Slomka, Piotr J

Abstract Aims Left bundle branch block (LBBB) is associated with mechanical dyssynchrony, heterogeneous perfusion, and adverse left ventricular (LV) remodeling. However, not all patients with LBBB develop cardiomyopathy, and dyssynchrony can occur without conduction defects. The role of microvascular dysfunction remains uncertain. We aimed to assess how mechanical dyssynchrony and perfusion heterogeneity relate to LV remodeling and function in patients with and without LBBB. Methods and results We retrospectively analyzed 233 patients with isolated LBBB and 932 matched controls who underwent PET myocardial perfusion imaging, assessing mechanical dyssynchrony (phase entropy), myocardial blood flow (MBF), coronary vascular resistance (CVR), myocardial flow reserve (MFR), septal-to-lateral MBF ratio (SLR) for perfusion heterogeneity, LV volumes, and ejection fraction (EF). Compared to controls, patients with LBBB had greater dyssynchrony (56% vs. 40%), larger LV volumes, and lower EF (54% vs. 67%) (all p <0.001), and had higher stress CVR (37 vs. 34 mmHg/mL·min⁻¹·g⁻¹), lower stress MBF (2.4 vs. 2.6 mL/min/g), reduced MFR (2.4 vs. 2.6), and lower SLR (0.95 vs. 1.00) (all p <0.05). Among patients with dyssynchrony, SLR<1.0 identified those with more adverse remodeling. In multivariable regression, phase entropy and SLR independently predicted LV volumes and EF, with adverse effects of SLR reduction amplified in LBBB (interaction p <0.01). In the Cox proportional hazards analysis, phase entropy (HR:1.02, p =0.01), MFR (HR:0.62, p <0.001), and LVEF (HR:0.97, p <0.001) were independently associated with mortality and heart failure hospitalization, whereas LBBB was not. Conclusions Mechanical dyssynchrony and perfusion heterogeneity independently predict adverse LV remodeling, irrespective of LBBB. Integrated imaging enhances cardiomyopathy stratification.

ABSTRACT Introduction Positron emission tomography (PET) myocardial flow reserve (MFR) is a robust indicator of coronary vascular health and a strong predictor of cardiovascular risk. Clinical guidelines typically use fixed MFR thresholds (e.g., <2.0) to stratify risk, yet this approach overlooks individual variation, particularly by age and sex. We aimed to establish age- and sex-adjusted MFR percentiles and to evaluate their prognostic and predictive performance for cardiovascular risk assessment, in comparison with conventional fixed-threshold MFR approach. Methods Using data from the REFINE PET registry (24,820 patients from 12 sites), we measured PET MFR and derived age- and sex-adjusted MFR reference percentiles using quantile regression in patients without known coronary artery disease. All patients were categorized into percentile-based quartile groups. The primary outcome for prognostic and prediction analyses was major adverse cardiovascular events (MACE), defined as all-cause mortality, myocardial infarction, or heart-failure hospitalization. Time-to-event associations were evaluated using covariate-adjusted survival models, with cumulative incidence and hazard ratios (HR) estimated at 1 and 5 years in the derivation dataset, an independent but similar validation dataset A, and a high-risk validation dataset B. Predictive performance for MACE was assessed using discrimination, calibration, and reclassification metrics, comparing percentile-based models with models using a fixed MFR threshold (<2.0). Results Among participants (mean age 66.5 years; median follow-up 3.6 years), age- and sex-adjusted MFR quartile groups were strong independent predictors of MACE, with adjusted HR increasing stepwise across quartile groups at both early and later follow-up. At 1 year, HR (95% CI) comparing the lowest to the highest quartile group were 4.06 (3.41–4.82) in the derivation cohort, 3.31 (2.32–4.71) in validation cohort A, and 2.35 (2.05–2.70) in validation cohort B. At 5 years, the corresponding HR were 2.18 (1.86–2.56), 1.77 (1.31–2.40), and 1.59 (1.36–1.86). Percentile-based models demonstrated consistently higher discrimination, better calibration, and greater net reclassification for MACE at both time points compared with fixed-threshold MFR models. Although 67.2% of patients had preserved MFR (>2.0), cardiovascular risk increased steadily across MFR percentiles even within this range. Conclusion Age- and sex-adjusted MFR percentiles provide a reliable, clinically actionable measure of vascular health, improving cardiovascular risk stratification by better capturing age- and sex-related heterogeneity in vascular risk. Compared with traditional fixed-threshold approaches, MFR percentiles demonstrate improved predictive performance for cardiovascular risk assessment across diverse patient populations. CLINICAL PERSPECTIVE What is new? In a large, multinational real-world registry of more than 24,000 patients undergoing PET MPI, we derived age- and sex-adjusted myocardial flow reserve (MFR) percentiles, establishing a lifespan-based reference framework with age-specific reference values. Age- and sex-adjusted MFR percentiles showed a strong, graded association with 1- and 5-year cardiovascular risk and demonstrated improved prognostic and predictive performance compared with fixed MFR thresholds and models without MFR across independent derivation and validation datasets, including a higher-risk cohort. Lower adjusted MFR percentiles were consistently associated with higher risk of major adverse cardiovascular events, including all-cause mortality, myocardial infarction, and heart-failure hospitalization. What are the clinical implications? Age- and sex-adjusted MFR percentiles provide a personalized, dynamic measure of coronary vascular health that improves cardiovascular risk stratification by accounting for age- and sex-related heterogeneity. A percentile-based approach may help clinicians better identify patients at elevated cardiovascular risk who may warrant closer monitoring or intensified preventive strategies, while avoiding unnecessary intervention in lower-risk individuals. By integrating macrovascular and microvascular dysfunction, age- and sex-adjusted MFR percentiles offer a clinically interpretable marker of vascular aging that may inform future research on aging biomarkers, novel therapeutics and cardiovascular risk assessment across the spectrum of atherosclerotic and microvascular disease.

