Citations

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 Moderate-severe mitral annulus calcification (MAC) and left ventricular (LV) diastolic dysfunction are both linked to increased risks of heart failure and mortality. Assessing LV diastolic function (LVDF) in moderate-severe-MAC by echocardiography is frequently challenging and unreliable. We recently validated an AI-ECG model for LVDF. We aimed to determine if this tool can predict long-term mortality in patients with moderate-severe-MAC and whether it can help establish MAC-specific echocardiographic LVDF parameter cutoffs. Methods This retrospective study included all patients from the AI-ECG LVDF study test cohort who underwent comprehensive transthoracic echocardiography confirming moderate-severe-MAC and ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022. AI-ECG LVDF was determined based on the index ECG. Patients were categorized based on AI-ECG into groups representing normal/grade-1 LVDF, grade-2 LVDF, or grade-3 LVDG (DF-1, DF-2, and DF-3, respectively). Results Of 2,506 patients with moderate-severe-MAC [mean age 77, 64.1% females], 709 (28.3%), 1,510 (60.3%), and 287 (11.5%) were classified as DF-1, DF-2, and DF-3, respectively. Worse LVDF grade was associated with older age, male sex, lower LV ejection fraction, and higher rates of noncardiovascular and cardiovascular comorbidities. Over a median follow-up of 2.8 years, 1,059 (42.3%) patients died. In multivariable survival analysis, DF-2 [adjusted hazard ratio (aHR) 1.43, 95% confidence interval (95%CI) 1.21-1.68] and DF-3 (aHR 1.54, 95%CI 1.22-1.93) were independently associated with a higher risk of death (figure). Findings were also consistent after adjusting to mitral inflow gradients. When comparing LVDF echocardiographic parameters in controls without MAC matched by age, sex, left ventricular ejection fraction, and AI-ECG DF grade, significant differences were observed. Patients with moderate to severe MAC generally exhibited lower e' velocities, higher TR velocities, elevated E/e' ratios, and increased LAVi. Interestingly, the E/A ratio between MAC patients and their matched controls across AI-ECG LVDF grades remained consistent. Conclusion AI-ECG LVDF predicts mortality risk in patients with moderate-severe-MAC, regardless of mitral inflow gradients. Utilizing AI-ECG LVDF might aid in the risk stratification of patients with significant MAC.

Abstract Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for LVDD detection has opened new avenues. Yet, whether AI-ECG-detected LVDD correlates with long-term AF risk remains unexplored. Methods We performed a retrospective study among all patients with a comprehensive LVDD assessment from the test population of the AI-ECG LVDD study between September 2001 and June 2022. Patients with prior documented AF according to health records or previous ECG were excluded. Patients were classified by AI-ECG as normal diastolic function (DF), grade 1 (G1), grade 2 (G2), or grade 3 (G3) LVDD. We assessed the risk of AF across AI-ECG LVDD grade, with normal DF as reference, with competing risk models. Results Of 97,023 patients free of AF at baseline, 63,533 (65.4%), 10,776 (11.1%), 18,940 (19.5%), and 3,774 (3.9%) were classified as normal DF, G1-LVDD, G2-LVDD, and G3-LVDD, respectively. Worse AI-ECG LVDD was associated with older age, lower left ventricular ejection fraction (LVEF), and higher rates of HF, diabetes, hypertension, chronic pulmonary disease, renal failure, and cardiovascular disease (p-of-trend<0.001 for all). Over a median follow-up of 4.8 years (interquartile range 1.8-9.2 years), new-onset AF occurred in 6,795 (7.0%) patients. In multivariable survival analysis, adjusted to multiple risk factors including LVEF and clinical HF, G2 and G3 LVDD, representing patients with high filling pressures, were independently associated with a higher AF-risk [G2-LVDD: adjusted hazard ratio (aHR) 1.64 (95% confidence interval (95%CI) 1.52-1.73), G3-LVDD: aHR 2.24 (95%CI 2.02-2.48)]. Conclusion The AI-ECG LVDD algorithm independently predicts long-term AF risk, regardless of LVEF and clinical HF status, highlighting its potential for identifying high-risk populations and guiding proactive monitoring and management.

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (>moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with >moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Introduction: A 65-year-old male with type one diabetes mellitus underwent a pancreas and kidney transplant approximately seven years ago and has received chronic tacrolimus. He presented with subacute dyspnea on exertion, orthopnea, and edema. His background also includes ongoing chronic kidney disease stage 3A, orthostatic hypotension, and spinal stenosis. On exam, he had abdominal and lower extremity swelling. An echocardiogram showed preserved LVEF, concentric hypertrophy, hyperrefractile myocardium, reduced longitudinal strain with apical sparing, and a tiny pericardial effusion. Hypothesis: His echocardiogram indicated an infiltrative process such as amyloidosis or tacrolimus cardiotoxicity or possibly hypertrophic cardiomyopathy. Methods: Free light chains and protein studies with mass-fixation demonstrated no monoclonal gammopathy. PYP scan was negative for transthyretin amyloidosis. A cardiac MRI showed patchy subendocardial and midmyocardial delayed gadolinium enhancement, supporting the clinical suspicion for cardiac amyloidosis. With the need to exclude tacrolimus toxicity as well, subsequent endomyocardial biopsy revealed apolipoprotein A-IV-associated cardiac amyloidosis (Figure 1). Results: Apolipoprotein A-IV-associated cardiac amyloidosis occurs in about 0.6% of patients with cardiac amyloid and has classic manifestations of LV outflow obstruction, microvascular disease, and especially renal insufficiency. When these features occur in patients with clinical and imaging findings suggesting cardiac amyloidosis despite negative testing for more common types of amyloidosis, clinicians should proceed with endomyocardial biopsy to evaluate for less common forms. Conclusions: We report a rare case of apolipoprotein A-IV-associated cardiac amyloidosis. This case highlights the need for endomyocardial biopsy when clinical suspicion for cardiac amyloidosis persists despite negative testing.