ASCVD risk is similar to that in individuals with diabetes.

Prediction of the development of new coronary atherosclerotic plaques with radiomics.

BACKGROUND: Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). METHODS: From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV â€‹≥ â€‹1 â€‹mm3, at follow-up CCTA in each segment. RESULTS: In total, 9583 normal coronary segments were identified from 1162 patients (60.3 â€‹± â€‹9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p â€‹> â€‹0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p â€‹< â€‹00.0001 compared to Models 1 and 2). CONCLUSION: Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT0280341.

Ejection fraction and ventricular volumes on rubidium positron emission tomography: Validation against cardiovascular magnetic resonance.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is the non-invasive gold standard for non-invasively determining left ventricular volumes (LVVs) and ejection fraction (EF). We aimed to assess the accuracy of LVV and left ventricular ejection fraction measured by positron emission tomography (PET) as compared to CMR. METHODS: Patients who underwent both PET and CMR within 1 year were identified from prospective institutional registries. Analysis was performed to evaluate the agreement between the raw and body-surface-area-normalized left ventricular volume (LVV) and EF derived from PET vs. those derived from CMR. RESULTS: The study population consisted of 669 patients (mean age 62 ± 13 years, 65% male). The median (interquartile range [IQR]) duration between CMR and PET imaging was 36 (7-118) days. The median (IQR) EF values were 52% (38-63%) on CMR and 53% (37-65%) on PET (mean difference: 0.53% ± 9.1, P = 0.129) with a strong correlation (Spearman rho = 0.84, P < 0.001; Intraclass Correlation Coefficient 0.84, 95% confidence interval [CI]: 0.82-0.86, P < 0.001; Lin's concordance correlation coefficient was 0.844, 95% CI: 0.822 to 0.865). Results were similar with LVV, normalized LVV/EF, and in subgroups of patients with reduced EF, coronary artery disease scar, and LV hypertrophy as well as in patients with defibrillators. However, PET tended to underestimate LVV compared to CMR. CONCLUSION: Our analysis showed a strong correlation of EF and LVV by PET against a reference standard of CMR, whereas PET significantly underestimated LVV, but not EF, compared to CMR.

Best Practices in Nuclear Imaging for the Diagnosis of Transthyretin Amyloid Cardiomyopathy (ATTR-CM) in KSA: The Eagle Eyes of Local Experts.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a complex and serious form of heart failure caused by the accumulation of transthyretin amyloid protein in the heart muscle. Variable symptoms of ATTR-CM can lead to a delayed diagnosis. Recognizing the diagnostic indicators is crucial to promptly detect this condition. A targeted literature review was conducted to examine the latest international consensus recommendations on a comprehensive diagnosis of ATTR-CM. Additionally, a panel consisting of nuclear medicine expert consultants (n = 10) and nuclear imaging technicians (n = 2) convened virtually from the Kingdom of Saudi Arabia (KSA) to formulate best practices for ATTR-CM diagnosis. The panel reached a consensus on a standard diagnostic pathway for ATTR-CM, which commences by evaluating the presence of clinical red flags and initiating a cardiac workup to assess the patient's echocardiogram. Cardiac magnetic resonance imaging may be needed, in uncertain cases. When there is a high suspicion of ATTR-CM, patients undergo nuclear scintigraphy and hematologic tests to rule out primary or light-chain amyloidosis. The expert panel emphasized that implementing best practices will support healthcare professionals in KSA to improve their ability to detect and diagnose ATTR-CM more accurately and promptly. Diagnosing ATTR-CM accurately and early can reduce morbidity and mortality rates through appropriate treatment.