Myocardial flow reserve recovery in patients with Takotsubo syndrome: insights from positron emission tomography

BACKGROUND

Coronary microvascular dysfunction (CMD) has been implicated in the pathogenesis of Takotsubo syndrome (TTS). Positron emission tomography (PET) plays a key role in the assessment of CMD through myocardial flow reserve (MFR). However, there is limited information on the temporal progression of MFR and its relationship to coronary artery disease (CAD) in TTS patients.

METHODS

This study evaluated patients with TTS who underwent cardiac catheterization and PET within one year of hospitalization. Patients were categorized into acute (≤10 days), subacute (11-30 days), and chronic (≥31 days) stages based on post-onset time of PET assessment. MFR values and prevalence of abnormal MFR (<2.0) were compared between stages. Temporal MFR changes in patients with obstructive CAD (≥70% stenosis by coronary angiography), non-obstructive CAD, and normal coronaries were compared.

RESULTS

Of the 88 patients studied (mean age 70; 96% female), 52 (59%) were in the acute, 17 (19%) in the subacute, and 19 (22%) in the chronic stage. Median MFR in the acute stage was 2.0 (1.5-2.3), with 58% of patients showing abnormal MFR. A significant time-dependent improvement in MFR was observed (p=0.002), accompanied by a decreased prevalence of abnormal MFR (p=0.016). While patients with normal coronaries showed significant MFR improvement over time (p=0.045), patients with obstructive or non-obstructive CAD demonstrated no improvement across three stages (p=0.346 and 0.174, respectively).

CONCLUSIONS

PET-derived MFR was impaired in TTS patients during the acute phase, with improvement suggesting potential recovery from CMD over time. The concurrent presence of obstructive CAD might impede this recovery process.

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

Background

While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment.

Methods

Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs).

Results

Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all).

Conclusion

In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Development and evolution of nuclear cardiology and cardiac PET in Canada

Gated radionuclide angiography and myocardial perfusion imaging were developed in the United States and Europe in the 1970's and soon adopted in Canadian centers. Much of the early development of nuclear cardiology in Canada was in Toronto, Ontario and was quickly followed by new programs across the country. Clinical research in Canada contributed to the further development of nuclear cardiology and cardiac PET. The Canadian Nuclear Cardiology Society (CNCS) was formed in 1995 and became the Canadian Society of Cardiovascular Nuclear and CT Imaging (CNCT) in 2014. The CNCS had a major role in education and advocacy for cardiovascular nuclear medicine testing. The CNCS established the Dr Robert Burns Lecture and CNCT named the Canadian Society of Cardiovascular Nuclear and CT Imaging Annual Achievement Award for Dr Michael Freeman in memoriam of these two outstanding Canadian leaders in nuclear cardiology. The future of nuclear cardiology in Canada is exciting with the expanding use of SPECT imaging to include Tc-99m-pyrophosphate for diagnosis of transthyretin cardiac amyloidosis and the ongoing introduction of cardiac PET imaging.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Impact of cardiac size on SPECT myocardial perfusion imaging performance: Insights from the REFINE-SPECT registry

AIMS

Variation in diagnostic performance of 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 2,066 patients without known CAD (67% male, 64.7 ± 11.2 years) across 9 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 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 to 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 to 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.

CONCLUSIONS

Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

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].

Characterizing Hypertension Specialist Care in Canada: A National Survey

Background

The hypertension specialist often receives referrals of patients with young-onset, severe, difficult-to-control hypertension, patients with hypertensive emergencies, and patients with secondary causes of hypertension. Specialist hypertension care compliments primary care for these complex patients and contributes to an overall hypertension control strategy. The objective of this study was to characterize hypertension centres and the practice patterns of Canadian hypertension specialists.

Methods

Adult hypertension specialists across Canada were surveyed to describe hypertension centres and specialist practice in Canada, including the following: the patient population managed by hypertension specialists; details on how care is provided; practice pattern variations; and differences in access to specialized hypertension resources across the country.

