Cardiac Computed Tomography for Quantification of Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis.

BACKGROUND: Extracellular volume (ECV) is a quantitative measure of extracellular compartment expansion, and an increase in ECV is a marker of myocardial fibrosis. Although cardiac magnetic resonance (CMR) is considered the standard imaging tool for ECV quantification, cardiac computed tomography (CT) has also been used for ECV assessment. OBJECTIVES: The aim of this meta-analysis was to evaluate the correlation and agreement in the quantification of myocardial ECV by CT and CMR. METHODS: PubMed and Web of Science were searched for relevant publications reporting on the use of CT for ECV quantification compared with CMR as the reference standard. The authors employed a meta-analysis using the restricted maximum-likelihood estimator with a random-effects method to estimate summary correlation and mean difference. A subgroup analysis was performed to compare the correlation and mean differences between single-energy CT (SECT) and dual-energy CT (DECT) techniques for the ECV quantification. RESULTS: Of 435 papers, 13 studies comprising 383 patients were identified. The mean age range was 57.3 to 82 years, and 65% of patients were male. Overall, there was an excellent correlation between CT-derived ECV and CMR-derived ECV (mean: 0.90 [95% CI: 0.86-0.95]). The pooled mean difference between CT and CMR was 0.96% (95% CI: 0.14%-1.78%). Seven studies reported correlation values using SECT, and 4 studies reported those using DECT. The pooled correlation from studies utilizing DECT for ECV quantification was significantly higher compared with those with SECT (mean: 0.94 [95% CI: 0.91-0.98] vs 0.87 [95% CI: 0.80-0.94], respectively; P = 0.01). There was no significant difference in pooled mean differences between SECT vs DECT (P = 0.85). CONCLUSIONS: CT-derived ECV showed an excellent correlation and mean difference of <1% with CMR-derived ECV. However, the overall quality of the included studies was low, and larger, prospective studies are needed to examine the accuracy and diagnostic and prognostic utility of CT-derived ECV.

Soft tissue displacement for detection of left ventricle apical dyskinesis with transthoracic echocardiography.

We tested the hypothesis that the use of outward displacement of the soft tissue between the apex and the chest wall as seen in TTE, is a sign of apical displacement and would allow for more accurate diagnosis of apical dyskinesis. This is a retrospective study of 123 patients who underwent TTE and cardiac magnetic resonance imaging (MRI) within a time frame of 6 months between 2008 and 2019. 110 subjects were deemed to have good quality studies and included in the final analysis. An observer blinded to the study objectives evaluated the echocardiograms and recorded the presence or absence of apical dyskinesis. Two independent observers evaluated the echocardiograms based on the presence or absence of outward displacement of the overlying tissue at the LV apex. Cardiac MRI was used to validate the presence of apical dyskinesis. The proportion of studies which were identified as having apical dyskinesis with conventional criteria defined as outward movement of the left ventricular apex during systole were compared to those deemed to have dyskinesis based on tissue displacement. By cardiac MRI, 90 patients had apical dyskinesis. Using conventional criteria on TTE interpretation, 21 were diagnosed with apical dyskinesis (23.3%). However, when soft tissue displacement was used as the diagnostic marker of dyskinesis, 78 patients (86.7%) were diagnosed with dyskinesis, p < 0.01. Detection of displacement of soft tissue overlying the LV apex facilitates better recognition of LV apical dyskinesis.

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.

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.

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.

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0-5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm3 or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70-16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07-5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99-1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction. FUNDING: National Heart, Lung, and Blood Institute and the Miriam & Sheldon G Adelson Medical Research Foundation.

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality.

PURPOSE: Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS: Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS: The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION: In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.

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.

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.

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.

Mean Heart Dose Is an Inadequate Surrogate for Left Anterior Descending Coronary Artery Dose and the Risk of Major Adverse Cardiac Events in Lung Cancer Radiation Therapy.

