Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content. This study proposes the design of a Cardiology Interface Terminology (CIT) to accurately highlight all details in EHR notes of cardiology patients. We introduce an innovative Machine Learning (ML) technique for the design of CIT. The ML technique requires training data. Manual preparation of such training data is time-consuming and expensive. The process of the CIT design includes three phases. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique. We start by designing an initial CIT, composed of several components: the cardiology-related sub-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e.g., medical abbreviations and medications. Utilizing an iterative process, fine-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates. The candidate concepts are semi-automatically reviewed before being added to CIT, yielding the training data CIT, TCIT. In the third phase, a ML model is trained with TCIT to identify candidates fitting to be concepts in the CIT. This model is used to extract further concepts from build set, yielding the final CIT. The final CIT is then used to highlight the test set and evaluate the extent to which it captures details in an unseen EHR dataset. For this purpose, four evaluation metrics, coverage, breadth, completeness, and conciseness are used. The highlighted test set has a coverage of 74.21

BACKGROUND:Cardiac balanced steady-state free precession (bSSFP) MRI suffers from dark band artifacts due to severe B0 inhomogeneity induced by air-tissue interfaces in the human body. Those artifacts remain uncontrollable despite scanner-provided B0 shimming based on low-order spherical harmonic shapes. To mitigate those artifacts for higher image fidelity, the development of advanced B0 shimming techniques is demanding and typically requires knowledge of cardiac B0 conditions across patient populations. We recently proposed a new simulation approach to derive cardiac B0 distributions from readily available chest-abdomen-pelvis CT structural images based on first principles of classical electrodynamics. This approach is able to calculate the air-tissue interface-induced magnetic field variation across the body from diverse subjects using regular CT images. While the computational method has been step-by-step validated in previous work, a comprehensive demonstration in patients has been missing. PURPOSE:The aim of this study is to validate our proposed structural CT-derived cardiac B0 magnetic field computation approach using a side-by-side comparison between simulation and in vivo measurement obtained from the same cohort of clinical subjects. METHODS:Six subjects (female: 3, male: 3, age: 54.5 ± 17.2 years) with aortic stenosis or cancer who underwent clinically-indicated CT scans of the chest, abdomen, and pelvis were consented to undergo additional in vivo cardiac B0 measurement on a 3T MRI scanner. B0 distributions in the heart were computed based on CT images using our recently proposed approach and in vivo B0 maps acquired experimentally. CT images and MRI-based anatomical images were co-registered before field calculation, followed by the side-by-side field comparison after computation using correlation analysis of inhomogeneous field values, as well as the comparison of first-order spherical harmonic coefficients after decomposing field maps and the residual B0 inhomogeneities after shim from first to fifth order. RESULTS:Simulated B0 maps show excellent agreement with in vivo measured B0 maps and strong average correlation (r = 0.92). After first and second-order shim analyses, both field maps show highly similar residual local B0 field patterns, especially near cardiophrenic angles and in the heart's inferior region, demonstrating the authenticity of localized features beyond overall field congruency. Both groups also demonstrate the trend of decreasing residual B0 inhomogeneity after shimming from first to fifth order, and their absolute differences in the mean of all subjects range from 0.25 to 2.3 Hz across different SH orders. CONCLUSIONS:The consistency between simulation and in vivo measurements of B0 field conditions in the human heart from patients exhibiting various cardiac conditions fully validates the established B0 simulation approach. This approach can accurately compute the air-tissue interface-induced inhomogeneous magnetic field shapes and amplitudes from readily available structural CT images, setting the stage for the development of tailored and advanced cardiac B0 magnetic field correction methods and more robust cardiac MRI.

INTRODUCTION:Cardiac allograft vasculopathy limits longevity following heart transplantation (HT). Non-invasive physiologic assessment with cardiac PET provides prognostic significance. We sought to assess the relative prognostic relevance of stress myocardial blood flow (sMBF) and myocardial blood flow reserve (MBFR) on post-transplant outcomes. METHODS:All adult single-organ HT recipients who underwent 13N-ammonia PET myocardial perfusion imaging between 2016 and 2019 were included with a median follow-up of 7.2 years. Patients were categorized by MBFR (normal MBFR >2.0, low MBFR ≤2.0) and sMBF (normal sMBF >1.7 mL/min/g, low sMBF ≤1.7 mL/min/g) values into three groups: concordant normal, discordant, or concordant low. RESULTS:Among 454 patients (25.8% female, median age 59 years, median time since HT 7.2 years), 287 (63.2%) had concordant normal MBFR and sMBF, 72 (15.9%) had discordant values (37 MBFR low, 35 sMBF low), and 95 (20.9%) were concordant low. Reductions in both MBFR and sMBF had an over 3-fold increased risk of death or retransplantation compared to those with both normal values (HR 3.3, 95% CI 2.2-4.8, p<0.0001). The risk remained elevated for those with discordant values, though magnitude of risk was lower (HR 2.5, 95% CI 1.6-3.9, p=0.0001). Only an isolated reduction in MBFR (HR 3.3, 95% CI 1.9-5.6, p<0.0001), not in sMBF (HR 1.7, 95% CI 0.8-3.3, p=0.14), conferred significant risk. Rate pressure product correction did not impact results. CONCLUSION:In this cohort, reduction in both sMBF and MBFR was associated with the highest risk of death or retransplant during a median follow-up of 7 years.

Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries. From hybrid MPI/CT imaging (N = 43,099) across 15 sites, we included 4,552 most relevant patients with (1) no prior coronary artery disease; (2) AI-derived mild CAC scores (1–99); and (3) normal perfusion (stress total perfusion deficit < 5

Increased right ventricular (RV) radiotracer uptake on perfusion imaging has been recognized as a marker of increased cardiovascular risk. However, this uptake is challenging to quantify because of the variable intensity of uptake in a thin structure. We used a validated artificial intelligence-enhanced method for segmenting the right ventricle from CT attenuation correction (CTAC) imaging to automatically quantify RV activity and then evaluated its prognostic significance. Methods: We evaluated consecutive patients from 11 sites who underwent PET myocardial perfusion imaging with available CTAC. We segmented the RV and left ventricular myocardium from CTAC images using deep learning and then quantified RV activity measures on coregistered PET images. We evaluated associations between RV activity measures and the incidence of death or myocardial infarction (MI). Results: In total, 25,444 patients were included in our analysis (median age, 67 y). During a median follow-up of 4.1 y, 6009 patients (23.6%) experienced death or MI. Most RV activity measures were associated with the risk of death or MI. Higher maximum RV rest activity was associated with an increased risk of death or MI (unadjusted hazard ratio, 1.17 per SD for 13N-ammonia and 1.19 per SD for 82Rb). These associations persisted after adjusting for age, sex, medical history, perfusion, function, and myocardial flow reserve. Conclusion: Deep learning can extract RV activity from hybrid PET/CT myocardial perfusion imaging. These measures are associated with myocardial flow reserve and provide complementary information regarding cardiovascular risk.

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.

BACKGROUND:Cardiac PET is an established tool for detecting cardiac allograft vasculopathy (CAV), however, transplant-patient-specific normal values for myocardial blood flow (MBF), myocardial blood flow reserve (MBFR), and left ventricular parameters are limited. We aimed to define normal ranges in heart transplant (HT) recipients using two software programs and to assess agreement between software-derived metrics. METHODS:We retrospectively evaluated 266 HT recipients undergoing 13N-ammonia PET between 2021-2023. Studies without ischemia or scar were included and compared with invasive coronary angiography and intravascular ultrasound. Normal values for MBF, MBFR, left ventricular volumes, and left ventricular ejection fraction were derived in patients without angiographic CAV and in a subset without micro-intimal disease, using two different software programs, Corridor4DM and Cedars-Sinai. Agreement between software-derived metrics was assessed using correlation and Bland-Altman analyses. RESULTS:Ninety-three patients without angiographic CAV (56 without micro-intimal disease) met criteria for normal reference value derivation. Rate-pressure-product-correction had a significant influence on the MBFR, with lower limits of normal for uncorrected MBFR and rate-pressure-product-corrected-MBFR ranging between 1.80-2.04 and 2.10-2.37, respectively, depending on the software program and use of residual subtraction. Values of software-derived metrics demonstrated strong monotonic relationship and moderate-to-good absolute agreement but the intraclass correlation coefficient was <0.90 for most metrics and there were wide limits of agreement for MBF metrics. CONCLUSIONS:This study establishes software-specific normal reference values for 13N-ammonia PET metrics in HT recipients without CAV. These findings support the use of software-specific thresholds and consistent post-processing for longitudinal assessment in transplant populations.

BACKGROUND:Radionuclide SPECT/computed tomography (CT) is standard for diagnosing transthyretin cardiac amyloidosis (ATTR-CM), but the prognostic value of quantitative metrics remains incompletely defined. We investigated their prognostic utility and additive value beyond existing staging systems. METHODS:Retrospective study of patients undergoing technetium-99m-pyrophosphate (PYP) SPECT/CT, including consecutive clinically referred patients (October 2023 to 2024) and trial participants with positive scans from January 2022. Myocardial PYP was quantified using standardized uptake values (SUVmean and SUVmax) and volumetric measures [percent injected dose (%ID), cardiac amyloid activity (CAA)]. Major adverse cardiovascular events (MACEs) comprised heart failure hospitalization or death. Cox and Kaplan-Meier analyses assessed associations; likelihood ratio testing and change in area under the curve (AUC) evaluated the additive prognostic value to existing staging systems. RESULTS:Among 43 patients with ATTR-CM, 16 experienced MACE over a median 518 days. AUCs ranged from 0.74 to 0.88 at 3 months and 0.56-0.62 at 12 months. All variables trended toward association with MACE: SUVmean (P = 0.02), SUVmax (P = 0.03), %ID (P = 0.06), and CAA (P = 0.06). After age adjustment, cut-points for SUVmax (5.8) and SUVmean (3.5) remained associated with MACE. Adding imaging variables to the National Amyloidosis Centre and Columbia staging systems did not significantly improve prognostic accuracy (Δχ2: 0.40-2.48, P = 0.11-0.53). Interobserver reproducibility was high (intraclass correlation coefficients = 0.89-0.99). CONCLUSION:Quantitative Tc-99m-PYP SPECT/CT metrics were associated with short-term outcomes in ATTR-CM, but did not improve established staging systems. Findings are hypothesis-generating and require validation in larger cohorts.

Background: Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods: We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings: During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation: An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies.

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 (MFRSE) beyond transmural MFR (MFRTM) 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 MFRSE 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 (MFRTM ≥2.0; MFRSE ≥2.1), discordant (low-MFRSE, normal MFRTM), and abnormal MFRTM. 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-MFRSE were older and more likely to have hypertension, diabetes, peripheral artery disease, and previous percutaneous coronary intervention. The median MFRTM 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-MFRSE 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 MFRTM group. CONCLUSIONS:Subendocardial MFR reveals clinically meaningful risk heterogeneity among patients with preserved transmural flow reserve, helping refine risk stratification beyond traditional PET metrics.

Stress testing is among the most frequently performed cardiac diagnostic procedures and is widely utilized in both outpatient and inpatient settings performed with and without myocardial perfusion imaging (MPI). Since the 2016 American Society of Nuclear Cardiology (ASNC) guideline, clinical experience with cardiac stress testing has continued to expand and evolve necessitating updates to the previous guidelines. This guideline presents updated practice standards for cardiac stress testing intended to support safe and effective testing across a range of clinical environments, while allowing for individualized risk assessment and protocol adaptation based on patient characteristics, special populations, and site-specific logistics. The current guideline offers practical beginning-to-end guidance on the performance of cardiac stress testing, encompassing patient selection and preparation, stress modalities and protocols, safety considerations, and result interpretation and reporting. The utilization of single photon emission computed tomography (SPECT) and positron emission tomography (PET) in conjunction with stress testing are addressed for stress protocols for each available stressor. This revised guideline is intended for a broad, multidisciplinary audience involved in the performance, interpretation, and oversight of cardiac stress testing. ASNC has developed this updated guideline to recognize the evolution of diagnostic imaging that has taken place since the last iteration of the guideline. These updates are intended to support optimal test selection, standardized patient preparation, and individualized, patient-centric practice of nuclear stress testing in diverse clinical scenarios. Utilizing this guideline, clinicians will optimize diagnostic accuracy, maintain patient safety, and at the same time maximize clinical impact.

Aims:Obtaining images of diagnostic quality using coronary CT angiography (CCTA) depends, in part, upon a patient's heart rate (HR) at time of scanning. HR reduction is most commonly achieved with beta blockers. In the paediatric population, the effectiveness of beta blockers is limited by hypotensive effects. Phenylephrine, a pure alpha agonist, raises blood pressure and causes reflex bradycardia so it could be used to reduce patients' HRs. Methods and results:We retrospectively reviewed all children at a single centre who underwent sedated CCTA study using phenylephrine between 2019 and 2024. In 25 children (mean age 5.3 ± 2.5 years), HR was reduced from a mean of 94.1 to 73.8 beats per minute (bpm). No adverse effects were reported. Images of diagnostic quality were obtained in all patients. Conclusion:In this first-of-its-kind study we found that phenylephrine was effective at reducing patients' HRs prior to CCTA, with an average reduction of 20 bpm.