Added prognostic value of visually estimated coronary artery calcium among heart transplant recipients.

BACKGROUND: Cardiac hybrid positron emission tomography/computed tomography (PET/CT) has become a valid screening modality for cardiac allograft vasculopathy (CAV) following heart transplantation (HT). Visually estimated coronary artery calcium (VECAC) can be quantified from CT images obtained as part of PET/CT and has been shown to be associated with adverse cardiovascular outcomes in coronary artery disease. We investigated the prognostic value of VECAC following HT. METHODS: A retrospective analysis of 430 consecutive adult HT patients who underwent 13N-ammonia cardiac PET/CT from 2016 to 2019 with follow-up through October 15, 2022, was performed. VECAC categories included: VECAC 0, VECAC 1-9, VECAC 10-99, and VECAC 100+. The association between VECAC categories and outcomes was assessed using univariable and multivariable proportional hazards regression. The primary outcome was death/retransplantation. RESULTS: The cohort was 73% male, 33% had diabetes, 67% had estimated glomerular filtration rate <60 ml/min, median age was 61 years, and median time since HT was 7.5 years. VECAC alone was insufficiently sensitive to screen for CAV. During a median follow-up of 4.2 years ninety patients experienced death or retransplantation. Compared with those with VECAC 0, patients VECAC 10-99 (HR 2.25, 95% CI 1.23-4.14, p = 0.009) and VECAC 100+ (HR 3.42, 95% CI 1.96-5.99, p < 0.001) experienced an increased risk of death/retransplantation. The association was similar for cardiovascular death and cardiovascular hospitalization. After adjusting for other predictors of death/retransplantation, VECAC 10-99 (VECAC 10-99: aHR 1.95, 95% CI 1.03-3.71 p = 0.04) and VECAC 100+ (VECAC 100+: aHR 2.33, 95% CI 1.17-4.63, p = 0.02) remained independently associated with death/retransplantation. CONCLUSIONS: VECAC is an independent prognostic marker of death/retransplantation following HT and merits inclusion as a part of post-HT surveillance PET/CT.

External Validation of the Recalibrated HEART Score for Evaluation of Possible Acute Coronary Syndrome.

A single high-sensitivity troponin-T (hs-TnT) measurement may be sufficient to risk-stratify emergency department (ED) patients with possible acute coronary syndrome (ACS) using the recalibrated History, Electrocardiogram, Age, Risk Factors, Troponin (rHEART) score. We sought to validate this approach in a multi-ethnic population of US patients and investigate sex-specific differences in performance. We conducted a secondary analysis of a prospective cohort study of adult ED patients with possible ACS at a single, urban, academic hospital. We investigated the diagnostic performance of rHEART for the incidence of type-1 acute myocardial infarction (AMI) and other major adverse cardiac events (MACE) at 30 days, using both single (19 ng/L) and sex-specific (14 ng/L for females, 22 ng/L for males) 99th percentile hs-TnT thresholds. The 821 patients included were 54% female, 57% Hispanic, and 26% Black. 4.6% patients had MACE, including 2.4% with AMI. Single threshold rHEART ≤ 3 had sensitivity of 94.4% (95% confidence interval, 81-99%) and negative predictive values (NPV) of 99.3% (98-100%) for MACE; sex-specific thresholds performed nearly identically. Sensitivity and NPV for AMI were 90.0% (67-98%) and 99.3% (97-100%). Excluding patients presenting < 3 hours from symptom onset improved sensitivity for MACE and AMI to 97.0% (84-100%) and 94.1% (71-100%). Logistic regression demonstrated odds of MACE increased with higher rHEART scores at a similar rate for males and females. In conclusion, a single initial hs-TnT and rHEART score can be used to risk-stratify male and female ED patients with possible ACS, especially when drawn > 3 hours after symptom onset.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study.

