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.

Prognostic value of myocardial flow reserve vs corrected myocardial flow reserve in patients without obstructive coronary artery disease.

BACKGROUND: Myocardial flow reserve (MFR) by positron emission tomography (PET) is a validated measure of cardiovascular risk. Elevated resting rate pressure product (RPP = heart rate x systolic blood pressure) can cause high resting myocardial blood flow (MBF), resulting in reduced MFR despite normal/near-normal peak stress MBF. When resting MBF is high, it is not known if RPP-corrected MFR (MFRcorrected) helps reclassify CV risk. We aimed to study this question in patients without obstructive coronary artery disease (CAD). METHODS: We retrospectively studied patients referred for rest/stress cardiac PET at our center from 2006 to 2020. Patients with abnormal perfusion (summed stress score >3) or prior coronary artery bypass grafting (CABG) were excluded. MFRcorrected was defined as stress MBF/corrected rest MBF where corrected rest MBF = rest MBF x 10,000/RPP. The primary outcome was major cardiovascular events (MACE): cardiovascular death or myocardial infarction. Associations of MFR and MFRcorrected with MACE were assessed using unadjusted and adjusted Cox regression. RESULTS: 3276 patients were followed for a median of 7 (IQR 3-12) years. 1685 patients (51%) had MFR <2.0, and of those 366 (22%) had an MFR ≥2.0 after RPP correction. MFR <2.0 was associated with an increased absolute risk of MACE (HR 2.24 [1.79-2.81], P < 0.0001). Among patients with MFR <2.0, the risk of MACE was not statistically different between patients with an MFRcorrected ≥2.0 compared with those with MFRcorrected <2.0 (1.9% vs 2.3% MACE/year, HR 0.84 [0.63-1.13], P = 0.26) even after adjustment for confounders (P = 0.66). CONCLUSIONS: In patients without overt obstructive CAD and MFR< 2.0, there was no significant difference in cardiovascular risk between patients with discordant (≥2.0) and concordant (<2) MFR following RPP correction. This suggests that RPP-corrected MFR may not consistently provide accurate risk stratification in patients with normal perfusion and MFR <2.0. Stress MBF and uncorrected MFR should be reported to more reliably convey cardiovascular risk beyond perfusion results.

Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.

BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-photon emission computed tomography (SPECT)/computed tomography (CT). METHODS: We identified a primary cohort of 77 subjects at Brigham and Women's Hospital and a validation cohort of 93 consecutive patients imaged at the University of Pennsylvania who underwent SPECT/CT with PYP and HDP, respectively, for evaluation of ATTR-CM. Global heart regions of interest (ROIs) were traced on CT axial slices from the apex of the ventricle to the carina. Myocardial images were visually scored as grade 0 (no uptake), 1 (uptakeribs). A 2D U-net architecture was used to develop whole-heart segmentations for CT scans. Uptake was determined by calculating a heart-to-blood pool (HBP) ratio between the maximal counts value of the total heart region and the maximal counts value of the most superior ROI. RESULTS: Deep learning and ground truth segmentations were comparable (p=0.63). A total of 42 (55%) patients had abnormal myocardial uptake on visual assessment. Automated quantification of the mean HBP ratio in the primary cohort was 3.1±1.4 versus 1.4±0.2 (p<0.01) for patients with positive and negative cardiac uptake, respectively. The model had 100% accuracy in the primary cohort and 98% in the validation cohort. CONCLUSION: We have developed a highly accurate diagnostic tool for automatically segmenting and identifying myocardial uptake suggestive of ATTR-CM.

COMPARATIVE EFFECTIVENESS OF PET AND SPECT MPI FOR PREDICTING RISK IN PATIENTS WITH CARDIOMETABOLIC DISEASE.

BACKGROUND: The epidemiology of coronary artery disease (CAD) has shifted, with increasing prevalence of cardiometabolic disease and decreasing findings of obstructive CAD on myocardial perfusion imaging (MPI). Coronary microvascular dysfunction (CMD), defined as impaired myocardial flow reserve (MFR) by positron emission tomography (PET), has emerged as a key mediator of risk. We aimed to assess whether PET MFR provides additive value for risk stratification of cardiometabolic disease patients compared with single-photon emission computed tomography (SPECT) MPI. METHODS: We retrospectively followed patients referred for PET, exercise SPECT, or pharmacologic SPECT MPI with cardiometabolic disease (obesity, diabetes, or chronic kidney disease) and without known CAD. We compared rates and hazards of composite MACE (annualized cardiac mortality or acute myocardial infarction) among propensity-matched PET and SPECT patients using Poisson and Cox regression. Normal SPECT was defined as a total perfusion deficit (TPD) <5% reflecting the absence of obstructive CAD. Normal PET was defined as TPD <5% plus MFR ≥2.0. RESULTS: Among 21,544 patients referred from 2006-2020, cardiometabolic disease was highly prevalent (PET: 2308 [67%], SPECT: 9984 [55%]) and higher among patients referred to PET (p <0.001). Obstructive CAD findings (TPD >5%) were uncommon (PET: 21% and SPECT 11%). Conversely, impaired MFR on PET (<2.0) was common (62%). In propensity-matched analysis over a median 6.4-year follow-up, normal PET identified low-risk (0.9%/year MACE) patients, and abnormal PET identified high-risk (4.2%/year MACE) patients with cardiometabolic disease; conversely, those with normal pharmacologic SPECT remained moderate-risk (1.6%/year, p<0.001 compared to normal PET). CONCLUSIONS: Cardiometabolic disease is common among patients referred for MPI and is associated with heterogenous level of risk. Compared with pharmacologic SPECT, PET with MFR can detect nonobstructive CAD including CMD and can more accurately discriminate low-risk from higher-risk individuals.

