Di Carli, Marcelo F

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: CT attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% men; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.

BACKGROUND: Although the prognostic utility of positron emission tomography (PET) myocardial flow reserve (MFR) is well established, emerging data suggest that reduced subendocardial flows also predict adverse outcomes. However, the incremental value of subendocardial MFR (MFR SE ) beyond transmural MFR (MFR TM ) remains unclear. METHODS: We studied patients in a multicenter PET registry with normal perfusion on stress/rest Rb-82 PET, excluding those with a previous history of coronary artery bypass surgery, heart transplantation, or left ventricular ejection fraction <40%. The optimal MFR SE cutoff for predicting major adverse cardiovascular events (MACEs; death, myocardial infarction [MI], revascularization, or heart failure [HF] hospitalization) was determined using Youden’s index. Patients were stratified into 3 groups: concordant-normal (MFR TM ≥2.0; MFR SE ≥2.1), discordant (low-MFR SE , normal MFR TM ), and abnormal MFR TM . Clinical outcomes were compared by MFR groups. RESULTS: Among 6603 patients (normal N=4103; discordant N=885; abnormal N=1615) the mean age was 66.3±12.4 years, and 54% were women. Compared with the concordant-normal group, patients with discordant low-MFR SE were older and more likely to have hypertension, diabetes, peripheral artery disease, and previous percutaneous coronary intervention. The median MFR TM for normal, discordant, and abnormal groups were 2.86, 2.15, and 1.72, respectively. Over a median follow-up of 4.9 years, 1661 MACE events occurred. Discordant low-MFR SE patients had a higher risk of MACE (hazard ratio [HR], 1.41; 95% CI, 1.22–1.64) and all-cause mortality (HR, 1.36; 95% CI, 1.14–1.61) compared with concordant-normal patients. The discordant group had an intermediate absolute risk of MACE, with an adjusted annualized event rate of 5.79% (95% CI, 5.10–6.49) compared with 3.99% (95% CI, 3.67–4.30; P <0.001) in the concordant-normal group and 8.35% (95% CI, 7.71–9.00; P <0.001) in the abnormal MFR TM group. CONCLUSIONS: Subendocardial MFR reveals clinically meaningful risk heterogeneity among patients with preserved transmural flow reserve, helping refine risk stratification beyond traditional PET metrics.

ABSTRACT Background Anemia is an established marker of cardiovascular disease severity and risk which leads to elevations in resting myocardial blood flow (MBF) and impaired myocardial flow reserve (MFR) in patients without obstructive coronary artery disease (CAD). Anemia can potentially be detected opportunistically from blood pool density changes on computed tomography (CT) imaging. Objectives We evaluated relationships between chamber density measurements with hemoglobin, positron emission tomography (PET) findings, and cardiovascular events. Methods We included 33460 patients from 13 sites in the REFINE-PET who underwent PET and 24368 patients undergoing lung cancer screening chest CT. A deep learning model segmented cardiac chambers from CT images, then quantified chamber density. We evaluated the relationship between chamber density measures with resting MBF and MFR, as well as associations with death or myocardial infarction (MI). Results We included a total of 57,828 patients. A higher density in myocardium compared to left ventricle blood pool was associated with reduced MFR (adjusted odds ratio 3.02 per SD increase, 95% confidence interval[CI] 2.72 – 3.38) and an increased risk of death or MI in (adjusted hazard ratio[HR] 1.38 per SD increase, 95% CI 1.26-1.51). Having myocardial density higher than blood pool density was also associated with cardiovascular death in patients undergoing low-dose chest CT (adjusted HR 1.73, 95% CI 1.20-2.52). Conclusions In a large multimodality dataset, lower cardiac chamber density is associated with impaired MFR and independently associated with cardiovascular events. These biomarkers can be automatically extracted from CT to provide physiologic insights and potentially guide patient care. CONDENSED ABSTRACT Anemia is an important marker of cardiovascular disease severity and predictor of cardiovascular events. We utilized deep learning to measure cardiac chamber density on computed tomography imaging as a surrogate for anemia in 57,828 patients from two large multicenter studies. A higher density in myocardium compared to left ventricle blood pool was associated with reduced MFR and an increased risk of death or MI in patients undergoing positron emission tomography, and an increased risk of cardiovascular death in patients undergoing low-dose computed tomography. These biomarkers can provide additionally physiologic insights which could be used to guide patient care.

