Berman, Daniel S.

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 Aims To investigate the impact of statins on plaque progression according to high-risk coronary atherosclerotic plaque (HRP) features and to identify predictive factors for rapid plaque progression in mild coronary artery disease (CAD) using serial coronary computed tomography angiography (CCTA). Methods and results We analyzed mild stenosis (25–49%) CAD, totaling 1432 lesions from 613 patients (mean age, 62.2 years, 63.9% male) and who underwent serial CCTA at a ≥2 year inter-scan interval using the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (NCT02803411) registry. The median inter-scan period was 3.5 ± 1.4 years; plaques were quantitatively assessed for annualized percent atheroma volume (PAV) and compositional plaque volume changes according to HRP features, and the rapid plaque progression was defined by the ≥90th percentile annual PAV. In mild stenotic lesions with ≥2 HRPs, statin therapy showed a 37% reduction in annual PAV (0.97 ± 2.02 vs. 1.55 ± 2.22, P = 0.038) with decreased necrotic core volume and increased dense calcium volume compared to non-statin recipient mild lesions. The key factors for rapid plaque progression were ≥2 HRPs [hazard ratio (HR), 1.89; 95% confidence interval (CI), 1.02–3.49; P = 0.042], current smoking (HR, 1.69; 95% CI 1.09–2.57; P = 0.017), and diabetes (HR, 1.55; 95% CI, 1.07–2.22; P = 0.020). Conclusion In mild CAD, statin treatment reduced plaque progression, particularly in lesions with a higher number of HRP features, which was also a strong predictor of rapid plaque progression. Therefore, aggressive statin therapy might be needed even in mild CAD with higher HRPs. Clinical trial registration ClinicalTrials.gov NCT02803411

Background: Preclinical studies and pilot patient studies have shown that chronic infarctions can be detected and characterized from cardiac magnetic resonance without gadolinium-based contrast agents using native-T1 maps at 3T. We aimed to investigate the diagnostic capacity of this approach for characterizing chronic myocardial infarctions (MIs) in a multi-center setting. Methods: Patients with a prior MI (n=105) were recruited at 3 different medical centers and were imaged with native-T1 mapping and late gadolinium enhancement (LGE) at 3T. Infarct location, size, and transmurality were determined from native-T1 maps and LGE. Sensitivity, specificity, receiver-operating characteristic metrics, and inter- and intraobserver variabilities were assessed relative to LGE. Results: Across all subjects, T1 of MI territory was 1621±110 ms, and remote territory was 1225±75 ms. Sensitivity, specificity, and area under curve for detecting MI location based on native-T1 mapping relative to LGE were 88%, 92%, and 0.93, respectively. Native-T1 maps were not different for measuring infarct size (native-T1 maps: 12.1±7.5%; LGE: 11.8±7.2%, P =0.82) and were in agreement with LGE ( R 2 =0.92, bias, 0.09±2.6%). Corresponding inter- and intraobserver assessments were also highly correlated (interobserver: R 2 =0.90, bias, 0.18±2.4%; and intraobserver: R 2 =0.91, bias, 0.28±2.1%). Native T1 maps were not different for measuring MI transmurality (native-T1 maps: 49.1±15.8%; LGE: 47.2±19.0%, P =0.56) and showed agreement ( R 2 =0.71; bias, 1.32±10.2%). Corresponding inter- and intraobserver assessments were also in agreement (interobserver: R 2 =0.81, bias, 0.1±9.4%; and intraobserver: R 2 =0.91, bias, 0.28±2.1%, respectively). While the overall accuracy for detecting MI with native-T1 maps at 3T was high, logistic regression analysis showed that MI location was a prominent confounder. Conclusions: Native-T1 mapping can be used to image chronic MI with high degree of accuracy, and as such, it is a viable alternative for scar imaging in patients with chronic MI who are contraindicated for LGE. Technical advancements may be needed to overcome the imaging confounders that currently limit native-T1 mapping from reaching equivalent detection levels as LGE.