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.

Cardiac magnetic resonance biomarkers as surrogate endpoints in cardiovascular trials for myocardial diseases.

Cardiac magnetic resonance offers multiple facets in the diagnosis, risk stratification, and management of patients with myocardial diseases. Particularly, its feature to precisely monitor disease activity lends itself to quantify response to novel therapeutics. This review critically appraises the value of cardiac magnetic resonance imaging biomarkers as surrogate endpoints for prospective clinical trials. The primary focus is to comprehensively outline the value of established cardiac magnetic resonance parameters in myocardial diseases. These include heart failure, cardiac amyloidosis, iron overload cardiomyopathy, hypertrophic cardiomyopathy, cardio-oncology, and inflammatory cardiomyopathies like myocarditis and sarcoidosis.

Recovery Rates of Diagnostic Cardiac Procedural Volume in Oceania 1 Year Into COVID-19: The IAEA Non-Invasive Cardiology Protocol Survey on COVID-19 (INCAPS COVID 2).

AIM: The aim of this study was to assess the recovery rates of diagnostic cardiac procedure volumes in the Oceania Region, midway through the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A survey was performed comparing procedure volumes between March 2019 (pre-pandemic), April 2020 (during first wave of COVID-19 pandemic), and April 2021 (1 year into the COVID-19 pandemic). A total of 31 health care facilities within Oceania that perform cardiac diagnostic procedures were surveyed, including a mixture of metropolitan and regional, hospital and outpatient, public and private sites, as well as teaching and non-teaching hospitals. A comparison was made with 549 centres in 96 countries in the rest of the world (RoW) outside of Oceania. The total number and median percentage change in procedure volume were measured between the three timepoints, compared by test type and by facility. RESULTS: A total of 11,902 cardiac diagnostic procedures were performed in Oceania in April 2021 as compared with 11,835 pre-pandemic in March 2019 and 5,986 in April 2020; whereas, in the RoW, 499,079 procedures were performed in April 2021 compared with 497,615 pre-pandemic in March 2019 and 179,014 in April 2020. There was no significant difference in the median recovery rates for total procedure volumes between Oceania (-6%) and the RoW (-3%) (p=0.81). While there was no statistically significant difference in percentage recovery been functional ischaemia testing and anatomical coronary testing in Oceania as compared with the RoW, there was, however, a suggestion of poorer recovery in anatomical coronary testing in Oceania as compared with the RoW (CT coronary angiography -16% in Oceania vs -1% in RoW, and invasive coronary angiography -20% in Oceania vs -9% in RoW). There was no statistically significant difference in recovery rates in procedure volume between metropolitan vs regional (p=0.44), public vs private (p=0.92), hospital vs outpatient (p=0.79), or teaching vs non-teaching centres (p=0.73). CONCLUSIONS: Total cardiology procedure volumes in Oceania normalised 1 year post-pandemic compared to pre-pandemic levels, with no significant difference compared with the RoW and between the different types of health care facilities.

99mTc Bone-Avid Tracer Cardiac Scintigraphy: Role in Noninvasive Diagnosis of Transthyretin Cardiac Amyloidosis.

Transthyretin cardiac amyloidosis (ATTR-CA) is an overlooked cause of heart failure, with substantial morbidity and mortality. The emergence of several novel therapies has fueled the interest in early and accurate diagnosis of ATTR-CA so that potentially life-saving pharmacologic therapy can be administered in a timely manner. The most promising imaging modality and biomarker is SPECT imaging with technetium 99m (99mTc)-radiolabeled bone-seeking tracers, which have high specificity in the diagnosis of ATTR-CA, potentially obviating biopsy. In this article, the authors provide a focused review on the use of 99mTc pyrophosphate (PYP), 3,3-diphosphono-1,2-propanodicarboxylic acid (DPD), and hydroxymethylene diphosphonate (HMDP) for diagnosis of ATTR-CA, present a systematic approach to interpretation of the scans, and highlight several common pitfalls to illustrate important diagnostic principles for accurate interpretation of these images. The authors indicate when to use endomyocardial biopsy for the diagnosis of cardiac amyloidosis and conclude with a section on quantitation of 99mTc-PYP/DPD/HMDP imaging.

