Search

Background: Little is known about changes of myocardial perfusion in patients undergoing coronary revascularization or medical therapy. The purpose of this observational study was to assess the long-term effects of revascularization or conservative therapy on serial quantitative myocardial perfusion single photon emission computed tomography (SPECT).Methods and Results: The study population consisted of 421 patients who underwent serial rest thallium-201/stress technetium-99m sestamibi dual-isotope myocardial perfusion SPECT with at least a 1-year interval between the 2 studies and who had abnormal quantitative scan results on the first stress SPECT. The mean interval between scans was 32.7 +/- 15.9 months. Patients were divided into 3 groups according to stress defect extent: group 1 had small stress defects (4%-10%, n = 145), group 2 had intermediate stress defects (>10%-20%, n = 144), and group 3 had extensive stress defects (>20%, n = 132) at baseline. Forty patients in group 1, 44 in group 2, and 54 in group 3 underwent coronary revascularization between 2 SPECT studies; the others had conservative therapy. In group 3 patients with revascularization, stress defect extent and reversible defect extent were remarkably reduced (14.5% +/- 13.6% and 13.1% +/- 12.5%, respectively; both P <.0001), with greater improvement in those patients reporting increased use of cardiac medications; resting defect extent was slightly reduced (1.9% +/- 6.4%, P <.05). In group 3 patients with conservative therapy, a small reduction in stress defect extent was noted (2.3% +/- 8.3%, P <.05). In group 2, there were modest, similar reductions in reversible defect extent in both the patients with revascularization (2.7% +/- 7.7%, P <.05) and those with conservative therapy (1.8% +/- 7.3%, P <.05), as well as a small but significant reduction in stress defect extent in those with conservative therapy (2.1% +/- 8.2%, P <.05). In group 1 patients, no significant changes in stress, rest, or reversible defect extent were found with either therapy.Conclusions: The findings of this study show that improvement in quantitative myocardial perfusion abnormalities over time occurs in some patients with either revascularization or conservative therapy and suggest that, in patients with extensive defects, greater improvement may be seen in those who undergo revascularization. [ABSTRACT FROM AUTHOR]

Background: Image artifacts caused by nonuniform photon attenuation are a source of error in interpretation of images during myocardial perfusion single photon emission computed tomography (SPECT). A newly introduced attenuation correction method was evaluated for improvement in image homogeneity during 201Tl SPECT. The method was assessed with a cardiac phantom and in examinations of 42 patients (29 men) with a low likelihood of coronary disease.Methods and Results: Simultaneous transmission-emission SPECT was performed with a moving collimated 153Gd line source synchronized with a moving electronic acquisition window for transmission imaging and a novel variable-width electronic exclusion window for emission imaging designed to avoid transmission-to-emission cross talk. The resulting uncorrected and corrected polar maps were analyzed visually and divided into 31 segments for quantitative analysis. Visual analysis of the color-coded mean polar maps showed clear improvement in homogeneity after correction among the phantom, male patients, female patients, and 42 patients combined at stress and redistribution. The male and female mean polar maps showed very little differences in regional count distribution after correction. Quantitative analysis of the mean polar maps showed the following mean segmental counts (%SD) before and after attenuation correction: phantom 88 (9) to 90 (7.5), P = .00005; men at stress 83 (10) to 88 (6), P = .0007, and at redistribution 84 (8) to 88 (6), P = .01; women at stress 86 (7) to 90 (5), P = .0002, and at redistribution 87 (5) to 88 (7), P = .3; patients combined at stress 84 (8) to 88 (6), P = .0004, and at redistribution 85 (7) to 87 (7), P = .03. Inferior/anterior count ratio for men at stress increased after correction from 0.82 to 0.99 and septal/lateral count ratio from 0.94 to 1.02. Inferior/anterior count ratio for men at redistribution increased from 0.86 to 1.06 and septal/lateral count ratio from 0.97 to 1.04. Inferior/anterior count ratio for women at stress increased from 0.95 to 1.03 and septal/lateral count ratio from 0.93 to 1.00. Inferior/anterior count ratio for women at redistribution increased from 1.04 to 1.10, and septal/lateral count ratio decreased from 1.02 to 1.00.Conclusion: Improvement in image homogeneity was demonstrated with this attenuation correction method with a cardiac phantom and for patients with low likelihood of coronary artery disease. The slight relative increase in inferior wall counts at redistribution was most likely caused by scatter from the relatively higher liver activity compared with the situation during stress and emphasizes the need for scatter correction. The close similarity in count distribution for the mean male and female polar maps supports use of a sex-independent normal database for quantitative analysis. The reduced variation in corrected images from patient to patient implies increased accuracy for detection of myocardial defects. [ABSTRACT FROM AUTHOR]

