Clinical spectrum of Transthyretin amyloidogenic mutations among diverse population origins.

PURPOSE: Coding mutations in the Transthyretin (TTR) gene cause a hereditary form of amyloidosis characterized by a complex genotype-phenotype correlation with limited information regarding differences among worldwide populations. METHODS: We compared 676 diverse individuals carrying TTR amyloidogenic mutations (rs138065384, Phe44Leu; rs730881165, Ala81Thr; rs121918074, His90Asn; rs76992529, Val122Ile) to 12,430 non-carriers matched by age, sex, and genetically-inferred ancestry to assess their clinical presentations across 1,693 outcomes derived from electronic health records in UK biobank. RESULTS: In individuals of African descent (AFR), Val122Ile mutation was linked to multiple outcomes related to the circulatory system (fold-enrichment = 2.96, p = 0.002) with the strongest associations being cardiac congenital anomalies (phecode 747.1, p = 0.003), endocarditis (phecode 420.3, p = 0.006), and cardiomyopathy (phecode 425, p = 0.007). In individuals of Central-South Asian descent (CSA), His90Asn mutation was associated with dermatologic outcomes (fold-enrichment = 28, p = 0.001). The same TTR mutation was linked to neoplasms in European-descent individuals (EUR, fold-enrichment = 3.09, p = 0.003). In EUR, Ala81Thr showed multiple associations with respiratory outcomes related (fold-enrichment = 3.61, p = 0.002), but the strongest association was with atrioventricular block (phecode 426.2, p = 2.81 × 10- 4). Additionally, the same mutation in East Asians (EAS) showed associations with endocrine-metabolic traits (fold-enrichment = 4.47, p = 0.003). In the cross-ancestry meta-analysis, Val122Ile mutation was associated with peripheral nerve disorders (phecode 351, p = 0.004) in addition to cardiac congenital anomalies (fold-enrichment = 6.94, p = 0.003). CONCLUSIONS: Overall, these findings highlight that TTR amyloidogenic mutations present ancestry-specific and ancestry-convergent associations related to a range of health domains. This supports the need to increase awareness regarding the range of outcomes associated with TTR mutations across worldwide populations to reduce misdiagnosis and delayed diagnosis of TTR-related amyloidosis.

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.

BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.

18F-FDG PET/CT and radiolabeled leukocyte SPECT/CT imaging for the evaluation of cardiovascular infection in the multimodality context: ASNC Imaging Indications (ASNC I2) Series Expert Consensus Recommendations from ASNC, AATS, ACC, AHA, ASE, EANM, HRS, IDSA, SCCT, SNMMI, and STS.

This document on cardiovascular infection, including infective endocarditis, is the first in the American Society of Nuclear Cardiology Imaging Indications (ASNC I2) series to assess the role of radionuclide imaging in the multimodality context for the evaluation of complex systemic diseases with multi-societal involvement including pertinent disciplines. A rigorous modified Delphi approach was used to determine consensus clinical indications, diagnostic criteria, and an algorithmic approach to diagnosis of cardiovascular infection including infective endocarditis. Cardiovascular infection incidence is increasing and is associated with high morbidity and mortality. Current strategies based on clinical criteria and an initial echocardiographic imaging approach are effective but often insufficient in complicated cardiovascular infection. Radionuclide imaging with 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and single photon emission computed tomography/CT leukocyte scintigraphy can enhance the evaluation of suspected cardiovascular infection by increasing diagnostic accuracy, identifying extracardiac involvement, and assessing cardiac implanted device pockets, leads, and all portions of ventricular assist devices. This advanced imaging can aid in key medical and surgical considerations. Consensus diagnostic features include focal/multi-focal or diffuse heterogenous intense 18F-FDG uptake on valvular and prosthetic material, perivalvular areas, device pockets and leads, and ventricular assist device hardware persisting on non-attenuation corrected images. There are numerous clinical indications with a larger role in prosthetic valves, and cardiac devices particularly with possible infective endocarditis or in the setting of prior equivocal or non-diagnostic imaging. Illustrative cases incorporating these consensus recommendations provide additional clarification. Future research is necessary to refine application of these advanced imaging tools for surgical planning, to identify treatment response, and more.