Results

The survey response rate was 73.5% from 25 hypertension centres. Most respondents were nephrologists and general internal medicine specialists. Hypertension centres saw between 50 and 2500 patients yearly. A mean of 17% (± 15%) of patients were referred from the emergency department and a mean of 52% (± 24%) were referred from primary care. Most centres had access to specialized testing (adrenal vein sampling, level 1 sleep studies, autonomic testing) and advanced therapies for resistant hypertension (renal denervation). Considerable heterogeneity was present in the target blood pressure in young people with low cardiovascular risk and in the diagnostic algorithms for investigating secondary causes of hypertension.

Conclusions

These results summarize the current state of hypertension specialist care and highlight opportunities for further collaboration among hypertension specialists, including standardization of the approach to specialist care for patients with hypertension.

Multicenter Evaluation of the Feasibility of Clinical Implementation of SPECT Myocardial Blood Flow Measurement: Intersite Variability and Imaging Time

BACKGROUND

Single-center studies have shown that single photon emission computed tomography myocardial blood flow (MBF) measurement is accurate compared with MBF measured with microspheres in a porcine model, positron emission tomography, and angiography. Clinical implementation requires consistency across multiple sites. The study goal is to determine the intersite processing repeatability of single photon emission computed tomography MBF and the additional camera time required.

METHODS

Five sites (Canada, Italy, Japan, Germany, and Singapore) each acquired 25 to 35 MBF studies at rest and with pharmacological stress using technetium-99m-tetrofosmin on a pinhole-collimated cadmium-zinc-telluride-based cardiac single photon emission computed tomography camera with standardized list-mode imaging and processing protocols. Patients had intermediate to high pretest probability of coronary artery disease. MBF was measured locally and at a core laboratory using commercially available software. The time a room was occupied for an MBF study was compared with that for a standard rest/stress myocardial perfusion study.

RESULTS

With motion correction, the overall correlation in MBF between core laboratory and local site was 0.93 (range, 0.87-0.97) at rest, 0.90 (range, 0.84-0.96) at stress, and 0.84 (range, 0.70-0.92) for myocardial flow reserve. The local-to-core difference in global MBF (bias-MBF) was 5.4% (-3.8% to 14.8%; median [interquartile range]) at rest and 5.4% (-6.2% to 19.4%) at stress. Between the 5 sites, bias-MBF ranged from -1.6% to 11.0% at rest and from -1.9% to 16.3% at stress; the interquartile range in bias-MBF was between 9.3% (4.8%-14.0%) and 22.3% (-10.3% to 12.0%) at rest and between 17.0% (-11.3% to 5.6%) and 33.3% (-10.4% to 22.9%) at stress and was not significantly different between most sites. Both bias and interquartile range were like previously reported interobserver variability and less than the SD of the test-retest difference of 30%. The overall difference in myocardial flow reserve was 1.52% (-10.6% to 11.3%). There were no significant differences between with and without motion correction. The average additional acquisition time varied between sites from 44 to 79 minutes.

CONCLUSIONS

The average bias-MBF and bias-MFR values were small with standard deviations substantially less than the test-retest variability. This demonstrates that MBF can be measured consistently across multiple sites and further supports that this technique can be reliably implemented.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT03427749.

Role of nuclear cardiology in diagnosis and risk stratification of coronary microvascular disease

Coronary flow reserve (CFR) with positron emission tomography/computed tomography (PET/CT) has an important role in the diagnosis of coronary microvascular disease (CMD), aids risk stratification and may be useful in monitoring therapy. CMD contributes to symptoms and a worse prognosis in patients with coronary artery disease (CAD), nonischemic cardiomyopathies, and heart failure. CFR measurements may improve our understanding of the role of CMD in symptoms and prognosis in CAD and other cardiovascular diseases. The clinical presentation of CAD has changed. The prevalence of nonobstructive CAD has increased to about 50% of patients with angina undergoing angiography. Ischemia with nonobstructive arteries (INOCA) is recognized as an important cause of symptoms and has an adverse prognosis. Patients with INOCA may have ischemia due to CMD, epicardial vasospasm or diffuse nonobstructive CAD. Reduced CFR in patients with INOCA identifies a high-risk group that may benefit from management strategies specific for CMD. Although measurement of CFR by PET/CT has excellent accuracy and repeatability, use is limited by cost and availability. CFR measurement with single-photon emission tomography (SPECT) is feasible, validated, and would increase availability and use of CFR. Patients with CMD can be identified by reduced CFR and selected for specific therapies.