PURPOSE: Mean heart dose (MHD) over 10 Gy and left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15Gy) greater than 10% can significantly increase the risk of major adverse cardiac events (MACE) in patients with non-small cell lung cancer (NSCLC). We sought to characterize the discordance between MHD and LAD dose and the association of this classification on the risk of MACE after radiation therapy. METHODS AND MATERIALS: The coefficient of determination for MHD and LAD V15Gy was calculated in this retrospective analysis of 701 patients with locally advanced NSCLC treated with radiation therapy. Four groups were defined on the basis of high or low MHD (≥10 Gy vs <10 Gy) and LAD V15Gy (≥10% vs <10%). MACE (unstable angina, heart failure, myocardial infarction, coronary revascularization, and cardiac death) cumulative incidence was estimated, and Fine and Gray regressions were performed. RESULTS: The proportion of variance in LAD V15Gy predictable from MHD was only 54.5% (R2 = 0.545). There was discordance (where MHD was high [≥10 Gy] and LAD low [V15Gy < 10%], or vice versa) in 23.1% of patients (n = 162). Two-year MACE estimates were 4.2% (MHDhigh/LADlow), 7.6% (MHDhigh/LADhigh), 1.8% (MHDlow/LADlow), and 13.0% (MHDlow/LADhigh). Adjusting for pre-existing coronary heart disease and other prognostic factors, MHDhigh/LADlow (subdistribution hazard ratio [SHR], 0.34; 95% CI, 0.13-0.93; P = .036) and MHDlow/LADlow (SHR, 0.24; 95% CI, 0.10-0.53; P < .001) were associated with a significantly reduced risk of MACE. CONCLUSIONS: MHD is insufficient to predict LAD V15Gy with confidence. When MHD and LAD V15Gy dose exposure is discordant, isolated low LAD V15Gy significantly reduces the risk of MACE in patients with locally advanced NSCLC after radiation therapy, suggesting that the validity of whole heart metrics for optimally predicting cardiac events should be reassessed.

Quantitation of Poststress Change in Ventricular Morphology Improves Risk Stratification.

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

Diastolic dysfunction in women with ischemia and no obstructive coronary artery disease: Mechanistic insight from magnetic resonance imaging.

BACKGROUND: Ischemia with no obstructive coronary artery disease (INOCA) is prevalent in women and is associated with increased risk of developing heart failure with preserved ejection fraction (HFpEF); however, the mechanism(s) contributing to this progression remains unclear. Given that diastolic dysfunction is common in women with INOCA, defining mechanisms related to diastolic dysfunction in INOCA could identify therapeutic targets to prevent HFpEF. METHODS: Cardiac MRI was performed in 65 women with INOCA and 12 reference controls. Diastolic function was defined by left ventricular early diastolic circumferential strain rate (eCSRd). Contributors to diastolic dysfunction were chosen a priori as coronary vascular dysfunction (myocardial perfusion reserve index [MPRI]), diffuse myocardial fibrosis (extracellular volume [ECV]), and aortic stiffness (aortic pulse wave velocity [aPWV]). RESULTS: Compared to controls, eCSRd was lower in INOCA (1.61 ± 0.33/s vs. 1.36 ± 0.31/s, P = 0.016); however, this difference was not exaggerated when the INOCA group was sub-divided by low and high MPRI (P > 0.05) nor was ECV elevated in INOCA (29.0 ± 1.9% vs. 28.0 ± 3.2%, control vs. INOCA; P = 0.38). However, aPWV was higher in INOCA vs. controls (8.1 ± 3.2 m/s vs. 6.1 ± 1.5 m/s; P = 0.045), and was associated with eCSRd (r = -0.50, P < 0.001). By multivariable linear regression analysis, aPWV was an independent predictor of decreased eCSRd (standardized ß = -0.39, P = 0.003), as was having an elevated left ventricular mass index (standardized ß = -0.25, P = 0.024) and lower ECV (standardized ß = 0.30, P = 0.003). CONCLUSIONS: These data provide mechanistic insight into diastolic dysfunction in women with INOCA, identifying aortic stiffness and ventricular remodeling as putative therapeutic targets.

Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.

BACKGROUND: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals. METHODS: This was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm3) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (< 30 s per case). NAFLD was defined as liver-to-spleen attenuation ratio < 1.0 and/or average liver attenuation < 40 HU. RESULTS: In the final population of 2068 participants (59% males, 56 ± 9 years), those with MetS (n = 280;13.5%) had a greater prevalence of NAFLD (26.0% vs. 9.9%), higher EAT volume (114.1 cm3 vs. 73.7 cm3), and lower EAT attenuation (-76.9 HU vs. -73.4 HU; all p < 0.001) compared to those without MetS. At 14 ± 3 years, MACE occurred in 223 (10.8%) participants. In multivariable Cox regression, MetS was associated with increased risk of MACE (HR 1.58 [95% CI 1.10-2.27], p = 0.01) independently of CAC score; however, not after adjustment for EAT measures (p = 0.27). In a separate Cox analysis, NAFLD predicted MACE (HR 1.78 [95% CI 1.21-2.61], p = 0.003) independently of MetS, CAC score, and EAT measures. Addition of EAT volume to current risk assessment tools resulted in significant net reclassification improvement for MACE (22% over ASCVD risk score; 17% over ASCVD risk score plus CAC score). CONCLUSIONS: MetS, NAFLD, and artificial intelligence-based EAT measures predict long-term MACE risk in asymptomatic individuals. Imaging biomarkers of cardiometabolic disease have the potential for integration into routine reporting of CAC scoring CT to enhance cardiovascular risk stratification. Trial registration NCT00927693.