Unlocking Diversity in Cardiovascular Clinical Research: Lessons from the Screening for Cardiac Amyloidosis With Nuclear Imaging in a Minority Populations Study.

BACKGROUND: The Screening for Cardiac Amyloidosis with Nuclear Imaging in Minority Populations study seeks to determine the prevalence of transthyretin cardiac amyloidosis (ATTR-CA) among older Black or Caribbean Hispanic individuals with heart failure and an increased wall thickness. We noticed varied recruitment percentages across the recruiting sites and sought to determine the factors associated with greater percentage enrollment of eligible participants. METHODS: The percentage of enrolled to eligible participants was calculated across study sites. Baseline demographic and clinical characteristics, health literacy, trust in providers, perceived discrimination, area deprivation index (ADI) and English proficiency were compared by site using Kruskal-Wallis's test or one-way ANOVA for continuous variables and the Chi-Square test or Fisher's exact test for categorical variables. Wilcoxon rank sum and Chi-Square tests, with multiple comparisons correction using the false discovery rate (FDR) method, were used as post-hoc analysis when results were statistically significant. RESULTS: Among the four recruiting sites, Boston Medical Center, Columbia University Irving Medical Center, Harlem Hospital and Yale University, which employed different recruitment approaches, the percentage of participants enrolled among eligible participants differed, with the highest rate at Harlem Hospital (n=149 of 310, 48%), followed by Yale University (n=27 of 67, 40%), Boston University (n=247 of 655, 38%), and Columbia University (n=137of 442, 32%), p <0.01. Direct recruitment by the primary cardiovascular care team providing clinical care was associated with higher percent enrolled across sites as were higher education levels and English proficiency. Enrollment differences across sites were not associated with the number of chronic diseases, physician trust, perceived discrimination, or health literacy. CONCLUSIONS: Recruitment of eligible under-represented minorities (URMs) in SCAN-MP was associated with approaches employed in recruitment, including direct initial contact by the primary cardiovascular care team providing the potential participant's clinical care. Such data may help improve approaches to more successful recruitment of URMs in clinical research.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

PURPOSE: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

18F-FDG PET/CT and radiolabeled leukocyte SPECT/CT imaging for the evaluation of cardiovascular infection in the multimodality context: ASNC Imaging Indications (ASNC I2) Series Expert Consensus Recommendations from ASNC, AATS, ACC, AHA, ASE, EANM, HRS, IDSA, SCCT, SNMMI, and STS.

This document on cardiovascular infection, including infective endocarditis, is the first in the American Society of Nuclear Cardiology Imaging Indications (ASNC I2) series to assess the role of radionuclide imaging in the multimodality context for the evaluation of complex systemic diseases with multi-societal involvement including pertinent disciplines. A rigorous modified Delphi approach was used to determine consensus clinical indications, diagnostic criteria, and an algorithmic approach to diagnosis of cardiovascular infection including infective endocarditis. Cardiovascular infection incidence is increasing and is associated with high morbidity and mortality. Current strategies based on clinical criteria and an initial echocardiographic imaging approach are effective but often insufficient in complicated cardiovascular infection. Radionuclide imaging with 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (CT) and single photon emission computed tomography/CT leukocyte scintigraphy can enhance the evaluation of suspected cardiovascular infection by increasing diagnostic accuracy, identifying extracardiac involvement, and assessing cardiac implanted device pockets, leads, and all portions of ventricular assist devices. This advanced imaging can aid in key medical and surgical considerations. Consensus diagnostic features include focal/multi-focal or diffuse heterogenous intense 18F-FDG uptake on valvular and prosthetic material, perivalvular areas, device pockets and leads, and ventricular assist device hardware persisting on non-attenuation corrected images. There are numerous clinical indications with a larger role in prosthetic valves, and cardiac devices particularly with possible infective endocarditis or in the setting of prior equivocal or non-diagnostic imaging. Illustrative cases incorporating these consensus recommendations provide additional clarification. Future research is necessary to refine application of these advanced imaging tools for surgical planning, to identify treatment response, and more.