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.

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

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.

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

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.

PET for Detection and Reporting Coronary Microvascular Dysfunction: A JACC: Cardiovascular Imaging Expert Panel Statement

Angina pectoris and dyspnea in patients with normal or nonobstructive coronary vessels remains a diagnostic challenge. Invasive coronary angiography may identify up to 60% of patients with nonobstructive coronary artery disease (CAD), of whom nearly two-thirds may, in fact, have coronary microvascular dysfunction (CMD) that may account for their symptoms. Positron emission tomography (PET) determined absolute quantitative myocardial blood flow (MBF) at rest and during hyperemic vasodilation with subsequent derivation of myocardial flow reserve (MFR) affords the noninvasive detection and delineation of CMD. Individualized or intensified medical therapies with nitrates, calcium-channel blockers, statins, angiotensin-converting enzyme inhibitors, angiotensin II type 1-receptor blockers, beta-blockers, ivabradine, or ranolazine may improve symptoms, quality of life, and outcome in these patients. Standardized diagnosis and reporting criteria for ischemic symptoms caused by CMD are critical for optimized and individualized treatment decisions in such patients. In this respect, it was proposed by the cardiovascular council leadership of the Society of Nuclear Medicine and Molecular Imaging to convene thoughtful leaders from around the world to serve as an independent expert panel to develop standardized diagnosis, nomenclature and nosology, and cardiac PET reporting criteria for CMD. This consensus document aims to provide an overview of the pathophysiology and clinical evidence of CMD, its invasive and noninvasive assessment, standardization of PET-determined MBFs and MFR into "classical" (predominantly related to hyperemic MBFs) and "endogen" (predominantly related to resting MBF) normal coronary microvascular function or CMD that may be critical for diagnosis of microvascular angina, subsequent patient care, and outcome of clinical CMD trials.

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.

Improved precision of SPECT myocardial blood flow using a net tracer retention model

BACKGROUND

Noninvasive quantification of absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR) provides incremental benefit to relative myocardial perfusion imaging (MPI) to diagnose and manage heart disease. MBF can be measured with single-photon emission computed tomography (SPECT) but the uncertainty in the measured values is high. Standardization and optimization of protocols for SPECT MBF measurements will improve the consistency of this technique. One element of the processing protocol is the choice of kinetic model used to analyze the dynamic image series.

PURPOSE

This study evaluates if a net tracer retention model (RET) will provide a better fit to the acquired data and greater test-retest precision than a one-compartment model (1CM) for SPECT MBF, with (+MC) and without (-MC) manual motion correction.

METHODS

Data from previously acquired rest-stress MBF studies (31 SPECT-PET and 30 SPECT-SPECT) were reprocessed ± MC. Rate constants (K1) were extracted using 1CM and RET, +/-MC, and compared pairwise with standard PET MBF measurements using cross-validation to obtain calibration parameters for converting SPECT rate constants to MBF and to assess the goodness-of-fit of the calibration curves. Precision (coefficient of variation of test re-test relative differences, COV) of flow measurements was computed for 1CM and RET ± MC using data from the repeated SPECT MBF studies.

RESULTS

Both the RET model and MC improved the goodness-of-fit of the SPECT MBF calibration curves to PET. All models produced minimal bias compared with PET (mean bias < 0.6%). The SPECT-SPECT MBF COV significantly improved from 34% (1CM+MC) to 28% (RET+MC, P = 0.008).

CONCLUSION

The RET+MC model provides a better calibration of SPECT to PET and blood flow measurements with better precision than the 1CM, without loss of accuracy.

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.

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.

Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging

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.

Unsupervised machine learning improves risk stratification of patients with visual normal SPECT myocardial perfusion imaging assessments

Background

Unsupervised machine learning has the potential to identify new cardiovascular phenotypes and more accurately assess individual risk in an unbiased fashion.

Purpose

We aimed to use unsupervised learning to identify, analyze, and risk-stratify subgroups of patients with normal perfusion by visual interpretation on single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).

