Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry.

AIMS: Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features. METHODS AND RESULTS: Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score. A decision threshold allowing for a 99% negative predictive value was defined in the development cohort. A protocol where TOE in patients on chronic OAC is performed depending on the LAT-AI score was validated in the external cohort. In the external testing cohort, LAT was found in 5.5% of patients. LAT-AI achieved an AUC of 0.85 [95% confidence interval (CI): 0.82-0.89], outperforming LVEF (0.81, 95% CI 0.76-0.86, P < .0001) and CHA2DS2-VASc score (0.69, 95% CI: 0.63-0.7, P < .0001) in the entire external cohort. Based on the proposed protocol, 40% of patients on chronic OAC from the external cohort would safely avoid TOE. CONCLUSION: LAT-AI allows accurate prediction of LAT. A LAT-AI-based protocol could be used to guide the decision to perform TOE despite chronic OAC.

Quantification of Aortic Valve Fibrotic and Calcific Tissue from CTA: Prospective Comparison with Histology.

Background Quantifying the fibrotic and calcific composition of the aortic valve at CT angiography (CTA) can be useful for assessing disease severity and outcomes of patients with aortic stenosis (AS); however, it has not yet been validated against quantitative histologic findings. Purpose To compare quantification of aortic valve fibrotic and calcific tissue composition at CTA versus histologic examination. Materials and Methods This prospective study included patients who underwent CTA before either surgical aortic valve replacement for AS or orthotopic heart transplant (controls) at two centers between January 2022 and April 2023. At CTA, fibrotic and calcific tissue composition were quantified using automated Gaussian mixture modeling applied to the density of aortic valve tissue components, calculated as [(volume/total tissue volume) × 100]. For histologic evaluation, explanted valve cusps were stained with Movat pentachrome as well as hematoxylin and eosin. For each cusp, three 5-µm slices were obtained. Fibrotic and calcific tissue composition were quantified using a validated artificial intelligence tool and averaged across the aortic valve. Correlations were assessed using the Spearman rank correlation coefficient. Intermodality and interobserver variability were measured using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results Twenty-nine participants (mean age, 63 years ± 10 [SD]; 23 male) were evaluated: 19 with severe AS, five with moderate AS, and five controls. Fibrocalcific tissue composition strongly correlated with histologic findings (r = 0.92; P < .001). The agreement between CTA and histologic findings for fibrocalcific tissue quantification was excellent (ICC, 0.94; P = .001), with underestimation of fibrotic composition at CTA (bias, -4.9%; 95% limits of agreement [LoA]: -18.5%, 8.7%). Finally, there was excellent interobserver repeatability for fibrotic (ICC, 0.99) and calcific (ICC, 0.99) aortic valve tissue volume measurements, with no evidence of a difference in measurements between readers (bias, -0.04 cm3 [95% LoA: -0.27 cm3, 0.19 cm3] and 0.02 cm3 [95% LoA: -0.14 cm3, 0.19 cm3], respectively). Conclusion In a direct comparison, standardized quantitative aortic valve tissue characterization at CTA showed excellent concordance with histologic findings and demonstrated interobserver reproducibility. Clinical trial registration no. NCT06136689 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Almeida in this issue.

Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography.

INTRODUCTION: There are sex differences in the extent, severity, and outcomes of coronary artery disease. We aimed to assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. METHODS: In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. RESULTS: Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p = 0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p = 0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p = 0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n = 80) and 48.3% (n = 73); p = 0.42), or CMA values > 1.56 (21.8% (n = 33) and 21.8% (n = 33); p = 1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n = 10) and 7.8% of men (n = 66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p = 0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p < 0.001). CONCLUSION: Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

PURPOSE OF REVIEW: Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. RECENT FINDINGS: Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes.

BACKGROUND: Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. METHODS: Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. RESULTS: Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). CONCLUSIONS: In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.

Role of 18F-sodium fluoride positron emission tomography in imaging atherosclerosis.

Atherosclerosis involving vascular beds across the human body remains the leading cause of death worldwide. Coronary and peripheral artery disease, which are almost universally a result of atherosclerotic plaque, can manifest clinically as myocardial infarctions, ischemic stroke, or acute lower-limb ischemia. Beyond imaging myocardial perfusion and blood-flow, nuclear imaging has the potential to depict the activity of the processes that are directly implicated in the atherosclerotic plaque progression and rupture. Out of several tested tracers to date, the literature is most advanced for 18F-sodium fluoride positron emission tomography. In this review, we present the latest data in the field of atherosclerotic 18F-sodium fluoride positron emission tomography imaging, discuss the advantages and limitation of the techniques, and highlight the aspects that require further research in the future.

Head-to-head comparison of 18F-sodium fluoride coronary PET imaging between a silicon photomultiplier with digital photon counting and conventional scanners.