Links Between Obstructive Sleep Apnea and Myocardial Blood Flow Changes Impacting Adverse Cardiovascular Disease-related Outcomes.

PURPOSE OF REVIEW: Recent studies have demonstrated an association between obstructive sleep apnea (OSA) and abnormal myocardial blood flow (MBF), myocardial flow reserve (MFR), and coronary microvascular dysfunction (CMD). Here, we review the evidence and describe the potential underlying mechanisms linking OSA to abnormal MBF. Examining relevant studies, we assess the impact of OSA-specific therapy, such as continuous positive airway pressure (CPAP), on MBF. RECENT FINDINGS: Recent studies suggest an association between moderate to severe OSA and abnormal MBF/MFR. OSA promotes functional and structural abnormalities of the coronary microcirculation. OSA also promotes the uncoupling of MBF to cardiac work. In a handful of studies with small sample sizes, CPAP therapy improved MBF/MFR. Moderate to severe OSA is associated with abnormal MFR, suggesting an association with CMD. Evidence suggests that CPAP therapy improves MBF. Future studies must determine the clinical impact of improved MBF with CPAP.

Severe aortic stenosis detection by deep learning applied to echocardiography.

BACKGROUND AND AIMS: Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS: In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION: This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.

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.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging.

BACKGROUND: Machine learning (ML) has been previously applied for prognostication in patients undergoing SPECT myocardial perfusion imaging (MPI). We evaluated whether including attenuation CT coronary artery calcification (CAC) scoring improves ML prediction of major adverse cardiovascular events (MACE) in patients undergoing SPECT/CT MPI. METHODS: From the REFINE SPECT Registry 4770 patients with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 45% female). ML algorithm (XGBoost) inputs were clinical risk factors, stress variables, SPECT imaging parameters, and expert-observer CAC scoring using CT attenuation correction scans performed to obtain CT attenuation maps. The ML model was trained and validated using tenfold hold-out validation. Receiver Operator Characteristics (ROC) curves were analyzed for prediction of MACE. MACE-free survival was evaluated with standard survival analyses. RESULTS: During a median follow-up of 24.1 months, 475 patients (10%) experienced MACE. Higher area under the ROC curve for MACE was observed with ML when CAC scoring was included (CAC-ML score, 0.77, 95% confidence interval [CI] 0.75-0.79) compared to ML without CAC (ML score, 0.75, 95% CI 0.73-0.77, P = .005) and when compared to CAC score alone (0.71, 95% CI 0.68-0.73, P < .001). Among clinical, imaging, and stress parameters, CAC score had highest variable importance for ML. On survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (hazard ratio 5.3, 95% CI 4.3-6.5, P < .001). CONCLUSION: Integration of attenuation CT CAC scoring improves the predictive value of ML risk score for MACE prediction in patients undergoing SPECT MPI.

Hot spot imaging in cardiovascular diseases: an information statement from SNMMI, ASNC, and EANM.

This information statement from the Society of Nuclear Medicine and Molecular Imaging, American Society of Nuclear Cardiology, and European Association of Nuclear Medicine describes the performance, interpretation, and reporting of hot spot imaging in nuclear cardiology. The field of nuclear cardiology has historically focused on cold spot imaging for the interpretation of myocardial ischemia and infarction. Hot spot imaging has been an important part of nuclear medicine, particularly for oncology or infection indications, and the use of hot spot imaging in nuclear cardiology continues to expand. This document focuses on image acquisition and processing, methods of quantification, indications, protocols, and reporting of hot spot imaging. Indications discussed include myocardial viability, myocardial inflammation, device or valve infection, large vessel vasculitis, valve calcification and vulnerable plaques, and cardiac amyloidosis. This document contextualizes the foundations of image quantification and highlights reporting in each indication for the cardiac nuclear imager.

Diagnostic performance characteristics of planar quantitative and semi-quantitative parameters of Tc99m pyrophosphate (PYP) imaging for diagnosis of transthyretin (ATTR) cardiac amyloidosis: the SCAN-MP study.

