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.

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.

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.

Progression of non-obstructive coronary plaque: a practical CCTA-based risk score from the PARADIGM registry.

OBJECTIVES: No clear recommendations are endorsed by the different scientific societies on the clinical use of repeat coronary computed tomography angiography (CCTA) in patients with non-obstructive coronary artery disease (CAD). This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD. METHODS: Patients were part of the Progression of AtheRosclerotic PlAque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry. Specifically, 370 (derivation cohort) and 219 (validation cohort) patients with two repeat, clinically indicated CCTA scans, non-obstructive CAD, and absence of high-risk plaque (≥ 2 high-risk features) at baseline CCTA were included. Disease progression was defined as the new occurrence of ≥ 50% stenosis and/or high-risk plaque at follow-up CCTA. RESULTS: In the derivation cohort, 104 (28%) patients experienced disease progression. The median time interval between the two CCTAs was 3.3 years (2.7-4.8). Odds ratios for disease progression derived from multivariable logistic regression were as follows: 4.59 (95% confidence interval: 1.69-12.48) for the number of plaques with spotty calcification, 3.73 (1.46-9.52) for the number of plaques with low attenuation component, 2.71 (1.62-4.50) for 25-49% stenosis severity, 1.47 (1.17-1.84) for the number of bifurcation plaques, and 1.21 (1.02-1.42) for the time between the two CCTAs. The C-statistics of the model were 0.732 (0.676-0.788) and 0.668 (0.583-0.752) in the derivation and validation cohorts, respectively. CONCLUSIONS: The new CCTA-based risk score is a simple and practical tool that can predict mid-term CAD progression in patients with known non-obstructive CAD. CLINICAL RELEVANCE STATEMENT: The clinical implementation of this new CCTA-based risk score can help promote the management of patients with non-obstructive coronary disease in terms of timing of imaging follow-up and therapeutic strategies. KEY POINTS: • No recommendations are available on the use of repeat CCTA in patients with non-obstructive CAD. • This new CCTA score predicts mid-term CAD progression in patients with non-obstructive stenosis at baseline. • This new CCTA score can help guide the clinical management of patients with non-obstructive CAD.

Deep neural network for identification of impaired microvascular and vasomotor function from stress electrocardiography.

Impaired microvascular and vasomotor function is a common consequence of aging, diabetes, and other risk factors, and is associated with adverse cardiac outcomes. Such impairments are not readily identified by standard clinical methods of cardiovascular testing such as coronary angiography and noninvasive single photon emission tomography (SPECT) myocardial perfusion imaging (MPI). We hypothesized that signals embedded within stress electrocardiograms (ECGs) identify individuals with microvascular and vasomotor dysfunction. Methods: We developed and validated a novel convolutional neural network (CNN) using stress and rest ECG data (ECG-Flow) to identify patients with impaired myocardial flow reserve (MFR) on quantitative positron emission tomography (PET) MPI (N=3887). Diagnostic accuracy was validated with an internal holdout set of patients undergoing stress PET MPI (N=963). The prognostic association of ECG-Flow with mortality was then evaluated in a separate cohort of patients undergoing SPECT MPI (N=5102). Results: ECG-Flow achieved good diagnostic accuracy for impaired MFR in the holdout PET cohort (AUC, sensitivity, specificity: 0.737, 71.1%, 65.7%). Abnormal ECG-Flow was found to be significantly associated with mortality in both PET holdout and SPECT MPI cohorts (adjusted HR 2.12 [95 ρ CI 1.45, 2.10], ρ = 0.0001, and 2.07 [1.82, 2.36], ρ < 0.0001, respectively). Conclusion: Signals predictive of microvascular and vasomotor dysfunction are embedded in stress ECG waveforms. These signals can be identified by deep learning methods and are related to prognosis in patients undergoing both stress PET and SPECT MPI.

Unveiling the Hidden Patterns: Multimodality Cardiac Imaging in Takotsubo Cardiomyopathy Diagnosis.

Takotsubo cardiomyopathy, also known as stress cardiomyopathy, is a reversible form of cardiomyopathy characterized by reduced ejection fraction with regional wall motion abnormalities, elevated cardiac enzyme levels, and signs of ischemia on electrocardiogram despite the absence of obstructive epicardial coronary artery disease. It is often preceded by intense emotional or physical illness stressors. This case describes a 65-year-old female patient who likely developed takotsubo cardiomyopathy precipitated by the stress of diverticulitis.