Reports on angiographic and clinical outcomes and complications of primary percutaneous coronary interventions (PCI) in 48 consecutive octogenarians. Safety in the application of primary PCI, provisional stenting and the use of IIb/IIIa inhibitors; Conclusion that in octogenarians with ST elevation AMI, primary angioplasty is safe and as effective in achieving reperfusion as in younger patients.

Postexercise wall motion abnormality (WMA) in patients with normal resting myocardial perfusion may represent prolonged postischemic stunning, and may be related to the presence of severe angiographic coronary artery disease (CAD). This study assesses the diagnostic value of postexercise WMA by technetium-99m (Tc-99m) sestamibi gated single-photon emission computed tomography (SPECT) in patients with normal resting perfusion. Ninety-nine patients underwent exercise gated Tc-99m sestamibi/resting thallium-201 SPECT and coronary angiography within 90 days of nuclear testing. All patients had normal perfusion at rest. Multivariate logistic regression analysis demonstrated an incremental value of wall motion and perfusion over perfusion data alone in identifying severe and extensive CAD. Sensitivity for identifying any severely stenosed coronary artery by WMA was significantly higher than by severe perfusion defect (78% vs 49%, p <0.0001). Overall specificities of severe perfusion defect and WMA were 91% and 85%, respectively (p = NS). Thus, postexercise WMA detected by gated Tc-99m sestamibi SPECT in patients with normal resting perfusion is a sensitive marker of severe and extensive CAD. [ABSTRACT FROM AUTHOR]

The Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) has been expanded to include more patients and CT attenuation correction imaging. We present the design and initial results from the updated registry. Methods: The updated REFINE SPECT is a multicenter, international registry with clinical data and image files. SPECT images were processed by quantitative software and CT images by deep learning software detecting coronary artery calcium (CAC). Patients were followed for major adverse cardiovascular events (MACEs) (death, myocardial infarction, unstable angina, late revascularization). Results: The registry included scans from 45,252 patients from 13 centers (55.9% male, 64.7 ± 11.8 y). Correlating invasive coronary angiography was available for 3,786 (8.4%) patients. CT attenuation correction imaging was available for 13,405 patients. MACEs occurred in 6,514 (14.4%) patients during a median follow-up of 3.6 y (interquartile range, 2.5-4.8 y). Patients with a stress total perfusion deficit of 5% to less than 10% (unadjusted hazard ratio [HR], 2.42; 95% CI, 2.23-2.62) and a stress total perfusion deficit of at least 10% (unadjusted HR, 3.85; 95% CI, 3.56-4.16) were more likely to experience MACEs. Patients with a deep learning CAC score of 101-400 (unadjusted HR, 3.09; 95% CI, 2.57-3.72) and a CAC of more than 400 (unadjusted HR, 5.17; 95% CI, 4.41-6.05) were at increased risk of MACEs. Conclusion: The REFINE SPECT registry contains a comprehensive set of imaging and clinical variables. It will aid in understanding the value of SPECT myocardial perfusion imaging, leverage hybrid imaging, and facilitate validation of new artificial intelligence tools for improving prediction of adverse outcomes incorporating multimodality imaging., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Aims: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex., Methods and Results: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex., Conclusion: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients., Competing Interests: Conflict of interest: D.S.B. and P.J.S. participated in software royalties for QPS software at Cedars-Sinai Medical Center. P.J.S. has received research grant support from Siemens Medical Systems. D.S.B., S.D., A.J.E., and E.J.M. are consultants for GE Healthcare. S.D. is a consultant for Bracco Diagnostics and has received a grant through her institution from Astellas. M.D.C. has received research grant support from Spectrum Dynamics; and he is a consultant for Sanof and GE Healthcare. D.S.B.’s institution has received grant support from HeartFlow. E.J.M. has served as a consultant for Bracco Inc, and he and his institution have received grant support from Bracco Inc. T.D.R. has received research grant support from GE Healthcare and Advanced Accelerator Applications. A.J.E. reports receiving a speaker's fee from Ionetix, consulting fees from W. L. Gore & Associates, authorship fees from Wolters Kluwer Healthcare—UpToDate, and serving on a scientific advisory board for Canon America Medical Systems USA; his institution has grants/grants pending from Attralus, BridgeBio Pharma, Canon Medical Systems USA, GE Healthcare, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction., Methods: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction., Findings: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36)., Interpretation: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results., Funding: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002]., Competing Interests: Declaration of interests Dr. Robert Miller has received consulting and research support from Pfizer. Drs Berman and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Williams serves as the President-Elect of the British Society of Cardiovascular Imaging and is on the Board of Directors for the Society of Cardiovascular Computed Tomography; she has received consulting support from FEOPS and has given lectures for Canon Medical Systems, Siemens Healthineers and Novartis. Dr. Pieszko has served as a consultant for Medicalgorithmics S.A. Dr. Slomka has received consulting fees from Synektik. Drs. Berman, Sharir, Kaufmann, and Edward Miller have served as consultants for GE Healthcare. Dr. Dorbala has received honoraria from Novo Nordisk and Pfizer; her institution has received grant support from Attralus, Pfizer, GE Healthcare, Siemans, and Phillips. Dr. DiCarli has received institutional research grant support from Gilead Sciences and Amgen and consulting honoraria from Sanofi, Valo Health and MedTrace. Dr. Ruddy has received research grant support from GE Healthcare and Pfizer. Dr. Edward Miller has served as a consultant for ROIVANT; has received grant support from Anylam, Pfizer and Siemens, and has participated on the study advisory board of BioBridge. Dr. Sinusas serves a leadership role on the Society of Nuclear Medicine and Molecular Imaging Cardiovascular Council. Dr. Einstein receives royalties from Wolters Kluwer UpToDate and the American Society of Nuclear Cardiology/Society of Nuclear Medicine and Molecular Imaging, consulting fees from W.L Gore & Associates, support through patents with Columbia Technology Ventures, and has given lectures for Ionetix. Dr. Einstein's institution has received research support from GE Healthcare, Roche Medical Systems, W. L. Gore & Associates, Eidos Therapeutics, Attralus, Pfizer, Neovasc, Intellia Therapeutics, Ionis Pharmaceuticals, Canon Medical Systems, the International Atomic Energy Agency, National Council on Radiation Protection and Measurements, and the United States Regulatory Commission. The remaining authors have nothing to disclose., (Copyright © 2023. Published by Elsevier B.V.)

Lasiodiplodia theobromae attacks over 500 plant species and is an important pathogen of tropical and subtropical fruit. Due to global warming and climate change, the incidence of disease associated with L. theobromae is rising. Virulence tests performed on avocado and mango branches and fruit showed a large diversity of virulence of different L. theobromae isolates. Genome sequencing was performed for two L. theobromae isolates, representing more virulent (Avo62) and less-virulent (Man7) strains, to determine the cause of their variation. Comparative genomics, including orthologous and single-nucleotide polymorphism (SNP) analyses, identified SNPs in the less-virulent strain in genes related to secreted cell wall-degrading enzymes, stress, transporters, sucrose, and proline metabolism, genes in secondary metabolic clusters, effectors, genes involved in the cell cycle, and genes belonging to transcription factors that may contribute to the virulence of L. theobromae . Moreover, carbohydrate-active enzyme analysis revealed a minor increase in gene counts of cutinases and pectinases and the absence of a few glycoside hydrolases in the less-virulent isolate. Changes in gene-copy numbers might explain the morphological differences found in the in-vitro experiments. The more virulent Avo62 grew faster on glucose, sucrose, or starch as a single carbon source. It also grew faster under stress conditions, such as osmotic stress, alkaline pH, and relatively high temperature. Furthermore, the more virulent isolate secreted more ammonia than the less-virulent one both in vitro and in vivo. These study results describe genome-based variability related to L. theobromae virulence, which might prove useful for the mitigation of postharvest stem-end rot. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license., Competing Interests: The author(s) declare no conflict of interest.