Abstract Aims Left bundle branch block (LBBB) is associated with mechanical dyssynchrony, heterogeneous perfusion, and adverse left ventricular (LV) remodelling. 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 remodelling and function in patients with and without LBBB. Methods and results We analysed 233 patients with LBBB and 932 matched controls who underwent PET myocardial perfusion imaging, assessing mechanical dyssynchrony (phase entropy), LV volumes, and ejection fraction (EF); coronary vascular resistance (CVR), myocardial blood flow (MBF), myocardial flow reserve (MFR) as markers of microvascular function; and septal-to-lateral MBF ratio (SLR) as a measure of regional perfusion heterogeneity. Compared with controls, LBBB patients had greater dyssynchrony (56% vs. 40%), larger LV volumes, and lower EF (54% vs. 67%) (all P &lt; 0.001), as well as higher stress CVR (37 vs. 34 mmHg/mL·min−1·g−1), and lower MBF (2.4 vs. 2.6 mL/min/g), MFR (2.4 vs. 2.6), and SLR (0.95 vs. 1.00) (all P &lt; 0.05). Among patients with dyssynchrony, SLR &lt;1.0 identified greater remodelling. In multivariable regression, phase entropy and SLR independently predicted LV volumes and EF, with adverse effects of SLR reduction amplified in LBBB (interaction P &lt; 0.01). In Cox analysis, phase entropy (HR:1.02, P = 0.01), MFR (HR:0.62, P &lt; 0.001), and LVEF (HR:0.97, P &lt; 0.001) were independently associated with death or heart failure hospitalization, whereas LBBB was not. Conclusion Mechanical dyssynchrony and perfusion heterogeneity independently predict adverse LV remodelling, irrespective of LBBB. Integrated imaging improves cardiomyopathy stratification.

BACKGROUND: Myocardial flow reserve (MFR), measured by positron emission tomography (PET) myocardial perfusion imaging, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of 13 N-ammonia PET MFR remains unconfirmed in larger multicenter cohorts of patients with suspected or known coronary artery disease. METHODS: We considered patients from 5 sites in the REFINE PET (Registry of Flow and Perfusion Imaging for Artificial Intelligence with PET) registry who underwent 13 N-ammonia PET myocardial perfusion imaging for coronary artery disease. Clinical and imaging data were collected at the time of myocardial perfusion imaging. MFR was quantified as the ratio of stress to rest myocardial blood flow, using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA). The primary outcome was all-cause mortality. Survival analyses were performed using Kaplan-Meier and Cox regression models adjusted for clinical and imaging covariates. RESULTS: In total, 6277 patients were included (median age of 65 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] versus n=537 [12.3%], respectively; P <0.001). Annualized all-cause mortality rates by MFR and summed stress score ranged from 1.7 to 15.8. In multivariable analysis, MFR ≤2 was independently associated with increased all-cause mortality in the overall population (hazard ratio, 2.70 [95% CI, 2.41–3.03]; P <0.001), even among patients with no perfusion defects (hazard ratio, 2.36 [95% CI, 1.93–2.89]; P <0.001). Mortality risk decreased across increasing MFR deciles, ranging from hazard ratio, 2.73 (95% CI, 2.39–3.11) to hazard ratio, 0.35 (95% CI, 0.25–0.49). CONCLUSIONS: In this large multicenter cohort, MFR derived from 13 N-ammonia PET myocardial perfusion imaging is a strong, independent predictor of all-cause mortality, even in patients with normal perfusion. An MFR of ≤2 identifies elevated risk, while higher values are associated with improved survival. These findings support the routine integration of MFR to enhance risk stratification in patients with suspected or known coronary artery 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.