BACKGROUND: The optimal heart-to-contralateral chest (H/CL) ratio threshold for non-invasive diagnosis of transthyretin cardiac amyloidosis (ATTR-CA) using Tc99m pyrophosphate (PYP) imaging in a population with low pretest probability is not known. METHODS: Using myocardial PYP retention by SPECT as the reference standard, we evaluated the diagnostic performance of different semi-quantitative and quantitative (H/CL chest ratio) planar parameters obtained from 3-hour PYP imaging in a prospectively recruited cohort of minority older adults with heart failure and increased LV wall thickness. RESULTS: Of 229 patients, 14 were found to have ATTR-CA (6.1%). No PYP uptake (grade 0) was observed in 77% of scans, all grade 3 scans were ATTR-CA, and only 4 of 11 (36%) grade 2 scans were ATTR-CA. An H/CL threshold of ≥ 1.4 maximized specificity (99%) and positive predictive value (93%) but resulted in decreased sensitivity (93%), compared to the ≥ 1.3 threshold which had 100% sensitivity. CONCLUSION: Among patients with a low pretest likelihood of ATTR-CA, planar interpretation, while useful to exclude disease, must be interpreted with caution. H/CL ratio threshold of ≥ 1.3 resulted in clinically important misclassifications. These data suggest that quantitative planar imaging thresholds may not be appropriate to apply in low pretest likelihood populations being evaluated for ATTR-CA.

Prevalence and significance of extracardiac uptake on pyrophosphate imaging in the SCAN-MP study: the first 379 cases.

INTRODUCTION: Technetium-labeled bone-avid radiotracers can be used to diagnose transthyretin cardiac amyloidosis (ATTR-CA). Extracardiac uptake of technetium pyrophosphate (Tc-99m PYP) in this context has not been extensively explored and its significance is not well characterized. We assessed extracardiac Tc-99m PYP uptake in individuals undergoing nuclear scintigraphy and the extent of clinically actionable findings. METHODS: The Screening for Cardiac Amyloidosis with Nuclear Imaging in Minority Populations (SCAN-MP) study utilizes Tc-99m PYP imaging to identify ATTR-CA in self-identified Black and Caribbean Hispanic participants ≥ 60 years old with heart failure. We characterized the distribution of extracardiac uptake, including stratification of findings by timing of scan (1 hour vs 3 hours after Tc-99m PYP administration) and noted any additional testing in these subjects. RESULTS: Of 379 participants, 195 (51%) were male, 306 (81%) Black race, and 120 (32%) Hispanic ethnicity; mean age was 73 years. Extracardiac Tc-99m PYP uptake was found in 42 subjects (11.1%): 21 with renal uptake only, 14 with bone uptake only, 4 with both renal and bone uptake, 2 with breast uptake, and 1 with thyroid uptake. Extracardiac uptake was more common in subjects with Tc-99m PYP scans at 1 hour (23.8%) than at 3 hours (6.2%). Overall, four individuals (1.1%) had clinically actionable findings. CONCLUSION: Extracardiac Tc-99m PYP uptake manifested in about 1 in 9 SCAN-MP subjects but was clinically actionable in only 1.1% of cases.

Impaired left-ventricular global longitudinal strain by feature-tracking cardiac MRI predicts mortality in systemic sclerosis.

Impaired left-ventricular (LV) and right-ventricular (RV) cardiac magnetic resonance (CMR) strain has been documented in systemic sclerosis (SSc). However, it is unknown whether the CMR strain is predictive of adverse outcomes in SSc. Therefore, we set out to investigate the prognostic value of CMR strain in SSc. Patients with SSc who underwent CMR for clinical indications between 11/2010 and 07/2020 were retrospectively studied. LV and RV strain was evaluated by feature tracking. The association between strain, late gadolinium enhancement (LGE), and survival was evaluated with time to event and Cox-regression analyses. During the study period, 42 patients with SSc (age: 57 ± 14 years, 83% female, 57% limited cutaneous SSc, SSc duration: 7 ± 8 years) underwent CMR. During the median follow-up of 3.6 years, 11 patients died (26%). Compared to surviving patients, patients who died had significantly worse LV GLS (- 8.2 ± 6.2% versus - 12.1 ± 2.9%, p = 0.03), but no difference in LV global radial, circumferential, or RV strain values. Patients within the quartile of most impaired LV GLS (≥ - 12.8%, n = 10) had worse survival when compared to patients with preserved LV GLS (< - 12.8%, n = 32, log-rank p = 0.02), which persisted after controlling for LV cardiac output, LV cardiac index, reduced LV ejection fraction, or presence of LGE. In addition, patients who had both impaired LV GLS and LGE (n = 5) had worse survival than patients with LGE or impaired GLS alone (n = 14) and compared to those without any of these features (n = 17, p = 0.003). In our retrospective cohort of patients with SSc undergoing CMR for clinical indications, LV GLS and LGE were found to be predictive of overall survival.