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., (© 2023. The Author(s).)

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making., (© 2023. The Author(s).)

Background: Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed., Objectives: The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups., Methods: Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC)., Results: During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external)., Conclusions: The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction., Competing Interests: Funding Support and Author Disclosures This research (principal investigator: Dr Slomka) was supported in part by grant R01HL089765 from the National Heart, Lung and Blood Institute of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Mr Kavanagh has participated in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Einstein has served as a consultant to GE Healthcare and W.L. Gore & Associates; and his institution has received research support from GE Healthcare, Philips Healthcare, Toshiba America Medical Systems, Roche Medical Systems, and W.L. Gore & Associates. Dr Ruddy has received research grant support from GE Healthcare and Advanced Accelerator Applications. Dr Edward Miller has served as a consultant to GE Healthcare. Dr Dorbala has served as a consultant to GE Healthcare and Bracco Diagnostics; and her institution has received grant support from Astellas. Dr Di Carli has received research grant support from Spectrum Dynamics; and has received consulting honoraria from Sanofi and GE Healthcare. Dr Berman has participated in software royalties for QPS software at Cedars-Sinai Medical Center; and has served as a consultant to GE Healthcare. Dr Slomka has participated in software royalties for QPS software at Cedars-Sinai Medical Center; and has received research grant support from Siemens Medical Systems. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

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., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

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., (© 2021. American Society of Nuclear Cardiology.)

Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC). Results: In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, P < 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results ( P < 0.001), but not compared with readers interpreting with DL results ( P = 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%, P < 0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

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 &lt; .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P &lt; .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 &lt; .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., (© 2021. American Society of Nuclear Cardiology.)

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 &lt; 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 &lt; 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., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

Aims: Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI., Methods and Results: This study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models., Conclusion: Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)

Background: Semiquantitative assessment of stress myocardial perfusion defect has been shown to have greater prognostic value for prediction of major adverse cardiac events (MACE) in women compared with men in single-center studies with conventional single-photon emission computed tomography (SPECT) cameras. We evaluated sex-specific difference in the prognostic value of automated quantification of ischemic total perfusion defect (ITPD) and the interaction between sex and ITPD using high-efficiency SPECT cameras with solid-state detectors in an international multicenter imaging registry (REFINE SPECT [Registry of Fast Myocardial Perfusion Imaging With Next-Generation SPECT])., Methods: Rest and exercise or pharmacological stress SPECT myocardial perfusion imaging were performed in 17 833 patients from 5 centers. MACE was defined as the first occurrence of death or myocardial infarction. Total perfusion defect (TPD) at rest, stress, and ejection fraction were quantified automatically by software. ITPD was given by stressTPD-restTPD. Cox proportional hazards model was used to evaluate the association between ITPD versus MACE-free survival and expressed as a hazard ratio., Results: In 10614 men and 7219 women, with a median follow-up of 4.75 years (interquartile range, 3.7-6.1), there were 1709 MACE. In a multivariable Cox model, after adjusting for revascularization and other confounding variables, ITPD was associated with MACE (hazard ratio, 1.08 [95% CI, 1.05-1.1]; P <0.001). There was an interaction between ITPD and sex ( P <0.001); predicted survival for ITPD<5% was worse among men compared to women, whereas survival among women was worse than men for ITPD≥5%, P <0.001., Conclusions: In the international, multicenter REFINE SPECT registry, moderate and severe ischemia as quantified by ITPD from high-efficiency SPECT is associated with a worse prognosis in women compared with men.