Methods

We included consecutive patients with visual normal clinical assessment (summed stress score of 0) from the multicenter (9 sites), REFINE SPECT registry. We considered 23 clinical, 17 image-acquisition, and 26 imaging variables. Optimal dimensionality reduction (Uniform Manifold Approximation and Projection), clustering (Gaussian Mixture Model), and number of clusters were selected to maximize the silhouette coefficient (how similar a patient is to those in their own cluster compared to other clusters). Risk stratification for all-cause mortality (ACM) and major adverse cardiac events (MACE) was assessed within these clusters and compared to risk stratification by quantitative ischemia (&lt;5%, 5–10%, &gt;10%) using Kaplan-Meier curves and Cox Proportional-Hazards analysis.

Results

In total, 17,527 (of 30,351) patients in the registry had visually normal perfusion, 49.7% female, median age of 64 [55, 72] years. There were 1,138 ACM events and 2,091 MACE events with a median follow-up of 4.1 [2.9, 5.7] years. Unsupervised learning provided better risk stratification for both ACM and MACE compared to quantitative ischemia (Figure). Notably, the high-risk cluster by unsupervised learning had a hazard ratio (HR) of 9.5 (95% confidence interval [CI]: 7.7–11.7) compared to 1.4 (95% CI: 1.1–1.9) for quantitative ischemia &gt;10%. The high-risk cluster had proportionally more women (45% [low-risk], 51% [medium-risk], 57% [high-risk], all p&lt;0.001), higher body mass indices (26.9, 27.4, 29.6, all p&lt;0.001), prevalence of diabetes (17%, 22%, 33%, all p&lt;0.001), and abnormal rest ECGs (30%, 43%, 64%, p&lt;0.001); with lower rates of family history of coronary artery disease (40%, 33%, 24%, p&lt;0.001). Patients in the low-risk cluster were more likely to undergo exercise stress (100%, 38%, 0%, all p&lt;0.001), had lower rest peak systolic blood pressure (130, 131, 140 mmHg, all p&lt;0.001), and higher stress peak systolic blood pressure (164, 150, 131 mmHg, all p&lt;0.001). Patients in the high-risk cluster had higher left ventricular mass (129, 135.45, 143.9 g, all p&lt;0.001) and stress volume (57, 59, 66 ml, all p&lt;0.001).

Conclusion

Unsupervised learning identified new phenotypic clusters for SPECT MPI patients with visual normal assessments which provided improved risk stratification for ACM and MACE compared to SPECT ischemia. Such individualized risk assessment may allow better targeted management of patients with visually normal perfusion.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Research reported in this publication was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number R01HL089765. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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.

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.

Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry

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.

Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study

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.

Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry

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.

Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease

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.

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.

Site qualification and clinical interpretation standards for 99mTc-SPECT perfusion imaging in a multi-center study of MITNEC (Medical Imaging Trials Network of Canada)

BACKGROUND

Qualification and interpretation standards are essential for establishing 99mTc-SPECT MPI accuracy vs. alternative modalities.

METHODS

Rest-stress 99mTc-SPECT phantom scans were acquired on 35 cameras. LV defects were quantified with summed stress (SSS) and difference scores (SDS) at 2 core labs. SDS ≥ 2 in the right coronary artery (RCA) was the qualifying standard. Twenty rest (R)-stress (S) patient images were acquired on qualified cameras and interpreted by core labs. Global scoring differences > 3 between labs or discordant clinical interpretations underwent review. Scoring, interpretation, image quality, and diagnostic parameter agreement were assessed.

RESULTS

Phantom scans: visual scoring confirmed RCA-ischemia on all cameras. Regional SSS, SDS agreement was moderate to very good: ICC-r = 0.57, 0.84. Patient scans: 90% of global SSS, 85% of SDS differences were ≤ 3. Regional SSS, SDS agreement: ICC-r = 0.87, 0.86, and global abnormal (SSS ≥ 4) and ischemic (SDS ≥ 2) interpretation: ICC-r = 0.90 were excellent. Clinical interpretation agreement was 100% following review. Image quality agreement was 70%. Automated metrics also agreed: ischemic total perfusion deficit ICC-r = 0.75, reversible perfusion defect, transient ischemic dilation, and S-R LV ejection fraction ICC-r ≥ 0.90.