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.

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.

Low coronary flow relative to myocardial mass predicts heart failure in symptomatic hypertensive patients with no obstructive coronary artery disease.

AIMS: The transition from hypertension to heart failure (HF) remains poorly understood. We hypothesized that insufficient perfusion to match global metabolic demand, reflected by a low ratio of myocardial blood flow to global myocardial mass, may be a HF risk marker. METHODS AND RESULTS: A retrospective cohort (n = 346) of patients with hypertension who underwent clinical positron emission tomography (PET) myocardial perfusion imaging for chest pain and/or dyspnoea at Brigham and Women's Hospital (Boston, MA, USA) were studied. Patients without obstructive coronary artery disease by history or PET perfusion (summed stress score <3), HF, cardiomyopathy, or ejection fraction (EF) <40% were followed for HF hospitalization (primary outcome), all-cause death, and their composite. Myocardial blood flow, left ventricular (LV) mass, volumes, and EF were obtained from PET, and a 'flow/mass ratio' was determined as hyperaemic myocardial blood flow over LV mass indexed to body surface area. A lower flow/mass ratio was independently associated with larger end-diastolic (ß = -0.44, P < 0.001) and end-systolic volume (ß = -0.48, P < 0.001) and lower EF (ß = 0.33, P < 0.001). A flow/mass ratio below the median was associated with an adjusted hazard ratio of 2.47 [95% confidence interval (CI) 1.24-4.93; P = 0.01] for HF hospitalization, 1.95 (95% CI 1.12-3.41; P = 0.02) for death, and 2.20 (95% CI 1.39-3.49; P < 0.001) for the composite. CONCLUSION: An integrated physiological measure of insufficient myocardial perfusion to match global metabolic demand identifies subclinical hypertensive heart disease and elevated risk of HF and death in symptomatic patients with hypertension but without flow-limiting coronary artery disease.

ASNC Imaging Indications (ASNC-I2): Multisocietal indications for radionuclide imaging in the multimodality context-Series rationale and methodology.

BACKGROUND: Appropriate use criteria (AUC) enhance application of cardiovascular imaging techniques but have been applied in limited settings, primarily in common cardiovascular disease processes. There are several complex systemic diseases with cardiovascular implications and special populations with unique cardiovascular considerations that could benefit from appropriateness analysis. Moreover, the high medical complexity of these topics indicate they would benefit from high-yield expert consensus recommendations of the available imaging options. The ASNC Imaging Indications (ASNC-I2) Series will provide a concise overview of relevant disease processes and their multimodality evaluation and will provide consensus clinical indications, diagnostic criteria, and clinical algorithms with representative clinical cases. METHODS: For each ASNC-I2 document, a diverse writing group and rating panel will be composed of experts from societies pertinent to the topic, including relevant imaging societies and clinical societies that manage the disease under evaluation. The rating panel will follow robust modified Delphi methodology and commonly-accepted appropriateness methods to create consensus diagnostic criteria, clinical algorithms, and clinical indications that they will then rate with level of agreement recorded. The clinical and imaging experts will provide concise, high-yield clinical summaries of the disease process, the non-imaging evaluation, and multimodality imaging. Relevant cases will be provided highlighting application of the diagnostic criteria and clinical algorithms. CONCLUSION: The ASNC Imaging Indications (ASNC-I2) Series will complement the diverse portfolio of documents from ASNC. It will use a multisocietal approach with robust appropriateness methodology to guide use of radionuclide imaging in the multimodality imaging context for the cardiovascular care of patients.

Inter-observer reproducibility and intra-observer repeatability in 99mTc-pyrophosphate scan interpretation for diagnosis of transthyretin cardiac amyloidosis.