Background: Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation., Objectives: This study sought to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease-deep learning [CAD-DL]) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI)., Methods: A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6-month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL was compared to automated quantitative total perfusion deficit (TPD) and reader diagnosis., Results: In total, 2,247 patients (63%) had obstructive CAD. In 10-fold repeated testing, the area under the receiver-operating characteristic curve (AUC) (95% CI) was higher according to CAD-DL (AUC: 0.83 [95% CI: 0.82-0.85]) than stress TPD (AUC: 0.78 [95% CI: 0.77-0.80]) or reader diagnosis (AUC: 0.71 [95% CI: 0.69-0.72]; P < 0.0001 for both). In external testing, the AUC in 555 patients was higher according to CAD-DL (AUC: 0.80 [95% CI: 0.76-0.84]) than stress TPD (AUC: 0.73 [95% CI: 0.69-0.77]) or reader diagnosis (AUC: 0.65 [95% CI: 0.61-0.69]; P < 0.001 for all). The present model can be integrated within standard clinical software and generates results rapidly (<12 seconds on a standard clinical workstation) and therefore could readily be incorporated into a typical clinical workflow., Conclusions: The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI., Competing Interests: Funding Support and Author Disclosures This research was supported in part by National Heart, Lung, and Blood Institute/National Institutes of Health grant R01HL089765 (principal investigator: Piotr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Drs Berman and Slomka and Mr Kavanagh participated in software royalties for QPS software at Cedars-Sinai Medical Center. Dr Slomka has received research grant support from Siemens Medical Systems. Drs Berman, Dorbala, Einstein, and Edward Miller are consultants for GE Healthcare. Dr Einstein is a consultant for W.L. Gore & Associates. Dr Dorbala is a consultant for Bracco Diagnostics; and has received a grant through her institution from Astellas. Dr Di Carli has received research grant support from Spectrum Dynamics; and he is a consultant for Sanofi and GE Healthcare. Dr Ruddy has received research grant support from GE Healthcare and Advanced Accelerator Applications. Dr Einstein has received research support through his institution from GE Healthcare, Philips Healthcare, Toshiba America Medical Systems, Roche Medical Systems, and W.L. Gore & Associates. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: The International Study of Comparative Health Effectiveness with Medical and Invasive Approaches trial demonstrated no overall difference in the composite primary endpoint and the secondary endpoints of cardiovascular (CV) death/myocardial infarction or all-cause mortality between an initial invasive or conservative strategy among participants with chronic coronary disease and moderate or severe myocardial ischemia. Detailed cause-specific death analyses have not been reported., Methods: We compared overall and cause-specific death rates by treatment group using Cox models with adjustment for pre-specified baseline covariates. Cause of death was adjudicated by an independent Clinical Events Committee as CV, non-CV, and undetermined. We evaluated the association of risk factors and treatment strategy with cause of death., Results: Four-year cumulative incidence rates for CV death were similar between invasive and conservative strategies (2.6% vs 3.0%; hazard ratio [HR] 0.98; 95% CI [0.70-1.38]), but non-CV death rates were higher in the invasive strategy (3.3% vs 2.1%; HR 1.45 [1.00-2.09]). Overall, 13% of deaths were attributed to undetermined causes (38/289). Fewer undetermined deaths (0.6% vs 1.3%; HR 0.48 [0.24-0.95]) and more malignancy deaths (2.0% vs 0.8%; HR 2.11 [1.23-3.60]) occurred in the invasive strategy than in the conservative strategy., Conclusions: In International Study of Comparative Health Effectiveness with Medical and Invasive Approaches, all-cause and CV death rates were similar between treatment strategies. The observation of fewer undetermined deaths and more malignancy deaths in the invasive strategy remains unexplained. These findings should be interpreted with caution in the context of prior studies and the overall trial results., (Copyright © 2022. Published by Elsevier Inc.)

Background: Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk., Methods: We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD)., Results: During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs., Conclusion: Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (© 2020. American Society of Nuclear Cardiology.)