How early can atherosclerosis be detected by coronary CT angiography? Insights from quantitative CT analysis of serial scans in the PARADIGM trial.

BACKGROUND: Non-obstructing small coronary plaques may not be well recognized by expert readers during coronary computed tomography angiography (CCTA) evaluation. Recent developments in atherosclerosis imaging quantitative computed tomography (AI-QCT) enabled by machine learning allow for whole-heart coronary phenotyping of atherosclerosis, but its diagnostic role for detection of small plaques on CCTA is unknown. METHODS: We performed AI-QCT in patients who underwent serial CCTA in the multinational PARADIGM study. AI-QCT results were verified by a level III experienced reader, who was blinded to baseline and follow-up status of CCTA. This retrospective analysis aimed to characterize small plaques on baseline CCTA and evaluate their serial changes on follow-up imaging. Small plaques were defined as a total plaque volume <50 â€‹mm3. RESULTS: A total of 99 patients with 502 small plaques were included. The median total plaque volume was 6.8 â€‹mm3 (IQR 3.5-13.9 â€‹mm3), most of which was non-calcified (median 6.2 â€‹mm3; 2.9-12.3 â€‹mm3). The median age at the time of baseline CCTA was 61 years old and 63% were male. The mean interscan period was 3.8 â€‹± â€‹1.6 years. On follow-up CCTA, 437 (87%) plaques were present at the same location as small plaques on baseline CCTA; 72% were larger and 15% decreased in volume. The median total plaque volume and non-calcified plaque volume increased to 18.9 â€‹mm3 (IQR 8.3-45.2 â€‹mm3) and 13.8 â€‹mm3 (IQR 5.7-33.4 â€‹mm3), respectively, among plaques that persisted on follow-up CCTA. Small plaques no longer visualized on follow-up CCTA were significantly more likely to be of lower volume, shorter in length, non-calcified, and more distal in the coronary artery, as compared with plaques that persisted at follow-up. CONCLUSION: In this retrospective analysis from the PARADIGM study, small plaques (<50 â€‹mm3) identified by AI-QCT persisted at the same location and were often larger on follow-up CCTA.

Integrated myocardial flow reserve (iMFR) assessment: diffuse atherosclerosis and microvascular dysfunction are more strongly associated with mortality than focally impaired perfusion.

BACKGROUND AND AIMS: Although treatment of ischemia-causing epicardial stenoses may improve symptoms of ischemia, current evidence does not suggest that revascularization improves survival. Conventional myocardial ischemia imaging does not uniquely identify diffuse atherosclerosis, microvascular dysfunction, or nonobstructive epicardial stenoses. We sought to evaluate the prognostic value of integrated myocardial flow reserve (iMFR), a novel noninvasive approach to distinguish the perfusion impact of focal atherosclerosis from diffuse coronary disease. METHODS: This study analyzed a large single-center registry of consecutive patients clinically referred for rest-stress myocardial perfusion positron emission tomography. Cox proportional hazards modeling was used to assess the association of two previously reported and two novel perfusion measures with mortality risk: global stress myocardial blood flow (MBF); global myocardial flow reserve (MFR); and two metrics derived from iMFR analysis: the extents of focal and diffusely impaired perfusion. RESULTS: In total, 6867 patients were included with a median follow-up of 3.4 years [1st-3rd quartiles, 1.9-5.0] and 1444 deaths (21%). Although all evaluated perfusion measures were independently associated with death, diffusely impaired perfusion extent (hazard ratio 2.65, 95%C.I. [2.37-2.97]) and global MFR (HR 2.29, 95%C.I. [2.08-2.52]) were consistently stronger predictors than stress MBF (HR 1.62, 95%C.I. [1.46-1.79]). Focally impaired perfusion extent (HR 1.09, 95%C.I. [1.03-1.16]) was only moderately related to mortality. Diffusely impaired perfusion extent remained a significant independent predictor of death when combined with global MFR (p < 0.0001), providing improved risk stratification (overall net reclassification improvement 0.246, 95%C.I. [0.183-0.310]). CONCLUSIONS: The extent of diffusely impaired perfusion is a strong independent and additive marker of mortality risk beyond traditional risk factors, standard perfusion imaging, and global MFR, while focally impaired perfusion is only moderately related to mortality.