AIM: The purpose of this study was to determine the inter- and intra-observer variability in 99mtechnetium-pyrophosphate (99mTc-PYP) scan interpretation for diagnosis of transthyretin cardiac amyloidosis (ATTR). METHODS AND RESULTS: Our study cohort comprised 100 consecutive subjects referred for 99mTc-PYP imaging based on clinical suspicion of ATTR cardiac amyloidosis. Myocardial 99mTc-PYP uptake was assessed by both visual (comparison of myocardial to rib uptake) and semi-quantitative (heart-to-contralateral lung uptake ratio, H:CL) methods. Twenty scans were analyzed twice, at least 48 hours apart, by each of two independent observers. Patients with visual scores of ≥ 2 on planar imaging as well as myocardial uptake on SPECT/CT were classified as ATTR positive. Diagnosis of ATTR by visual 99mTc-PYP grade was perfectly reproducible [concordance: positive and negative scans 100% (53/53 and 47/47, respectively). Both inter- and intra-observer correlations for H:CL ratio (r2 = 0.90, 0.99 (Observer 1) and 0.98 (Observer 2), respectively) and repeatability values on Bland-Altman plots were excellent. The coefficient of variation (%) for Observers 1 and 2 was 3.21 (2.14 to 4.29) and 7.49 (4.95 to 10.09), respectively. In addition, there was 100% concordance in positive and negative scan interpretation by visual grading between novice CV imagers (< 3 years' experience) and an experienced CV imager (10 years' experience). CONCLUSIONS: This study showed excellent inter-observer reproducibility and intra-observer repeatability of 99mTc-PYP visual scan interpretation and H:CL ratio for diagnosis of cardiac ATTR amyloidosis. Cardiac ATTR amyloidosis can be diagnosed reliably using 99mTc-PYP SPECT/CT by novice and experienced CV imagers.

Coronary microvascular dysfunction in patients with psoriasis.

BACKGROUND: Psoriasis is a common chronic inflammatory skin disorder that is associated with excess cardiovascular risk. Inflammation is a key mediator in the onset and progression of these cardiometabolic abnormalities; however, the excess cardiovascular risk conferred by psoriatic disease remains understudied. We investigated the prevalence and severity of CMD in patients with psoriasis and determined whether CMD is a result of CV risk factors and atherosclerotic burden. METHODS: This was a consecutive retrospective cohort study of patients with psoriasis, normal myocardial perfusion, and LV ejection fraction (EF) > 50% (N = 62) and matched controls without psoriasis (N = 112). Myocardial perfusion and myocardial flow reserve (MFR) were quantified using PET imaging. Atherosclerotic burden was determined by semi-quantitative computed tomography (CT) coronary calcium assessment. RESULTS: The prevalence of CMD (defined as MFR < 2) was 61.3% in patients with psoriatic disease, compared to 38.4% in a matched control population (P = .004). Furthermore, patients with psoriasis had a more severe reduction in adjusted MFR (2.3 ± .81 vs 1.92 ± .65, respectively, P = .001). The degree of atherosclerotic burden, as assessed by qualitative calcium score, was similar between psoriasis and controls. CONCLUSIONS: Patients with psoriasis without overt CAD demonstrated a high prevalence of coronary vasomotor abnormalities that are not entirely accounted for by the commonly associated coronary risk factors or the burden of atherosclerosis.

Coronary vasomotor dysfunction portends worse outcomes in patients with breast cancer.

BACKGROUND: Impaired MFR in the absence of flow-limiting CAD is associated with adverse events. Cardiovascular disease is an important cause of morbidity and mortality in patients with breast cancer. We sought to test the utility of MFR to predict outcomes in a cohort of patients with breast cancer. METHODS: We retrospectively studied consecutive patients with breast cancer or breast cancer survivors who underwent cardiac stress PET imaging from 2006 to 2017 at Brigham and Women's Hospital. Patients with a history of clinically overt CAD, LVEF < 45%, or abnormal myocardial perfusion were excluded. Subjects were followed from time of PET to the occurrence of a first major adverse cardiovascular event (MACE) and all-cause death. RESULTS: The final cohort included 87 patients (median age 69.0 years, 98.9% female, mean MFR 2.05). Over a median follow-up of 7.6 years after PET, the lowest MFR tertile was associated with higher cumulative incidence of MACE (adjusted subdistribution hazard ratio 4.91; 95% CI 1.68-14.38; p = 0.004) when compared with the highest MFR tertile. CONCLUSIONS: In patients with breast cancer, coronary vasomotor dysfunction was associated with incident cardiovascular events. MFR may have potential as a risk stratification biomarker among patients with/survivors of breast cancer.

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.

Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry.

BACKGROUND: The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed. METHODS: We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia. RESULTS: In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center. CONCLUSION: The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia.

Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry.

BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.

Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT.

BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01). CONCLUSIONS: The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.

Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry.

BACKGROUND: Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) vs other clinical variables is not well delineated. METHODS AND RESULTS: From 19,658 patients who underwent SPECT-MPI, we identified 3122 patients with DM without known coronary artery disease (CAD) (DM+/CAD-) and 3564 without DM with known CAD (DM-/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD- and DM-/CAD+ groups. There was comparable MACE in the matched DM+/CAD- and DM-/CAD+ groups (HR 1.15, 95% CI 0.97-1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and DM status. The combined consideration of mode of stress, TPD, and DM provided synergistic stratification, an 8.87-fold (HR 8.87, 95% CI 7.27-10.82) increase in MACE among pharmacologically stressed patients with DM and TPD > 10% (vs non-ischemic, exercised stressed patients without DM). CONCLUSIONS: Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.