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

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

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Causes of cardiovascular and noncardiovascular death in the ISCHEMIA trial.

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

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

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

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

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

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

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

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

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

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

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

Survival benefit of coronary revascularization after myocardial perfusion SPECT: The role of ischemia.

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

Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT).

BACKGROUND: We aim to establish a multicenter registry collecting clinical, imaging, and follow-up data for patients who undergo myocardial perfusion imaging (MPI) with the latest generation SPECT scanners. METHODS: REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) uses a collaborative design with multicenter contribution of clinical data and images into a comprehensive clinical-imaging database. All images are processed by quantitative software. Over 290 individual imaging variables are automatically extracted from each image dataset and merged with clinical variables. In the prognostic cohort, patient follow-up is performed for major adverse cardiac events. In the diagnostic cohort (patients with correlating invasive angiography), angiography and revascularization results within 6 months are obtained. RESULTS: To date, collected prognostic data include scans from 20,418 patients in 5 centers (57% male, 64.0 ± 12.1 years) who underwent exercise (48%) or pharmacologic stress (52%). Diagnostic data include 2079 patients in 9 centers (67% male, 64.7 ± 11.2 years) who underwent exercise (39%) or pharmacologic stress (61%). CONCLUSION: The REFINE SPECT registry will provide a resource for collaborative projects related to the latest generation SPECT-MPI. It will aid in the development of new artificial intelligence tools for automated diagnosis and prediction of prognostic outcomes.

Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry.

BACKGROUND: Upper reference limits for transient ischemic dilation (TID) have not been rigorously established for cadmium-zinc-telluride (CZT) camera systems. We aimed to derive TID limits for common myocardial perfusion imaging protocols utilizing a large, multicenter registry (REFINE SPECT). METHODS: One thousand six hundred and seventy-two patients with low likelihood of coronary artery disease with normal perfusion findings were identified. Images were processed with Quantitative Perfusion SPECT software (Cedars-Sinai Medical Center, Los Angeles, CA). Non-attenuation-corrected, camera-, radiotracer-, and stress protocol-specific TID limits in supine position were derived from 97.5th percentile and mean + 2 standard deviations (SD). Reference limits were compared for different solid-state cameras (D-SPECT vs. Discovery), radiotracers (technetium-99m-sestamibi vs. tetrofosmin), different types of stress (exercise vs. four different vasodilator-based protocols), and different vasodilator-based protocols. RESULTS: TID measurements did not follow Gaussian distribution in six out of eight subgroups. TID limits ranged from 1.18 to 1.52 (97.5th percentile) and 1.18 to 1.39 (mean + 2SD). No difference was noted between D-SPECT and Discovery cameras (P = 0.71) while differences between exercise and vasodilator-based protocols (adenosine, regadenoson, or regadenoson-walk) were noted (all P < 0.05). CONCLUSIONS: We used a multicenter registry to establish camera-, radiotracer-, and protocol-specific upper reference limits of TID for supine position on CZT camera systems. Reference limits did not differ between D-SPECT and Discovery camera.

Advances in imaging instrumentation for nuclear cardiology.

Advances in imaging instrumentation and technology have greatly contributed to nuclear cardiology. Dedicated cardiac SPECT cameras incorporating novel, highly efficient detector, collimator, and system designs have emerged with the expansion of nuclear cardiology. Solid-state radiation detectors incorporating cadmium zinc telluride, which directly convert radiation to electrical signals and yield improved energy resolution and spatial resolution and enhanced count sensitivity geometries, are increasingly gaining favor as the detector of choice for application in dedicated cardiac SPECT systems. Additionally, hybrid imaging systems in which SPECT and PET are combined with X-ray CT are currently widely used, with PET/MRI hybrid systems having also been recently introduced. The improved quantitative SPECT/CT has the potential to measure the absolute quantification of myocardial blood flow and flow reserve. Rapid development of silicon photomultipliers leads to enhancement in PET image quality and count rates. In addition, the reduction of emission-transmission mismatch artifacts via application of accurate time-of-flight information, and cardiac motion de-blurring aided by anatomical images, are emerging techniques for further improvement of cardiac PET. This article reviews recent advances such as these in nuclear cardiology imaging instrumentation and technology, and the corresponding diagnostic benefits.

Comparison of the diagnostic accuracies of very low stress-dose with standard-dose myocardial perfusion imaging: Automated quantification of one-day, stress-first SPECT using a CZT camera.