Risk and predictors of mortality after implantable cardioverter-defibrillator implantation in patients with sarcoid cardiomyopathy.

BACKGROUND: Implantable cardioverter-defibrillators (ICDs) are recommended for patients with cardiac sarcoidosis (CS) with an indication for pacing, prior ventricular arrhythmias, cardiac arrest, or left ventricular ejection fraction <35%, but data on outcomes are limited. METHODS: Using data from the National Cardiovascular Data Registry ICD Registry between April 1, 2010 and December 31, 2015, we evaluated a propensity matched cohort of CS patients implanted with ICDs versus non-ischemic cardiomyopathies (NICM). We compared mortality using Kaplan-Meier survival curves and Cox proportional hazards models. RESULTS: We identified 1,638 patients with CS and 8,190 propensity matched patients with NICM. The rate of death at 1 and 2 years was similar in patients with CS and patients with NICM (5.2% vs 5.4%, P = 0.75 and 9.0% vs 9.3%, P = 0.72, respectively). After adjusting for other covariates, patients with CS had similar mortality at 2 years after ICD implantations compared with NICM patients (RR 1.03, 95% CI 0.87-1.23). Among patients with CS, multivariable logistic regression identified 6 factors significantly associated with increased 2-year mortality: presence of heart failure (HR 1.92, 95% CI 1.44-3.22), New York Heart Association (NYHA) Class III heart failure (HR 1.68, 95% CI 1.16-2.45), NYHA Class IV heart failure (HR 3.08, 95% CI 1.49-6.39), atrial fibrillation/flutter (HR 1.66, 95% CI 1.17-2.35), chronic lung disease (HR 1.64, 95% CI 1.17-2.29), creatinine >2.0 mg/dL (HR 4.07, 95% CI 2.63-6.30), and paced rhythm (HR 2.66, 95% CI 1.07-6.59). CONCLUSION: Mortality following ICD implantation was similar in CS patients compared with propensity matched NICM patients. Presence of heart failure, NYHA class, atrial fibrillation/flutter, chronic lung disease, renal dysfunction, and paced rhythm at time of implantation were all predictors of increased 2-year mortality among CS patients with ICDs.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

PURPOSE: Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (µ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without µ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS: For dedicated SPECT, we developed strategies to predict truncated µ-maps from NAC images reconstructed with a small matrix, or full µ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict µ-maps or AC images. RESULTS: For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full µ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS: We developed strategies of generating µ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Potential novel imaging targets of inflammation in cardiac sarcoidosis.

Cardiac sarcoidosis (CS) is an inflammatory disease with high morbidity and mortality, with a pathognomonic feature of non-caseating granulomatous inflammation. While 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a well-established modality to image inflammation and diagnose CS, there are limitations to its specificity and reproducibility. Imaging focused on the molecular processes of inflammation including the receptors and cellular microenvironments present in sarcoid granulomas provides opportunities to improve upon FDG-PET imaging for CS. This review will highlight the current limitations of FDG-PET imaging for CS while discussing emerging new nuclear imaging molecular targets for the imaging of cardiac sarcoidosis.

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

BACKGROUND: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. METHODS: CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance. RESULTS: Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net. CONCLUSIONS: Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.

Machine learning to predict abnormal myocardial perfusion from pre-test features.

BACKGROUND: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features. METHODS: We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation. RESULTS: In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750-0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001). CONCLUSION: ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.