CONCLUSION

Quantitative scoring and interpretation of scans were highly repeatable with site qualification and clinical interpretation standardization, indicating that dual-core lab interpretation is appropriate to determine 99mTc-SPECT MPI accuracy.

Reduced acquisition times for measurement of myocardial blood flow with 99mTc-tetrofosmin and solid-state detector SPECT

BACKGROUND

Measurement of myocardial blood flow (MBF) is feasible using SPECT imaging but the acquisition requires more time than usual. Our study assessed the impact of reducing acquisition times on the accuracy and repeatability of the uptake rate constant (K1).

METHODS

Twenty-nine patients underwent two rest/stress studies with Tc-99m-tetrofosmin 18 ± 13 days apart, using a one-day rest/stress dynamic SPECT imaging protocol with a solid-state cardiac camera. A 5-minute static image was acquired prior to tracer injection for subtraction of residual activity, followed immediately by 11-minute of list-mode data collection. Static image acquisition times of 0.5, 1, and 3 minutes and dynamic imaging times of 5, 7, and 9 minutes were simulated by truncating list-mode data. Images were reconstructed with/without attenuation correction and with/without motion correction. Kinetic parameters were calculated using a 1-tissue-compartment model.

RESULTS

K1 increased with reduced dynamic but not static imaging time (P < 0.001). The increase in K1 for a 9-minute scan was small (4.7 ± 5.3%) compared with full-length studies. The repeatability of K1 did not change significantly (13 ± 12%, P > 0.17).

CONCLUSIONS

A shortened imaging protocol of 3-minute (rest) or 30-second (stress) static image acquisition and 9 minutes of dynamic image acquisition altered K1 by less than 5% compared to a previously validated 11-minute acquisition.

Impact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry

Background

Single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) is a well-validated non-invasive method for detecting coronary artery disease (CAD). Variations in diagnostic performance due to age and sex have been thoroughly investigated in the literature yet have demonstrated conflicting results. Several studies have associated female sex with reduced accuracy, although others have discovered no significant difference (1). Similarly, while SPECT MPI in the elderly has shown prognostic utility, cardiac event rates are elevated compared to younger patients despite a normal study (2). Additional analyses have suggested that cardiac chamber size may contribute to these observed differences due to its relationship with spatial resolution; however, the interaction of age, sex, and cardiac size remains unknown.

Purpose

We aimed to leverage a large, multicenter, international registry to assess the impact of age, sex, and left ventricular size on the diagnostic accuracy of contemporary SPECT MPI.

Methods

In 9 centers, 2067 patients (67% male, 64.7±11.2 years) in the REFINE SPECT database (REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT) underwent MPI with new generation solid-state scanners followed by invasive coronary angiography within 6 months (3). Stress total perfusion deficit was quantified automatically, and obstructive CAD was defined as &gt;70% stenosis or &gt;50% for left main. Receiver-operating characteristic curves and corresponding areas under the curve (AUC) were computed to compare diagnostic performance between cohorts created based on age (&lt;75 vs. ≥75 years), sex, and end-diastolic volume (EDV; ≥20th vs. &lt;20th sex-specific percentile).

Results

Female and elderly patients had a significantly lower EDV than male and younger patients respectively (p&lt;0.001, Figure 1). Diagnostic accuracy of SPECT was similar by sex (p=0.63). Elderly patients (AUC 0.72 vs. 0.78, p=0.025) and patients with reduced volumes (AUC 0.72 vs. 0.79, p=0.009) exhibited significantly worse performance. When isolating male patients with reduced volumes, a significant difference in accuracy was observed (AUC 0.69 vs. 0.79, p=0.001; Figure 2A), while female patients trended towards significance (p=0.32). Likewise, SPECT performed poorly for elderly patients with reduced volumes (AUC 0.64 vs. 0.78, p=0.01; Figure 2B). If patients possessed any two characteristics of male sex, age ≥75, or low EDV, prediction of CAD with SPECT was significantly decreased (p=0.002; Figure 2C).

