Association between left ventricular mechanical dyssynchrony with myocardial perfusion and functional parameters in patients with left bundle branch block

Objective To identify predictors of left ventricular mechanical dyssynchrony (LVMD) in patients with known left bundle branch block (LBBB) using gated single-photon emission computed tomography (SPECT) phase analysis. Methods 81 patients (74% male, 70 ± 10 years) with LBBB and suspected or known coronary artery disease underwent ECG-gated myocardial perfusion SPECT. LV perfusion and functional parameters were measured, and phase analysis was performed to quantify LV-dyssynchrony. Results 35/81 patients (42%) had prior myocardial infarction (MI), and the mean left ventricular ejection fraction (LVEF) was 49% ± 16%. LVMD was present in 58/81 (72%) patients. The summed thickening score (STS) (P Conclusion In patients with LBBB, the occurrence of LVMD as assessed by gated SPECT phase analysis is mainly influenced by reduced myocardial contractility as expressed by the STS. Proper discrimination between LVMD arising from known electrical conduction delay as opposed to areas of MI causing reduced regional contractility seems to be mandatory for therapy planning in patients with LVMD

Left ventricular dyssynchrony assessed by gated SPECT phase analysis is an independent predictor of death in patients with advanced coronary artery disease and reduced left ventricular function not undergoing cardiac resynchronization therapy

Purpose Left ventricular (LV) mechanical dyssynchrony (LVMD) was assessed by gated single-photon emission CT myocardial perfusion imaging (MPI) as an independent predictor of death from any cause in patients with known coronary artery disease (CAD) and reduced LV function. Methods Between 2001 and 2010, 135 patients (64 ± 11 years of age, 84 % men) with known CAD, reduced LV ejection fraction (LVEF, 38 ± 15 %) and without an implanted cardiac resynchronization therapy device underwent gated MPI at rest. LV functional evaluation, which included phase analysis, was conducted to identify patients with LVMD. Kaplan-Meier survival curves were calculated for death of any cause during a mean follow-up of 2.0 ± 1.7 years. Uni- and multivariate Cox proportional hazards regression models were calculated to identify independent predictors of death from any cause. Results Of the 135 patients, 30 (22 %) died during follow-up (18 cardiac deaths and 12 deaths from other causes). Kaplan-Meier curves showed a significantly shorter survival time in the patients with severely reduced LVEF (Conclusion In patients with known CAD and reduced LV function, dyssynchrony of the LV is an independent predictor of death from any cause

The amount of dysfunctional but viable myocardium predicts long-term survival in patients with ischemic cardiomyopathy and left ventricular dysfunction

To evaluate the prognostic significance of combined myocardial perfusion SPECT and [18F]FDG PET viability scanning for the prediction of survival in patients with ischemic cardiomyopathy (iCMP) and left ventricular dysfunction. 244 patients (64.0 ± 10.6 years, 86 % men) with iCMP and LVEF ≤45 % underwent SPECT/PET. Percent scar tissue and SPECT/PET-mismatch (%-mismatch) were calculated and correlated with event-free survival according to the type of therapy (medical therapy with/out revascularization) provided after imaging. Death from any cause was defined as the primary endpoint. Early revascularization (ER) was performed in 113/244 (46 %) patients within 32 ± 52 days (26 bypass surgeries and 87 percutaneous coronary interventions). 65 patients died during follow-up for a median of 33 months. Kaplan–Meier analysis showed that those patients with ≥5 % mismatch not undergoing ER had significantly higher mortality than did the group with similar mismatch who did receive ER. Cox analysis identified both SPECT/PET-mismatch and the interaction of SPECT/PET-mismatch with ER as independent predictors for death due to all causes. A threshold of ≥5 % SPECT/PET-mismatch predicted best which patients with iCMP and LV dysfunction would benefit from ER in terms of long-term survival

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

Abstract 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

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

BackgroundMachine 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.MethodsWe 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).ResultsDuring mean follow-up of 4.7&nbsp;±&nbsp;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&nbsp;&lt;&nbsp;0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs.ConclusionMissing 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

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

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 single-photon emission computed tomography (SPECT) MPI. METHODS AND RESULTS This study included 20,414 patients from the multicenter REFINE SPECT registry and 2,984 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 0.798, p = 0.18) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.795) 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. TRANSLATIONAL PERSPECTIVE A reduced machine learning model, with 12 out of 40 manually collected variables and 11 of 58 imaging variables, achieved >99% of the prognostic accuracy of the full model. Models with fewer manually collected features require less infrastructure to implement, are easier for physicians to utilize, and are potentially critical to ensuring broader clinical implementation. Additionally, these models can integrate mechanisms to explain patient-specific risk estimates to improve physician confidence in the machine learning prediction

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

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 We 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 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