BACKGROUND: Previous studies have demonstrated accurate diagnosis of reduced dose myocardial perfusion imaging (MPI) using Cadmium-Zinc-Telluride (CZT) technology. We compared the diagnostic performances of very low stress-dose (<2 mSv) with standard-dose stress-first, quantitative MPI using a CZT camera. METHODS: Patients without known coronary artery- disease who underwent a stress-first Tc-99 m sestamibi CZT-MPI and invasive coronary angiography (ICA), and low-risk patients without ICA were included. A stress-rest standard-dose (10/30 mCi) MPI and a low-dose (5/15 mCi) MPI were compared. Normal limits for quantification were developed from 40 (20 males) low-risk patients, and total perfusion deficit (TPD) was derived. RESULTS: 208 patients who underwent MPI and ICA, and 76 low-risk patients were included. Of these, 128 had a standard-dose MPI and 156 had a low-dose MPI. Stress-doses in low-dose and standard-dose groups were 5.9 ± 1.2 vs 10.2 ± 0.5 mCi (1.7 ± 0.3 vs 3.0 ± 0.1 mSv), respectively, P < 0.001, and stress-rest effective radiation was 6.9 ± 1.1 vs 11.7 ± 0.4 mSv, respectively, P < 0.001. Sensitivity, specificity, and accuracy values in the low-dose and standard-dose groups were 86.1%, 76.6%, and 81.4%; and 90.6%, 78.1%, and 84.4%, respectively, P = ns. Using TPD prone, specificity values were 84.9% and 80.3%, respectively, P = ns. CONCLUSION: One-day stress-first MPI with 50% radiation reduction and a very low stress-dose (<2 mSv) using CZT technology and quantitative supine and prone analysis provided a high diagnostic value, similar to standard-dose MPI.

[Fast myocardial perfusion imaging of stress--first protocol using CZT-technology: a preliminary report of the correlation to invasive coronary angiography].

BACKGROUND: Myocardial perfusion imaging (MPI) is valuable for diagnosing coronary disease and assessing the risk of cardiac events. New technology based on semiconductors (Cadmium zinc telluride-CZT) enables reducing imaging time and improving image quality. METHODS: We retrospectively identified 57 patients who underwent MPI at Assuta using a CZT camera and invasive coronary angiography (ICA) within 60 days following nuclear testing without an intervening cardiac event, and without history of coronary disease. In addition, 25 patients with low pretest likelihood who did not undergo ICA were included in the study. All 82 patients underwent fast-acquisition, stress-first Tc-99m sestamibi MPI using very short imaging time (stress: 5:00 min., prone: 3:40 min. and rest: 2:00 min]. A low-dose stress injection (9-12 mCi, ≤ 3.5 mSv) and high rest-dose (25-32 mCi) with adjustment to patient weight were administered. Semi-quantitative visual analysis utilized a 17-segment model, 0-4 scale (0 = normal uptake, 4 = absent uptake). The summed stress score (SSS) representing stress perfusion abnormality was converted to a percentage of abnormal myocardium (SSS% = SSS/68*100). ICA served as a gold standard for the nuclear perfusion findings. RESULTS: The mean age was 60.1 ± 11 years and most subjects were men (58, 70.7%). Compared to low-risk patients, patients who underwent ICAwere older, and had a higher frequency of hypertension, diabetes mellitus and dyslipidemia. A low-dose stress-only test with low radiation exposure ≤ 3.5 mSv) was frequent among low-risk patients (18/25-72%). ROC analysis for identification of angiographic coronary disease by SSS% demonstrated area under curve of 0.923, 95% confidence interval 0.859-0.988, p < 0.001. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were: 91.4%, 81.2%, 78.3%, 97.2% and 86.6%, respectively. CONCLUSION: Fast, low-dose stress MPI using CZT technology enables semi-quantitative analysis with high diagnostic value for coronary disease.

Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography.

AIMS: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex. METHODS AND RESULTS: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. CONCLUSION: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

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].

Initial multicentre experience of high-speed myocardial perfusion imaging: comparison between high-speed and conventional single-photon emission computed tomography with angiographic validation.

PURPOSE: High-speed (HS) single-photon emission computed tomography (SPECT) with a recently developed solid-state camera shows comparable myocardial perfusion abnormalities to those seen in conventional SPECT. We aimed to compare HS and conventional SPECT images from multiple centres with coronary angiographic findings. METHODS: The study included 50 patients who had sequential conventional SPECT and HS SPECT myocardial perfusion studies and coronary angiography within 3 months. Stress and rest perfusion images were visually analysed and scored semiquantitatively using a 17-segment model by two experienced blinded readers. Global and coronary territorial summed stress scores (SSS) and summed rest scores (SRS) were calculated. Global SSS ≥3 or coronary territorial SSS ≥2 was considered abnormal. In addition the total perfusion deficit (TPD) was automatically derived. TPD >5% and coronary territorial TPD ≥3% were defined as abnormal. Coronary angiograms were analysed for site and severity of coronary stenosis; ≥50% was considered significant. RESULTS: Of the 50 patients, 13 (26%) had no stenosis, 22 (44%) had single-vessel disease, 6 (12%) had double-vessel disease and 9 (18%) had triple-vessel disease. There was a good linear correlation between the visual global SSS and SRS (Spearman's ρ 0.897 and 0.866, respectively; p < 0.001). In relation to coronary angiography, the sensitivities, specificities and accuracies of HS SPECT and conventional SPECT by visual assessment were 92% (35/38), 83% (10/12) and 90% (45/50) vs. 84% (32/38), 50% (6/12) and 76% (38/50), respectively (p < 0.001). The sensitivities, specificities and accuracies of HS SPECT and conventional SPECT in relation to automated TPD assessment were 89% (31/35), 57% (8/14) and 80% (39/49) vs. 86% (31/36), 77% (10/13) and 84% (41/49), respectively. CONCLUSION: HS SPECT allows fast acquisition of myocardial perfusion images that correlate well with angiographic findings with overall accuracy by visual assessment better than conventional SPECT. Further assessment in a larger patient population may be needed to confirm this observation.