Shape index and eccentricity index are measures of left ventricular morphology. Although both measures can be quantified with any stress imaging modality, they are not routinely evaluated during clinical interpretation. We assessed their independent associations with major adverse cardiovascular events (MACE), including measures of poststress change in shape index and eccentricity index. Methods: Patients undergoing SPECT myocardial perfusion imaging between 2009 and 2014 from the Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) were studied. Shape index (ratio between the maximum left ventricular diameter in short axis and ventricular length) and eccentricity index (calculated from orthogonal diameters in short axis and length) were calculated in end-diastole at stress and rest. Multivariable analysis was performed to assess independent associations with MACE (death, nonfatal myocardial infarction, unstable angina, or late revascularization). Results: In total, 14,016 patients with a mean age of 64.3 ± 12.2 y (8,469 [60.4%] male were included. MACE occurred in 2,120 patients during a median follow-up of 4.3 y (interquartile range, 3.4-5.7). Rest, stress, and poststress change in shape and eccentricity indices were associated with MACE in unadjusted analyses (all P < 0.001). However, in multivariable models, only poststress change in shape index (adjusted hazard ratio, 1.38; P < 0.001) and eccentricity index (adjusted hazard ratio, 0.80; P = 0.033) remained associated with MACE. Conclusion: Two novel measures, poststress change in shape index and eccentricity index, were independently associated with MACE and improved risk estimation. Changes in ventricular morphology have important prognostic utility and should be included in patient risk estimation after SPECT myocardial perfusion imaging., (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Survival benefit of revascularization over medical therapy (MT) in patients with stable ischemic heart disease (SIHD) is uncertain. We evaluated the prognostic effects of revascularization in patients with SIHD undergoing single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI)., Methods: Of 47,894 patients, 7973 had ischemia ≥ 5% of the left ventricle. Of these, 1837 underwent early revascularization (≤ 60 days after SPECT-MPI). The rest were MT subgroup. Follow-up period was 4.04 ± 1.86 years. Statin therapy intensity and adherence were assessed. Outcomes were all-cause mortality, death + non-fatal myocardial infarction (MI), and MACE [major adverse cardiac event = death + MI + late revascularization (> 60 days after SPECT-MPI)]., Results: Among patients with moderate-severe ischemia (≥ 10%), death rate was lower in early revascularization compared to MT subgroup (1.42%/year vs 3.12%/year, adjusted hazard ratio (HR) 0.67 (95% CI 0.50-0.90, P = .008). Death + MI and MACE rates were also lower, adjusted HR 0.69 (0.55-0.88, P = .003) and 0.80 (0.69-0.92, P = .003). Revascularization was beneficial in optimal statin therapy subgroup (death rate 1.04%/year vs 2.36%/year, adjusted HR 0.51 (0.30-0.86, P = .012). In mild ischemia (5%-9%), revascularization did not improve survival or MI-free survival, and was associated with higher MACE rate (8.86%/year vs 7.67%/year, adjusted HR 1.30 (1.12-1.52, P < .001)., Conclusion: Compared to MT, revascularization was associated with reduced risk of death, death + MI, and MACE in patients with moderate-severe ischemia, incremental over optimal statin therapy. In mild ischemia, revascularization was associated with higher risk of MACE, driven by late revascularization, with no impact on death and death + MI., (© 2019. American Society of Nuclear Cardiology.)

Background: Phase analysis of single-photon emission computed tomography myocardial perfusion imaging provides dyssynchrony information which correlates well with assessments by echocardiography, but the independent prognostic significance is not well defined. This study assessed the independent prognostic value of single-photon emission computed tomography-myocardial perfusion imaging phase analysis in the largest multinational registry to date across all modalities., Methods: From the REFINE SPECT (Registry of Fast Myocardial Perfusion Imaging With Next Generation SPECT), a total of 19 210 patients were included (mean age 63.8±12.0 years and 56% males). Poststress total perfusion deficit, left ventricular ejection fraction, and phase variables (phase entropy, bandwidth, and SD) were obtained automatically. Cox proportional hazards analyses were performed to assess associations with major adverse cardiac events (MACE)., Results: During a follow-up of 4.5±1.7 years, 2673 (13.9%) patients experienced MACE. Annualized MACE rates increased with phase variables and were ≈4-fold higher between the second and highest decile group for entropy (1.7% versus 6.7%). Optimal phase variable cutoff values stratified MACE risk in patients with normal and abnormal total perfusion deficit and left ventricular ejection fraction. Only entropy was independently associated with MACE. The addition of phase entropy significantly improved the discriminatory power for MACE prediction when added to the model with total perfusion deficit and left ventricular ejection fraction ( P <0.0001)., Conclusions: In a largest to date imaging study, widely representative, international cohort, phase variables were independently associated with MACE and improved risk stratification for MACE beyond the prediction by perfusion and left ventricular ejection fraction assessment alone. Phase analysis can be obtained fully automatically, without additional radiation exposure or cost to improve MACE risk prediction and, therefore, should be routinely reported for single-photon emission computed tomography-myocardial perfusion imaging studies.