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Background: The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict., Objectives: The authors investigated whether a previously validated echocardiography-based deep learning (DL) model assessing diastolic dysfunction (DD) could identify the latent risk associated with the development and progression of AS., Methods: The authors evaluated 898 participants with AV sclerosis from the ARIC (Atherosclerosis Risk In Communities) cohort study and associated the DL-predicted probability of DD with 2 endpoints: 1) the new diagnosis of AS; and 2) the composite of subsequent mortality or AV interventions. Validation was performed in 2 additional cohorts: 1) in 50 patients with mild-to-moderate AS undergoing cardiac magnetic resonance (CMR) imaging and serial echocardiographic assessments; and 2) in 18 patients with AV sclerosis undergoing 18 F-sodium fluoride (NaF) and 18 F-fluorodeoxyglucose positron emission tomography (PET) combined with computed tomography (CT) to assess valvular inflammation and calcification., Results: In the ARIC cohort, a higher DL-predicted probability of DD was associated with the development of AS (adjusted HR: 3.482 [95% CI: 2.061-5.884]; P < 0.001) and subsequent mortality or AV interventions (adjusted HR: 7.033 [95% CI: 3.036-16.290]; P < 0.001). The multivariable Cox model (incorporating the DL-predicted probability of DD) derived from the ARIC cohort efficiently predicted the progression of AS (C-index: 0.798 [95% CI: 0.648-0.948]) in the CMR cohort. Moreover, the predictions of this multivariable Cox model correlated positively with valvular 18 F-NaF mean standardized uptake values in the PET/CT cohort (r = 0.62; P = 0.008)., Conclusions: Assessment of DD using DL can stratify the latent risk associated with the progression of early-stage AS., Competing Interests: Funding Support and Author Disclosures The work presented in this paper was supported in part by funds from the National Science Foundation (award number: 1920920). Dr Tokodi was supported by the New National Excellence Program (ÚNKP-23-4-II-SE-39) of the Ministry of Culture and Innovation in Hungary from the National Research, Development, and Innovation Fund. Dr Tokodi has received consulting fees from CardioSight outside the submitted work. Dr Hahn has received speaker fees from Abbott Structural, Baylis Medical, and Edwards Lifesciences; has institutional educational and consulting contracts for which she receives no direct compensation, with Abbott Structural, Boston Scientific, Edwards Lifesciences, and Medtronic; and is the chief scientific officer for the Echocardiography Core Laboratory at the Cardiovascular Research Foundation for multiple industry-sponsored trials for which she receives no direct industry compensation. Dr Dweck is supported by the British Heart Foundation (FS/14/78/31020); has received the Sir Jules Thorn Award for Biomedical Research 2015 (15/JTA); and has received speaker fees from Pfizer, Radcliffe Cardiology, Bristol Myers Squibb, Edwards, and Novartis and consulting fees from Novartis, Jupiter Bioventures, Beren, and Silence Therapeutics. Dr Pibarot has received funding from Edwards Lifesciences, Medtronic, Pi-Cardia, and Cardiac Success for echocardiography core laboratory analyses and research studies in transcatheter valve therapies, for which he received no personal compensation; and has received lecture fees from Edwards Lifesciences and Medtronic. Dr Yanamala serves as an advisor for Turnkey Techstart. Dr Sengupta serves as an advisor for RCE Technologies and HeartSciences. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Aortic valve area (AVA) using CT-LVOT area (AVA CT-LVOT ) <1.2 ​cm 2 has been shown comparable to echocardiography AVA of <1.0 ​cm 2 for severe aortic stenosis (AS). Current study evaluates how AS diagnosis will be affected when we substitute CT-LVOT with echo derived LVOT., Methods: We retrospectively studied 367 patients who underwent cardiac CTA and echocardiogram for assessment of high- and low-gradient AS (HG-AS and LG-AS). AVA CT-LVOT was derived from CT-LVOT area and echo doppler data. Three AVA CT-LVOT categories were created (<1.0, 1.0-1.2 and ​> ​1.2 ​cm 2 ). Outcomes were defined as composite of all-cause mortality and/or valve intervention., Results: Median echocardiographic profiles were consistent with severe AS across three AVA CT-LVOT categories for HG-AS. HG-AS patients with AVA CT-LVOT >1.2 ​cm 2 had larger median CT-LVOT area (5.06 ​cm 2 ) and AVC (2917AU). Among LG-AS with AVA CT-LVOT  ​≤1.2 cm 2 , 57% met echo criteria for low-flow LG-AS and 63% met criteria for severe AS using aortic valve calcium (AVC). Additionally, 45% with AVA CT-LVOT >1.2 ​cm 2 had larger median CT-LVOT area (5.43 ​cm 2 ) and AVC (2389AU). Patients with AVA CT-LVOT >1.2 ​cm 2 and high AVC had large body surface area and were mostly characterized as severe with indexed AVA and AVC. Stroke volume index using CT-LVOT reclassified 70% of low-flow, LG-AS as normal flow, LG-AS. Composite outcomes were higher among patients with AVA CT-LVOT ≤1.2 ​cm 2 (p ​< ​0.01), however, with no superior net reclassification improvement compared to AVA echo <1.0 ​cm 2 ., Conclusion: AVA CT-LVOT ≤1.2 ​cm 2 is a reasonable CT criterion for severe AS. Large LVOT with elevated AVC identified a severe AS phenotype despite an AVA CT-LVOT >1.2 ​cm 2 , best characterized by indexed AVA and AVC., Competing Interests: Declaration of competing interest Dr. Philippe Pibarot has received funding from Edwards Lifesciences, Medtronic, Pi-Cardia, and Cardiac Phoenix for echocardiography core laboratory analyses and research studies in the field of transcatheter valve therapies, for which he received no personal compensation. He has received lecture fees from Edwards Lifesciences and Medtronic. Dr. Rebecca T. Hahn has received speaker fees from Abbott Structural, Baylis Medical, Edwards Lifesciences, and Philips Healthcare; has institutional consulting contracts for which she receives no direct compensation with Abbott Structural, Boston Scientific, Edwards Lifesciences, Medtronic, and Novartis; and has equity with Navigate. Dr. Jonathon Leipsic has held institutional research core lab agreements with Medtronic, Edwards Lifesciences, Abbott, Boston Scientific, and Pi-Cardia. Rest of the authors have conflict of interest to disclose., (Copyright © 2023 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Background: Pre-procedural chronic kidney disease (CKD) and in-hospital acute kidney injury (AKI) are associated with worse outcomes following transcatheter aortic valve replacement (TAVR). We tested the feasibility of reducing overall AKI by avoiding pre-procedural cardiac CT angiography (CCTA) by using direct 3D-TEE guidance in TAVR patients with known CKD., Methods: An institutional TAVR database was examined from January 2016 to June 2020 to identify 396 patients in whom CCTA sizing was performed and 54 patients with creatinine (Cr) of >1.6 mg/dL in whom direct 3D-TEE, without prior CCTA, was used for TAVR guidance. Baseline demographics, procedural, echocardiographic, and clinical endpoints were compared as defined by the Valve Academic Research Consortium-2 criteria., Results: Baseline demographics and risk factors were similar in both groups other than the creatinine level in CCTA vs. TEE groups (1.33 ± 1.1 vs 1.76 ± 0.7 mg/dL, p = 0.005). Procedural contrast volume was significantly lower in the TEE group compared to the CCTA group. No differences were noted in echocardiographic and clinical endpoints for both groups. Despite higher baseline Cr, patents in the TEE group experienced a similar pattern of changes in Cr compared to the CCTA group, with an overall renal improvement noted at the time of discharge for both groups., Conclusions: In patients with baseline CKD, careful avoidance of large contrast loads associated with CCTA and intra-procedural aortography by using TEE guidance may help reduce AKI following TAVR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Objectives: The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity., Background: In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty., Methods: Using echocardiography (ECHO) measurements (ECHO cohort, n = 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine-learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT cohort, n = 752) and cardiovascular magnetic resonance (CMR) imaging (CMR cohort, n = 160). The classifier's prognostic value was further validated using clinical outcomes (aortic valve replacement [AVR] and death) observed in the ECHO and CMR cohorts., Results: In 1,964 patients from the 3 multi-institutional cohorts, 1,346 (68%) subjects had either nonsevere or discordant AS severity. Machine learning identified 1,117 (57%) patients as having high-severity and 847 (43%) as having low-severity AS. High-severity patients in CT and CMR cohorts had higher valve calcium scores and left ventricular mass and fibrosis, respectively than the low-severity group. In the ECHO cohort, progression to AVR and progression to death in patients who did not receive AVR was faster in the high-severity group. Compared with the conventional classification of disease severity, machine-learning-based severity classification improved discrimination (integrated discrimination improvement: 0.07; 95% confidence interval: 0.02 to 0.12) and reclassification (net reclassification improvement: 0.17; 95% confidence interval: 0.11 to 0.23) for the outcome of AVR at 5 years. For