BACKGROUND: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization. METHODS: We included 18 079 patients with cardiac PET/CT from 6 sites. A deep learning model estimated cardiac chamber volumes and left ventricular mass from CT attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis. RESULTS: During a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio [HR], 1.25 [95% CI, 1.19–1.30]), right atrial volume (HR, 1.29 [95% CI, 1.23–1.35]), right ventricular volume (HR, 1.25 [95% CI, 1.20–1.31]), left ventricular volume (HR, 1.27 [95% CI, 1.23–1.35]), and left ventricular mass (HR, 1.25 [95% CI, 1.18–1.32]) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 [95% CI, 1.0–1.19]) and ventricular mass (odds ratio, 1.12 [95% CI, 1.6–1.17]) were independent predictors of reduced myocardial flow reserve. CONCLUSIONS: Deep learning–derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.

Abstract Background and Aims Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI). Methods In PET MPI from three sites, deep learning quantified SM, bone, epicardial adipose tissue (EAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). Sex-specific thresholds for abnormal values were established. Associations with death or MI were evaluated using unadjusted and multivariable models adjusted for clinical and imaging factors. Results This study included 10 085 patients, with median age 68 (interquartile range 59–76) and 5767 (57%) male. Body tissue segmentations were completed in 102 ± 4 s. Higher VAT density was associated with an increased risk of death or MI in both unadjusted [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.37–1.43] and adjusted (HR 1.24, 95% CI 1.19–1.28) analyses, with similar findings for IMAT, SAT, and EAT. Patients with elevated VAT density and reduced myocardial flow reserve had a significantly increased risk of death or MI (adjusted HR 2.49, 95% CI 2.23–2.77). Conclusions Volumetric body tissue composition can be obtained rapidly and automatically from standard cardiac PET/CT. This new information provides a detailed, quantitative assessment of sarcopenia and cardiometabolic health for physicians.

Abstract Background/Introduction Little is known about the prevalence of high-risk plaque features or cardiometabolic predictors in diverse patient populations with underrepresented minorities, in the setting of stable chest pain. Purpose The goals of our study are to 1) describe plaque characteristics in a diverse patient population with underrepresented minorities and 2) characterize cardiometabolic risk factors associated with high prevalence of high-risk quantitative low attenuation noncalcified plaque (LDNCP) burden. Methods Our study included patients with chest pain undergoing CCTA between June 2016 and October 2021 for stable chest pain, who had a complete cardiometabolic panel including lipoprotein(a) and lipid panel, and at least one blood pressure recording before CCTA. Patients with prior PCI or CABG where excluded. CACS was performed before CCTA as per Agatston method and quantified in Agatston Units (AU). Stenosis was graded as per SCCT guidelines by cardiologists and radiologists with level 3 cardiac CT expertise. Plaque measurements were performed using previously validated semiautomated software (AutoPlaque version 2.5) in all patients with CAD-RADS &gt;0 by expert readers blinded from patients' characteristics. Coronary atherosclerotic plaque volumes were measured. Independent predictors for plaque on CCTA among patients were examined using Wilcox multivariate logistic regression. Results A total of 227 consecutive patients were included in our study (see table; age 55.00 [47.50–62.00] years, 63% female, 16% diabetes, 44% hypertension, 40% hyperlipidemia and 32% with current or previous smoking history). Majority of patients were Hispanic (64%) and the rest were Black (27%), White (6%) and Asian (3%). Patients with LDNCP burden &gt;4% were older (60.00 [52.00–66.50] vs 53.00 [43.75–61.00]; p&lt;0.001), more likely to be diabetic (27.7 vs 11.5%; p=0.005), hypertensive (67.7 vs 33.8%; p&lt;0.001), hyperlipidemic (64.6 vs 29.9%; p&lt;0.001) and present smokers (31.3 vs 13.9%; p=0.003). Almost all patients (63/67) with LDNCP burden &gt;4% had non-obstructive disease (CAD-RADS&lt;4). Patient with LDNCP burden &gt;4% were more likely to be on statin therapy (46.0 vs 30.4%; p=0.041). There was no differences in ethnicity, hemoglobin A1C, TC, LDL-C, HLD-C, TGs, lipoprotein(a), SBP or DBP. By logistic regression analysis, age (OR [CI]: 1.06 [1.01–1.08]), hypertension (2.20, [1.06–4.63]) and hyperlipidemia (2.73 [1.37–5.47]) increased the likelihood of LDNCP burden &gt;4%, but not Lipoprotein (a)&gt;175 nmol/L (OR [CI]: 1.07 [0.48–2.31]. Conclusions In our cohort of patients with high number of unrepresented minorities presenting with stable chest pain, almost all patients (94%) with LDNCP burden &gt;4% had non-obstructive CAD (CAD-RADS&lt;4). There were no differences in prevalence of LDNCP or CAD-RADS among different ethnic groups. Age, hypertension and hyperlipidemia, were the cardiometabolic factors related to LDNCP burden &gt;4%. Funding Acknowledgement Type of funding sources: None.