Statin Use, Heart Radiation Dose, and Survival in Locally Advanced Lung Cancer.

PURPOSE: Patients with locally advanced non-small cell lung cancer (LA-NSCLC) have a high prevalence of pre-existing coronary heart disease and face excess cardiac risk after thoracic radiation therapy. We sought to assess whether statin therapy is a predictor of overall survival (OS) after thoracic radiation therapy. METHODS AND MATERIALS: We performed a retrospective analysis of 748 patients with LA-NSCLC treated with thoracic radiation therapy, using Kaplan-Meier OS estimates and Cox regression. RESULTS: Statin use among high cardiac risk patients (Framingham risk ≥20% or pre-existing coronary heart disease; n = 496) was 51.2%. After adjustment for baseline cardiac risk and other prognostic factors, statin therapy was associated with a significantly increased risk of all-cause mortality (adjusted hazard ratio, 1.39; 95% confidence interval [CI], 1.00-1.91; P = .048) but not major adverse cardiac events (adjusted hazard ratio, 1.18; 95% CI, 0.52-2.68; P = .69). Among statin-naïve patients, mean heart dose ≥10 Gy versus <10 Gy was associated with a significantly increased risk of all-cause mortality (hazard ratio, 1.32; 95% CI, 1.04-1.68; P = .022), with 2-year OS estimates of 46.9% versus 60.0%, respectively. However, OS did not differ by heart dose among patients on statin therapy (hazard ratio, 1.00; 95% CI, 0.76-1.32; P = 1.00; P-interaction = .031), with 2-year OS estimates of 46.9% versus 50.3%, respectively. CONCLUSIONS: Among patients with LA-NSCLC, only half of statin-eligible high cardiac risk patients were on statin therapy, reflecting the highest cardiac risk level of our cohort. Statin use was an independent predictor of all-cause mortality but not major adverse cardiac events. Elevated mean heart dose (≥10 Gy) was associated with increased risk of all-cause mortality in statin-naïve patients but not among those on statin therapy, identifying a group of patients in which early intervention with statins may mitigate the deleterious effects of high heart radiation therapy dose. This warrants evaluation in prospective trials.

Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia.

OBJECTIVES: Using a contemporary, multicenter international single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) registry, this study characterized the potential major adverse cardiovascular event(s) (MACE) benefit of early revascularization based on automatic quantification of ischemia. BACKGROUND: Prior single-center data reported an association between moderate to severe ischemia SPECT-MPI and reduced cardiac death with early revascularization. METHODS: Consecutive patients from a multicenter, international registry who underwent 99mTc SPECT-MPI between 2009 and 2014 with solid-state scanners were included. Ischemia was quantified automatically as ischemic total perfusion deficit (TPD). Early revascularization was defined as within 90 days. The primary outcome was MACE (death, myocardial infarction, and unstable angina). A propensity score was developed to adjust for nonrandomization of revascularization; then, multivariable Cox modeling adjusted for propensity score and demographics was used to predict MACE. RESULTS: In total, 19,088 patients were included, with a mean follow-up of 4.7 ± 1.6 years, during which MACE occurred in 1,836 (9.6%) patients. There was a significant interaction between ischemic TPD modeled as a continuous variable and early revascularization (interaction p value: 0.012). In this model, there was a trend toward reduced MACE in patients with >5.4% ischemic TPD and a significant association with reduced MACE in patients with >10.2% ischemic TPD. CONCLUSIONS: In this large, international, multicenter study reflecting contemporary cardiology practice, early revascularization of patients with >10.2% ischemia on SPECT-MPI, quantified automatically, was associated with reduced MACE.

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.

OBJECTIVES: The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA. METHODS: This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined. RESULTS: The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001). CONCLUSIONS: ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization. KEY POINTS: • Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.

Non-calcific aortic tissue quantified from computed tomography angiography improves diagnosis and prognostication of patients referred for transcatheter aortic valve implantation.

