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.