both ECHO and CMR cohorts, we observed prognostic value of the machine-learning classifications for subgroups with asymptomatic, nonsevere or discordant AS., Conclusions: Machine learning can integrate ECHO measurements to augment the classification of disease severity in most patients with AS, with major potential to optimize the timing of AVR., Competing Interests: Funding Support and Author Disclosures This work is supported in part by funds from the National Science Foundation (NSF: # 1920920) and National Institute of General Medical Sciences of the National Institutes of Health under (NIH: #5U54GM104942-04). Dr. Tastet is supported by a doctoral scholarship from Fonds de Recherche en Santé Québec. Dr. Newby is supported by the British Heart Foundation (CH/09/002, RE/18/5/34216, RG/16/10/32375); and is the recipient of a Wellcome Trust Senior Investigator Awrd (WT103782AIA). Dr. Dweck is supported by the British Heart Foundation (FS/14/78/31020); and is the recipient of the Sir Jules Thorn Award for Biomedical Research 2015 (15/JTA). Dr. Sengupta is a consultant for Kencor Health, RCE Technologies, and Ultromics. All other authors have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Currently, American Diabetes Association guidelines suggest statin use among persons with diabetes mellitus aged >40 years. The presence of calcified plaque in coronary arteries is a sensitive surrogate of coronary artery disease and has been shown to be an independent predictor of mortality and cardiac events., Objective: We aimed to assess the prevalence and severity of calcified plaque in coronary arteries in patients aged <40 years with and without diabetes., Methods: We included 3723 asymptomatic patients aged <40 years who had undergone coronary calcium scanning. Clinical and demographic data were collected. Agatston score was categorized into Agatston score 0 as normal, 1 to 99 as low, 100 to 399 as intermediate, and ≥400 as severe; and statistical analysis was performed., Results: The study population consisted of 4% persons with diabetes (n = 142) and 56% men with a mean age of 35 ± 5 years. Young persons with diabetes had greater prevalence of Agatston score > 0 than persons without diabetes (43% vs 24%; P < .0001). In addition, 12% of persons with diabetes vs 2.5% of persons without diabetes had an Agatston score ≥ 100 (P < .0001). The prevalence of calcified plaque in coronary arteries was >50% in persons with diabetes aged >35 years. After taking into account risk factors, the presence of diabetes was associated with a 4-fold higher odds of an Agatston score ≥ 100 (odds ratio, 4.19; 95% CI, 2.29-7.65; P < .0001)., Conclusion: Our study found that 43% of young patients with diabetes have detectable coronary atherosclerosis. Given the known clinical implications of calcified plaque in coronary arteries, future studies are needed to evaluate interventions in persons aged <40 years who exhibit subclinical atherosclerosis to reduce future cardiovascular disease events in this vulnerable population., (Copyright © 2013 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Background: Many studies have used multidetector computed tomography (MDCT) angiography to evaluate coronary stents qualitatively but not quantitatively., Objectives: This study sought to validate a method of quantitatively evaluating stent patency by using 64-row compared with invasive coronary angiography (ICA) and to evaluate the stent size threshold of MDCT in detecting stent patency., Methods: Stented lesions (n=122) in 55 patients (age, 65+/-10 years; 90% men) who underwent both 64-row MDCT and ICA were studied. Density measurements in Hounsfield units (HUs) and stent diameters in millimeters were recorded in the stented segments, with the density of the ascending aorta (AO) taken as a reference. The ratio of the average of stent's proximal, middle, and distal densities to mean AO density was defined as the AS/AO HU. Threshold values for the detection of stent patency were examined by using receiver operator characteristic (ROC) curve analysis., Results: One hundred six of 122 stents were interpretable. By ICA, 24 stents were found to have in-stent restenosis (22 interpretable and 2 noninterpretable with MDCT). The ROC curve showed that the optimal cutoff value of AS/AO HU to predict stent patency on MDCT was 0.81 with sensitivity of 90.9%, specificity of 95.2%, and the optimal stent diameter cutoff value was > or = 2.5 mm with a sensitivity of 91.8% and a specificity of 93.8%., Conclusion: With 64-row MDCT, coronary stent patency can be evaluated quantitatively with high sensitivity and specificity and with adequate diagnostic accuracy in stents > or = 2.5 mm in diameter., (2010 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)