AIMS: We aimed to investigate the role of aortic valve tissue composition from quantitative cardiac computed tomography angiography (CTA) in patients with severe aortic stenosis (AS) for the differentiation of disease subtypes and prognostication after transcatheter aortic valve implantation (TAVI). METHODS AND RESULTS: Our study included 447 consecutive AS patients from six high-volume centres reporting to a prospective nationwide registry of TAVI procedures (POL-TAVI), who underwent cardiac CTA before TAVI, and 224 matched controls with normal aortic valves. Components of aortic valve tissue were identified using semi-automated software as calcific and non-calcific. Volumes of each tissue component and composition [(tissue component volume/total tissue volume) × 100%] were quantified. Relationship of aortic valve composition with clinical outcomes post-TAVI was evaluated using Valve Academic Research Consortium (VARC)-2 definitions.High-gradient (HG) AS patients had significantly higher aortic tissue volume compared to low-flow low-gradient (LFLG)-AS (1672.7 vs. 1395.3 mm3, P < 0.001) as well as controls (509.9 mm3, P < 0.001), but increased non-calcific tissue was observed in LFLG compared to HG patients (1063.6 vs. 860.2 mm3, P < 0.001). Predictive value of aortic valve calcium score [area under the curve (AUC) 0.989, 95% confidence interval (CI): 0.981-0.996] for severe AS was improved after addition of non-calcific tissue volume (AUC 0.995, 95% CI: 0.991-0.999, P = 0.011). In the multivariable analysis of clinical and quantitative computed tomography parameters of aortic valve tissue, non-calcific tissue volume [odds ratio (OR) 5.2, 95% CI 1.8-15.4, P = 0.003] and history of stroke (OR 2.6, 95% CI 1.1-6.5, P = 0.037) were independent predictors of 30-day major adverse cardiovascular event (MACE). CONCLUSION: Quantitative CTA assessment of aortic valve tissue volume and composition can improve detection of severe AS, differentiation between HG and LFLG-AS in patients referred for TAVI as well as prediction of 30-day MACEs post-TAVI, over the current clinical standard.

Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study.

BACKGROUND AND AIMS: We sought to assess the performance of a comprehensive machine learning (ML) risk score integrating circulating biomarkers and computed tomography (CT) measures for the long-term prediction of hard cardiac events in asymptomatic subjects. METHODS: We studied 1069 subjects (age 58.2 ± 8.2 years, 54.0% males) from the prospective EISNER trial who underwent coronary artery calcium (CAC) scoring CT, serum biomarker assessment, and long-term follow-up. Epicardial adipose tissue (EAT) was quantified from CT using fully automated deep learning software. Forty-eight serum biomarkers, both established and novel, were assayed. An ML algorithm (XGBoost) was trained using clinical risk factors, CT measures (CAC score, number of coronary lesions, aortic valve calcium score, EAT volume and attenuation), and circulating biomarkers, and validated using repeated 10-fold cross validation. RESULTS: At 14.5 ± 2.0 years, there were 50 hard cardiac events (myocardial infarction or cardiac death). The ML risk score (area under the receiver operator characteristic curve [AUC] 0.81) outperformed the CAC score (0.75) and ASCVD risk score (0.74; both p = 0.02) for the prediction of hard cardiac events. Serum biomarkers provided incremental prognostic value beyond clinical data and CT measures in the ML model (net reclassification index 0.53 [95% CI: 0.23-0.81], p < 0.0001). Among novel biomarkers, MMP-9, pentraxin 3, PIGR, and GDF-15 had highest variable importance for ML and reflect the pathways of inflammation, extracellular matrix remodeling, and fibrosis. CONCLUSIONS: In this prospective study, ML integration of novel circulating biomarkers and noninvasive imaging measures provided superior long-term risk prediction for cardiac events compared to current risk assessment tools.

The Impact of Valvuloarterial Impedance on Left Ventricular Geometrical Change after Transcatheter Aortic Valve Replacement: A Comparison between Valvuloarterial Impedance and Mean Pressure Gradient.

Increase in left ventricular (LV) mass develops as a compensatory mechanism against pressure overload in aortic valve stenosis. However, long-standing LV geometrical changes are related to poor prognosis. The LV geometrical change occurs after transcatheter aortic valve replacement (TAVR). The present study aimed to investigate the relationship between improvement in valvuloarterial impedance (Zva) and change in LV mass index (LVMI) and the ratio of LVMI to LV end-diastolic volume index (LVMI/LVEDVI). We compared these relationships to that between Zva and mean pressure gradient (MPG). Baseline and follow-up transthoracic echocardiograms of 301 patients who underwent TAVR from November 2011 to December 2015 were reviewed. Spearman correlation coefficient (ρ) was used to compare ΔLVMI and ΔLVMI/LVEDVI with Zva or MPG. The correlation between ΔZva and ΔLVMI (ρ = 0.47, p < 0.001) was superior to that between ΔMPG and ΔLVMI (ρ = 0.15, p = 0.009) (p for comparison < 0.001). The correlation between ΔZva and ΔLVMI/LVEDVI was statistically significant (ρ = 0.54, p < 0.001); in contrast, that of ΔMPG and ΔLVMI/LVEDVI was not. The improvement in Zva after TAVR was more closely related to LVMI and LVMI/LVEDVI reduction than MPG reduction.