BACKGROUND: We compared silicone photomultipliers with digital photon counting (SiPM) and photomultiplier tubes (PMT) PET in imaging coronary plaque activity with 18F-sodium fluoride (18F-NaF) and evaluated comprehensively SiPM PET reconstruction settings. METHODS: In 25 cardiovascular disease patients (mean age 67±12 years), we conducted 18F-NaF PET on a SiPM (Biograph Vision) and conventional PET (Discovery 710) on the same day as part of a prospective clinical trial (NCT03689946). Following administration of 250 MBq of 18F-NaF, patients underwent a contrast-enhanced CT angiography and a 30-min PET acquisition in list mode on each PET consecutively. Image noise was defined as mean standard deviation of blood pool activity within the left atria. Target-to-background ratio (TBR) and signal-to-noise ratio (SNR) were measured within the whole-vessel tubular 3-dimensional volumes of interest on the cardiac motion and attenuation corrected 18F-NaF PET images using dedicated software. RESULTS: There were significant differences in image noise and background activity between the two PETs (Image noise (%), PMT: 7.6±3.7 vs. SiPM: 4.0±2.3, p<0.001; background activity, PMT: 1.4±0.4 vs. SiPM: 1.0±0.3, p<0.001). Similarly, the SNR and TBR were significantly higher in vessels scanned with the SiPM PET (SNR, PMT: 16.3±11.5 vs. SiPM: 32.7±29.8, p<0.001; TBR, PMT: 0.8±0.4 vs. SiPM: 1.1±0.6, p<0.001). SiPM PET image reconstruction with a 256 matrix, 1.4 mm pixel, and 2 mm Gaussian filter provided best tradeoff in terms of maximal SNR, TBR and clinically practical file size. CONCLUSIONS: In 18F-NaF coronary PET imaging, the SiPM PET showed superior image contrast and less image noise compared to PMT PET.

Infarct-related artery and long-term mortality following recurrent ST-segment elevation myocardial infarction. Insights from a Polish nationwide registry.

BACKGROUND: Recurrent ST-segment elevation myocardial infarction (rSTEMI) can be attributed to the same (target-vessel, TV-rSTEMI) or different culprit vessel (non-target, nonTV-rSTEMI) compared with the first infarction. We hypothesized that long-term mortality after rSTEMI depends on the infarct-related artery (the same or different compared with the first STEMI). METHODS: Using the Polish Registry of Acute Coronary Syndromes (PL-ACS) we retrospectively identified survivors of first STEMI treated with PCI who experienced rSTEMI. We divided rSTEMI into TV-rSTEMI and nonTV-rSTEMI. We compared clinical, angiographic, and procedural characteristics and utilized propensity score matching to adjust for baseline differences. Primary outcome was 1-, 3- and 5-year all-cause mortality. RESULTS: Between 2003 and 2019 a total of 3,411 patients (mean age 63.7 years, 76% male) had rSTEMI, of whom 1,916 (56%) had TV-rSTEMI and 1,495 (44%) had nonTV-rSTEMI. Median time since first infarction was 716 days (100, 1,807). Patients with nonTV-rSTEMI had higher body mass index (27.2 vs 26.7 kg/m2, P = .041), more arterial hypertension (77.4 vs 73.7%, P = .015) and atrial fibrillation (4.8 vs 3.3%, P = .02), and lower left ventricular ejection fraction (43 [35, 50] vs 45 [38, 50]%, P <.001) compared with TV-rSTEMI. On coronary angiography nonTV-rSTEMI more frequently presented with Thrombolysis In Myocardial Infarction >1 flow (25.8 vs 15.7%, P < .001), multivessel disease (51.9 vs 40.8%, P =.002), culprit lesion located in circumflex artery (22.6 vs 5.6%, P < .001), and more frequently underwent stenting (88.8 vs 76.1%, P < .001) compared with TV-rSTEMI. There was no difference in unadjusted 1-, 3- and 5-year mortality between nonTV-rSTEMI and TV-rSTEMI. After propensity score analysis, 807 well-matched pairs of patients were selected. Adjusted 1-, 3- and 5-year mortality remained similar between nonTV-rSTEMI and TV-rSTEMI (14.7 vs 14.4%, P = .88; 23.6 vs 23.1%, P = .81; 30.0 vs 32.0%, P = .50 respectively). CONCLUSIONS: Our study shows overall low frequency of rSTEMI. Patients with nonTV-rSTEMI have different clinical and angiographic characteristics compared with TV-rSTEMI. No long-term mortality difference was observed between both groups.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks.

BACKGROUND: Coronary 18F-sodium-fluoride (18F-NaF) positron emission tomography (PET) showed promise in imaging coronary artery disease activity. Currently image processing remains subjective due to the need for manual registration of PET and computed tomography (CT) angiography data. We aimed to develop a novel fully automated method to register coronary 18F-NaF PET to CT angiography using pseudo-CT generated by generative adversarial networks (GAN). METHODS: A total of 169 patients, 139 in the training and 30 in the testing sets were considered for generation of pseudo-CT from non-attenuation corrected (NAC) PET using GAN. Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was used to align PET with CT angiography. We compared translations, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax) at the location of plaques, obtained after observer and automated alignment. RESULTS: Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 27.5 seconds per patient. Difference in displacement motion vectors between GAN-based and observer-based registration in the x-, y-, and z-directions was 0.8 ± 3.0, 0.7 ± 3.0, and 1.7 ± 3.9 mm, respectively. TBRmax had a coefficient of repeatability (CR) of 0.31, mean bias of 0.03 and narrow limits of agreement (LOA) (95% LOA: - 0.29 to 0.33). SUVmax had CR of 0.26, mean bias of 0 and narrow LOA (95% LOA: - 0.26 to 0.26). CONCLUSION: Pseudo-CT generated by GAN are perfectly registered to PET can be used to facilitate quick and fully automated registration of PET and CT angiography.