Background: Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification. Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves. Findings: End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001). Interpretations: CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value. Research in context: Evidence before this study: Fully automated volumetric body composition analysis of chest computed tomography attenuation correction (CTAC) can be obtained in patients undergoing myocardial perfusion imaging. This new information has potential to significantly improve risk stratification and patient management. However, the CTAC scans have not been utilized for body composition analysis to date. We searched PubMed and Google Scholar for existing body composition related literature on June 5, 2024, using the search term ("mortality") AND ("risk stratification" OR "survival analysis" OR "prognostic prediction" OR "prognosis") AND ("body composition quantification" OR "body composition analysis" OR "body composition segmentation"). We identified 34 articles either exploring body composition segmentation or evaluating clinical value of body composition quantification. However, to date, all the prognostic evaluation is performed for quantification of three or fewer types of body composition tissues. Within the prognostic studies, only one used chest CT scans but utilized only a few specified slices selected from the scans, and not a standardized volumetric analysis. None of these previous efforts utilized CTAC scans, and none included epicardial adipose tissue in comprehensive body composition analysis.Added value of this study: In this international multi-center study, we demonstrate a novel artificial intelligence-based annotation-free approach for segmenting six key body composition tissues (bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue, epicardial adipose tissue, and visceral adipose tissue) from low-dose ungated CTAC scans, by exploiting existing CT segmentation models and image processing techniques. We evaluate the prognostic value of metrics derived from volumetric quantification of CTAC scans obtained during cardiac imaging, for all-cause mortality prediction in a large cohort of patients. We reveal strong and independent associations between several volumetric body composition metrics and all-cause mortality after adjusting for existing clinical factors, and available cardiac perfusion and atherosclerosis biomarkers.Implications of all the available evidence: The comprehensive body composition analysis can be routinely performed, at the point of care, for all cardiac perfusion scans utilizing CTAC. Automatically-obtained volumetric body composition quantification metrics provide added value over existing risk factors, using already-obtained scans to significantly improve the risk stratification of patients and clinical decision-making.

Current and Emerging Approaches for Primary Prevention of Coronary Artery Disease Using Cardiac Computed Tomography.

PURPOSE OF REVIEW: To summarize the current use of cardiac computed tomography (CT) technologies as well as their pertinent evidence in regards to prevention of coronary artery disease (CAD). RECENT FINDINGS: Cardiac CTA has now become a main non-invasive method for the evaluation of symptomatic CAD. In addition to coronary calcium score, other CT technologies such as atherosclerotic plaque analysis, fractional flow reserve estimation by CT, pericoronary fat attenuation, and endothelial wall shear stress have emerged. Whether the use of CT modalities can enhance risk prediction and prevention in CAD has not been fully answered. We discuss the evidence for coronary artery calcium scoring and coronary CT angiography in primary prevention and the current barriers to their use. We attempt to delineate what can be done to expand use and what studies are needed to broaden adoption in the future. We also examine the potential roles of emerging CT technologies. Finally, we describe potential clinical approaches to prevention that would incorporate cardiac CT technologies.

Effects of confounding and effect-modifying lifestyle, environmental and medical factors on risk of radiation-associated cardiovascular disease.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. It has been known for some considerable time that radiation is associated with excess risk of CVD. A recent systematic review of radiation and CVD highlighted substantial inter-study heterogeneity in effect, possibly a result of confounding or modifications of radiation effect by non-radiation factors, in particular by the major lifestyle/environmental/medical risk factors and latent period. METHODS: We assessed effects of confounding by lifestyle/environmental/medical risk factors on radiation-associated CVD and investigated evidence for modifying effects of these variables on CVD radiation dose-response, using data assembled for a recent systematic review. RESULTS: There are 43 epidemiologic studies which are informative on effects of adjustment for confounding or risk modifying factors on radiation-associated CVD. Of these 22 were studies of groups exposed to substantial doses of medical radiation for therapy or diagnosis. The remaining 21 studies were of groups exposed at much lower levels of dose and/or dose rate. Only four studies suggest substantial effects of adjustment for lifestyle/environmental/medical risk factors on radiation risk of CVD; however, there were also substantial uncertainties in the estimates in all of these studies. There are fewer suggestions of effects that modify the radiation dose response; only two studies, both at lower levels of dose, report the most serious level of modifying effect. CONCLUSIONS: There are still large uncertainties about confounding factors or lifestyle/environmental/medical variables that may influence radiation-associated CVD, although indications are that there are not many studies in which there are substantial confounding effects of these risk factors.

Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR).

BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification. METHODS: A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation. RESULTS: A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for moderate/severe MR) and external test sets (exact accuracy, 79%; κ=0.80; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR). Most (63% internal and 66% external) misclassification disagreements were between none/trace and mild MR. MR classification accuracy was slightly higher using multiple TTE views (accuracy, 82%) than with only apical 4-chamber views (accuracy, 80%). In subset analyses, the model was accurate in the classification of both primary and secondary MR with slightly lower performance in cases of eccentric MR. In the analysis of the 6-step classification system, the exact accuracy was 80% and 76% with a ±1 step agreement of 99% and 98% in the internal and external test set, respectively. CONCLUSIONS: This end-to-end DL system can intake entire echocardiogram studies to accurately classify MR severity and may be useful in helping clinicians refine MR assessments.

First Experience With Augmented Reality Guidance for Cerebral Embolic Protection During TAVR.

Background: Augmented reality (AR) guidance holds potential to improve transcatheter interventions by enabling visualization of and interaction with patient-specific 3-dimensional virtual content. Positioning of cerebral embolic protection devices (CEP) during transcatheter aortic valve replacement (TAVR) increases patient exposure to radiation and iodinated contrast, and increases procedure time. AR may enhance procedural guidance and facilitate a safer intervention. Objectives: The purpose of this study was to develop and test a novel AR guidance system with a custom user interface that displays virtual, patient-specific 3-dimensional anatomic models, and assess its intraprocedural impact during CEP placement in TAVR. Methods: Patients undergoing CEP during TAVR were prospectively enrolled and assigned to either AR guidance or control groups. Primary endpoints were contrast volume used prior to filter placement, times to filter placement, and fluoroscopy time. Postprocedure questionnaires were administered to assess intraprocedural physician experience with AR guidance. Results: A total of 24 patients presenting for TAVR were enrolled in the study (12 with AR guidance and 12 controls). AR guidance eliminated the need for aortic arch angiograms prior to device placement thus reducing contrast volume (0 mL vs 15 mL, P < 0.0001). There was no significant difference in the time required for filter placement or fluoroscopy time. Postprocedure questionnaires indicated that AR guidance increased confidence in wiring of the aortic arch and facilitated easier device placement. Conclusions: We developed a novel AR guidance system that eliminated the need for additional intraprocedural angiograms prior to device placement without any significant difference in time to intervention and offered a subjective improvement in performance of the intervention.

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Influence of Body Mass Index on Radiation Exposure Across Imaging Modalities in the Evaluation of Chest Pain.

BACKGROUND: Essential to a patient-centered approach to imaging individuals with chest pain is knowledge of differences in radiation effective dose across imaging modalities. Body mass index (BMI) is an important and underappreciated predictor of effective dose. This study evaluated the impact of BMI on estimated radiation exposure across imaging modalities. METHODS AND RESULTS: This was a retrospective analysis of patients with concern for cardiac ischemia undergoing positron emission tomography (PET)/computed tomography (CT), cadmium zinc telluride single-photon emission CT (SPECT) myocardial perfusion imaging, or coronary CT angiography (CCTA) using state-of-the-art imaging modalities and optimal radiation-sparing protocols. Radiation exposure was calculated across BMI categories based on established cardiac imaging-specific conversion factors. Among 9046 patients (mean±SD age, 64.3±13.1 years; 55% men; mean±SD BMI, 30.6±6.9 kg/m2), 4787 were imaged with PET/CT, 3092 were imaged with SPECT/CT, and 1167 were imaged with CCTA. Median (interquartile range) radiation effective doses were 4.4 (3.9-4.9) mSv for PET/CT, 4.9 (4.0-6.3) mSv for SPECT/CT, and 6.9 (4.0-11.2) mSv for CCTA. Patients at a BMI <20 kg/m2 had similar radiation effective dose with all 3 imaging modalities, whereas those with BMI ≥20 kg/m2 had the lowest effective dose with PET/CT. Radiation effective dose and variability increased dramatically with CCTA as BMI increased, and was 10 times higher in patients with BMI >45 kg/m2 compared with <20 kg/m2 (median, 26.9 versus 2.6 mSv). After multivariable adjustment, PET/CT offered the lowest effective dose, followed by SPECT/CT, and then CCTA (P<0.001). CONCLUSIONS: Although median radiation exposure is modest across state-of-the-art PET/CT, SPECT/CT, and CCTA systems using optimal radiation-sparing protocols, there are significant variations across modalities based on BMI. These data are important for making patient-centered decisions for ischemic testing.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Cardiovascular testing recovery in Latin America one year into the COVID-19 pandemic: An analysis of data from an international longitudinal survey.

Background: The COVID-19 pandemic disproportionately impacted Latin America (LATAM), significantly disrupting cardiovascular testing. This study evaluated cardiac procedure recovery in LATAM one year after the outbreak. Methods: The International Atomic Energy Agency (IAEA) surveyed 669 centers in 107 countries worldwide, including 135 facilities in 19 LATAM countries, to assess cardiovascular procedure volumes in March 2019, April 2020, and April 2021, and changes in center practices and staffing conditions one year into the COVID-19 pandemic. Findings: LATAM centers reported a 21 % decrease in procedure volumes in April 2021 from pre-pandemic-baseline, vs. a 0 % change in the rest of the world (RoW), and greater volume reductions for almost all procedure types. Centers in Central America and Mexico reported the largest procedure reductions (47 % reduction) compared to the Caribbean (15 %), and South America (14 %, p = 0.01), and this LATAM region was a significant predictor of lower procedure recovery in multivariable regression. More LATAM centers reported reduced salaries and increased layoffs of clinical staff compared to RoW, and LATAM respondents estimated that half of physician and non-physician staff experienced excess psychological stress related to the pandemic, compared to 25 % and 30 % in RoW (p < 0.001). Conclusions: Cardiovascular testing recovery in LATAM trailed behind RoW for most procedure types, with centers in Central America and Mexico reporting the greatest volume reductions. This study found lasting impacts of COVID-19 on cardiovascular care in LATAM and the need for mental health support for LATAM healthcare workers in current and future pandemics.

Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography.

AIMS: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex. METHODS AND RESULTS: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. CONCLUSION: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.

Recovery Rates of Diagnostic Cardiac Procedural Volume in Oceania 1 Year Into COVID-19: The IAEA Non-Invasive Cardiology Protocol Survey on COVID-19 (INCAPS COVID 2).

AIM: The aim of this study was to assess the recovery rates of diagnostic cardiac procedure volumes in the Oceania Region, midway through the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A survey was performed comparing procedure volumes between March 2019 (pre-pandemic), April 2020 (during first wave of COVID-19 pandemic), and April 2021 (1 year into the COVID-19 pandemic). A total of 31 health care facilities within Oceania that perform cardiac diagnostic procedures were surveyed, including a mixture of metropolitan and regional, hospital and outpatient, public and private sites, as well as teaching and non-teaching hospitals. A comparison was made with 549 centres in 96 countries in the rest of the world (RoW) outside of Oceania. The total number and median percentage change in procedure volume were measured between the three timepoints, compared by test type and by facility. RESULTS: A total of 11,902 cardiac diagnostic procedures were performed in Oceania in April 2021 as compared with 11,835 pre-pandemic in March 2019 and 5,986 in April 2020; whereas, in the RoW, 499,079 procedures were performed in April 2021 compared with 497,615 pre-pandemic in March 2019 and 179,014 in April 2020. There was no significant difference in the median recovery rates for total procedure volumes between Oceania (-6%) and the RoW (-3%) (p=0.81). While there was no statistically significant difference in percentage recovery been functional ischaemia testing and anatomical coronary testing in Oceania as compared with the RoW, there was, however, a suggestion of poorer recovery in anatomical coronary testing in Oceania as compared with the RoW (CT coronary angiography -16% in Oceania vs -1% in RoW, and invasive coronary angiography -20% in Oceania vs -9% in RoW). There was no statistically significant difference in recovery rates in procedure volume between metropolitan vs regional (p=0.44), public vs private (p=0.92), hospital vs outpatient (p=0.79), or teaching vs non-teaching centres (p=0.73). CONCLUSIONS: Total cardiology procedure volumes in Oceania normalised 1 year post-pandemic compared to pre-pandemic levels, with no significant difference compared with the RoW and between the different types of health care facilities.

Disparities in Noninvasive Traditional and Advanced Testing for Coronary Artery Disease: Findings from the INCAPS-COVID 2 Study.

The COVID-19 pandemic disrupted the delivery of cardiovascular care, including noninvasive testing protocols and test selection for the evaluation of coronary artery disease (CAD). Trends in test selection in traditional versus advanced noninvasive tests for CAD during the pandemic and in countries of varying income status have not been well studied. The International Atomic Energy Agency conducted a global survey to assess the pandemic-related changes in the practice of cardiovascular diagnostic testing. Site procedural volumes for noninvasive tests to evaluate CAD from March 2019 (prepandemic), April 2020 (onset), and April 2021 (initial recovery) were collected. We considered traditional testing modalities, such as exercise electrocardiography, stress echocardiography, and stress single-photon emission computed tomography, and advanced testing modalities, such as stress cardiac magnetic resonance, coronary computed tomography angiography, and stress positron emission tomography. Survey data were obtained from 669 centers in 107 countries, reporting the performance of 367,933 studies for CAD during the study period. Compared with 2019, traditional tests were performed 14% less frequently (recovery rate 82%) in 2021 versus advanced tests, which were performed 15% more frequently (128% recovery rate). Coronary computed tomography angiography, stress cardiac magnetic resonance, and stress positron emission tomography showed 14%, 25%, and 25% increases in volumes from 2019 to 2021, respectively. The increase in advanced testing was isolated to high- and upper middle-income countries, with 132% recovery in advanced tests by 2021 compared with 55% in lower income nations. The COVID-19 pandemic exacerbated economic disparities in CAD testing practice between wealthy and poorer countries. Greater recovery rates and even new growth were observed for advanced imaging modalities; however, this growth was restricted to wealthy countries. Efforts to reduce practice variations in CAD testing because of economic status are warranted.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study.

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction. FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36). INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results. FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].

Cardiovascular Testing in the United States during the COVID-19 Pandemic: Volume Recovery and Worldwide Comparison.

Purpose: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. Materials and Methods: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. Results: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. Conclusion: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.