Conclusions

Our findings suggest that men with reduced cardiac volumes display worse diagnostic SPECT performance, although it is uncertain whether a pathophysiologic reason exists or further investigation is required for female patients. Patients age ≥75 tended to have lower cardiac volumes as well as lower diagnostic performance. Given these results, alternative diagnostic modalities may better diagnose CAD in patients with these characteristics.

Funding Acknowledgement

Type of funding sources: None. Figure 1Figure 2

Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT

AIMS

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) stress-only protocols reduce radiation exposure and cost but require clinicians to make immediate decisions regarding rest scan cancellation. We developed a machine learning (ML) approach for automatic rest scan cancellation and evaluated its prognostic safety.

METHODS AND RESULTS

In total, 20 414 patients from a solid-state SPECT MPI international multicentre registry with clinical data and follow-up for major adverse cardiac events (MACE) were used to train ML for MACE prediction as a continuous probability (ML score), using 10-fold repeated hold-out testing to separate test from training data. Three ML score thresholds (ML1, ML2, and ML3) were derived by matching the cancellation rates achieved by physician interpretation and two clinical selection rules. Annual MACE rates were compared in patients selected for rest scan cancellation between approaches. Patients selected for rest scan cancellation with ML had lower annualized MACE rates than those selected by physician interpretation or clinical selection rules (ML1 vs. physician interpretation: 1.4 ± 0.1% vs. 2.1 ± 0.1%; ML2 vs. clinical selection: 1.5 ± 0.1% vs. 2.0 ± 0.1%; ML3 vs. stringent clinical selection: 0.6 ± 0.1% vs. 1.7 ± 0.1%, all P < 0.0001) at matched cancellation rates (60 ± 0.7, 64 ± 0.7, and 30 ± 0.6%). Annualized all-cause mortality rates in populations recommended for rest cancellation by physician interpretation, clinical selection approaches were higher (1.3%, 1.2%, and 1.0%, respectively) compared with corresponding ML thresholds (0.6%, 0.6%, and 0.2%).

CONCLUSION

ML, using clinical and stress imaging data, can be used to automatically recommend cancellation of rest SPECT MPI scans, while ensuring higher prognostic safety than current clinical approaches.

Prognostic durability of coronary computed tomography angiography

AIMS

This large prospective cohort study sought to confirm the incremental prognostic value of coronary computed tomographic angiography (CCTA) measured over a prolonged follow-up duration. CCTA has diagnostic and prognostic value but data supporting its long-term prognostic value in a large prospectively recruited cohort with suspected coronary artery disease (CAD) has been limited.

METHODS AND RESULTS

Consecutive patients (without history of myocardial infarction, revascularization, cardiac transplantation, and congenital heart disease) were prospectively enrolled. CCTA was evaluated for CAD severity, total plaque score (TPS), and left ventricular ejection fraction. Patients were followed for major adverse events (MAE) and major adverse cardiac events (MACE).Over a total of 99 months, 8667 consecutive CCTA patients (mean age = 57.1 ± 11.1 years, 52.9% men) were prospectively enrolled and followed for a mean duration of 7.0 ± 2.6 years. At follow-up, there were a total of 723 MAE, 278 MACE, 547 all-cause deaths, 110 cardiac deaths, and 104 non-fatal myocardial infarction. Patients without coronary atherosclerosis at the time of CCTA had a very low annual event rate for both MAE and MACE (0.45%/year and 0.19%/year, respectively). Both MAE and MACE increased with increasing TPS and severity of CAD. In patients with non-obstructive CAD and who were statin-naive, TPS ≥5 had MACE rates >0.75%/year. Patients with high-risk CAD had an annual MAE and MACE rates of 3.52%/year and 2.58%/year, respectively. Adjusted hazard ratio of the severity of CAD based on multivariable analyses indicated that the prognostic values were incremental.

CONCLUSION

CCTA has independent and incremental prognostic value that is durable over time. The absence of coronary atherosclerosis portends an excellent prognosis. Patients with increasing non-obstructive plaque burden have worse prognosis and a TPS threshold ≥5 may identify a population that may benefit from statin therapy.

Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry

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.

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry

AIMS

To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting.

METHODS AND RESULTS

A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed.

CONCLUSION

In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient).