The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Aims: Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results: In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion: Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Multi-Modality Imaging in Vasculitis.

Systemic vasculitides are a rare and complex group of diseases that can affect multiple organ systems. Clinically, presentation may be vague and non-specific and as such, diagnosis and subsequent management are challenging. These entities are typically classified by the size of vessel involved, including large-vessel vasculitis (giant cell arteritis, Takayasu's arteritis, and clinically isolated aortitis), medium-vessel vasculitis (including polyarteritis nodosa and Kawasaki disease), and small-vessel vasculitis (granulomatosis with polyangiitis and eosinophilic granulomatosis with polyangiitis). There are also other systemic vasculitides that do not fit in to these categories, such as Behcet's disease, Cogan syndrome, and IgG4-related disease. Advances in medical imaging modalities have revolutionized the approach to diagnosis of these diseases. Specifically, color Doppler ultrasound, computed tomography and angiography, magnetic resonance imaging, positron emission tomography, or invasive catheterization as indicated have become fundamental in the work up of any patient with suspected systemic or localized vasculitis. This review presents the key diagnostic imaging modalities and their clinical utility in the evaluation of systemic vasculitis.

How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

Does a high Haller index influence outcomes in pectus excavatum repair?

OBJECTIVE: Severity for pectus excavatum includes Haller index (HI) > 3.25. An extremely high HI (≥8) may influence surgical approach and complications. This study reviews outcomes of patients with high HI after repair. METHODS: A single institution retrospective analysis was performed on adult patients with HI ≥ 8 undergoing pectus excavatum repairs. For outcomes, a propensity score-matched control group with a HI ≤ 4 was utilized. RESULTS: In total, 64 cases (mean age, 33.5 ± 10.9 years; HI, 13.1 ± 5.0; 56% women) were included. A minimally invasive repair was successful in 84%. A hybrid procedure was performed in the remaining either to repair fractures of the ribs (8 patients) and sternum (5 patients) or when osteotomy and/or cartilage resection was required (10 patients). In comparison with the matched cohort (HI ≤ 4), patients with high HI had longer operative times (171 vs 133 minutes; P < .001), more frequently required hybrid procedures (16% vs 2%; P = .005), experienced higher incidences of rib (22% vs 3%; P = .001) and sternal fractures (12% vs 0%; P = .003), and had increased repair with 3 bars (50% vs 19%; P < .001). There were no significant differences between the groups for length of hospital stay or postoperative 30-day complications. CONCLUSIONS: Patients with an extremely high HI can be challenging cases with greater risks of fracture and need for osteotomy/cartilage resection. Despite this, minimally invasive repair techniques can be utilized in most cases without increased complications when performed by an experienced surgeon.

Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities.

Cardiac allograft vasculopathy (CAV) is a distinct form of coronary artery disease that represents a major cause of death beyond the first year after heart transplantation. The pathophysiology of CAV is still not completely elucidated; it involves progressive circumferential wall thickening of both the epicardial and intramyocardial coronary arteries. Coronary angiography is still considered the gold-standard test for the diagnosis of CAV, and intravascular ultrasound (IVUS) can detect early intimal thickening with improved sensitivity. However, these tests are invasive and are unable to visualize and evaluate coronary microcirculation. Increasing evidence for non-invasive surveillance techniques assessing both epicardial and microvascular components of CAV may help improve early detection. These include computed tomography coronary angiography (CTCA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and vasodilator stress myocardial contrast echocardiography perfusion imaging. This review summarizes the current state of diagnostic modalities and their utility and prognostic value for CAV and also evaluates emerging tools that may improve the early detection of this complex disease.

Role of Genetics in Diagnosis and Management of Hypertrophic Cardiomyopathy: A Glimpse into the Future.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.

Identifying the Causes of Unexplained Dyspnea at High Altitude Using Normobaric Hypoxia with Echocardiography.

Exposure to high altitude results in hypobaric hypoxia, leading to physiological changes in the cardiovascular system that may result in limiting symptoms, including dyspnea, fatigue, and exercise intolerance. However, it is still unclear why some patients are more susceptible to high-altitude symptoms than others. Hypoxic simulation testing (HST) simulates changes in physiology that occur at a specific altitude by asking the patients to breathe a mixture of gases with decreased oxygen content. This study aimed to determine whether the use of transthoracic echocardiography (TTE) during HST can detect the rise in right-sided pressures and the impact of hypoxia on right ventricle (RV) hemodynamics and right to left shunts, thus revealing the underlying causes of high-altitude signs and symptoms. A retrospective study was performed including consecutive patients with unexplained dyspnea at high altitude. HSTs were performed by administrating reduced FiO2 to simulate altitude levels specific to patients' history. Echocardiography images were obtained at baseline and during hypoxia. The study included 27 patients, with a mean age of 65 years, 14 patients (51.9%) were female. RV systolic pressure increased at peak hypoxia, while RV systolic function declined as shown by a significant decrease in the tricuspid annular plane systolic excursion (TAPSE), the maximum velocity achieved by the lateral tricuspid annulus during systole (S' wave), and the RV free wall longitudinal strain. Additionally, right-to-left shunt was present in 19 (70.4%) patients as identified by bubble contrast injections. Among these, the severity of the shunt increased at peak hypoxia in eight cases (42.1%), and the shunt was only evident during hypoxia in seven patients (36.8%). In conclusion, the use of TTE during HST provides valuable information by revealing the presence of symptomatic, sustained shunts and confirming the decline in RV hemodynamics, thus potentially explaining dyspnea at high altitude. Further studies are needed to establish the optimal clinical role of this physiologic method.

Opportunistic screening for coronary artery calcium deposition using chest radiographs - a multi-objective models with multi-modal data fusion.

Background: To create an opportunistic screening strategy by multitask deep learning methods to stratify prediction for coronary artery calcium (CAC) and associated cardiovascular risk with frontal chest x-rays (CXR) and minimal data from electronic health records (EHR). Methods: In this retrospective study, 2,121 patients with available computed tomography (CT) scans and corresponding CXR images were collected internally (Mayo Enterprise) with calculated CAC scores binned into 3 categories (0, 1-99, and 100+) as ground truths for model training. Results from the internal training were tested on multiple external datasets (domestic (EUH) and foreign (VGHTPE)) with significant racial and ethnic differences and classification performance was compared. Findings: Classification performance between 0, 1-99, and 100+ CAC scores performed moderately on both the internal test and external datasets, reaching average f1-score of 0.66 for Mayo, 0.62 for EUH and 0.61 for VGHTPE. For the clinically relevant binary task of 0 vs 400+ CAC classification, the performance of our model on the internal test and external datasets reached an average AUCROC of 0.84. Interpretation: The fusion model trained on CXR performed better (0.84 average AUROC on internal and external dataset) than existing state-of-the-art models on predicting CAC scores only on internal (0.73 AUROC), with robust performance on external datasets. Thus, our proposed model may be used as a robust, first-pass opportunistic screening method for cardiovascular risk from regular chest radiographs. For community use, trained model and the inference code can be downloaded with an academic open-source license from https://github.com/jeong-jasonji/MTL_CAC_classification . Funding: The study was partially supported by National Institute of Health 1R01HL155410-01A1 award.

Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy.

BACKGROUND: Constrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. However, its diagnosis remains a challenge for clinicians. Artificial intelligence may enhance the identification of CP. OBJECTIVES: The authors proposed a deep learning approach based on transthoracic echocardiography to differentiate CP from restrictive cardiomyopathy. METHODS: Patients with a confirmed diagnosis of CP and cardiac amyloidosis (CA) (as the representative disease of restrictive cardiomyopathy) at Mayo Clinic Rochester from January 2003 to December 2021 were identified to extract baseline demographics. The apical 4-chamber view from transthoracic echocardiography studies was used as input data. The patients were split into a 60:20:20 ratio for training, validation, and held-out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve. GradCAM was used for model interpretation. RESULTS: A total of 381 patients were identified, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7 ± 11.4 years, and 72.8% were male. ResNet50 had a performance with an area under the curve of 0.97 to differentiate the 2-class classification task (CP vs CA). The GradCAM heatmap showed activation around the ventricular septal area. CONCLUSIONS: With a standard apical 4-chamber view, our artificial intelligence model provides a platform to facilitate the detection of CP, allowing for improved workflow efficiency and prompt referral for more advanced evaluation and intervention of CP.

Evolution of Pain Control for Adult Pectus Excavatum Repair.

BACKGROUND: Pain control after minimally invasive repair of pectus excavatum (MIRPE) can be challenging, especially in adult patients undergoing surgical repair. This study reviewed different analgesic modalities used over ≥10 years after pectus repair. METHODS: A retrospective analysis was performed of adult patients (≥18 years) who underwent uncomplicated primary MIRPE at a single institution from October 2010 to December 2021. Patients were classified by analgesic modality used: epidural, elastomeric continuous infusion subcutaneous catheters (SC-Caths), and intercostal nerve cryoablation. Comparisons among the 3 groups were performed. RESULTS: In total, 729 patients were included (mean age, 30.9 ±10.3 years; 67% male; mean Haller index, 4.9 ±3.0). Patients in the cryoablation group required significantly lower doses of morphine equivalents (P < .001) and had overall the shortest hospital stay (mean, 1.9 ±1.5 days; P < .001) with <17% staying >2 days (vs epidural at 94% and SC-Cath at 48%; P < .001). The cryoablation group had a lower incidence of ileus and constipation (P < .001) but a higher incidence of pleural effusion requiring thoracentesis (P = .024). Mean pain scores among groups were minor (<3), and differences were insignificant. CONCLUSIONS: The use of cryoablation in conjunction with enhanced recovery pathways provided significant benefit to our patients undergoing MIRPE compared with previous analgesic modalities. These benefits included a decrease in length of hospital stay, a reduction of in-hospital opioid use, and a lower incidence of opioid-related complications associated with constipation and ileus. Further studies to assess additional potential benefits with long-term follow-up after discharge are warranted.

Role of lipoprotein(a) concentrations in bioprosthetic aortic valve degeneration.

OBJECTIVES: Lipoprotein(a) (Lp(a)) is associated with an increased incidence of native aortic stenosis, which shares similar pathological mechanisms with bioprosthetic aortic valve (bAV) degeneration. However, evidence regarding the role of Lp(a) concentrations in bAV degeneration is lacking. This study aims to evaluate the association between Lp(a) concentrations and bAV degeneration. METHODS: In this retrospective multicentre study, patients who underwent a bAV replacement between 1 January 2010 and 31 December 2020 and had a Lp(a) measurement were included. Echocardiography follow-up was performed to determine the presence of bioprosthetic valve degeneration, which was defined as an increase >10 mm Hg in mean gradient from baseline with concomitant decrease in effective orifice area and Doppler Velocity Index, or new moderate/severe prosthetic regurgitation. Levels of Lp(a) were compared between patients with and without degeneration and Cox regression analysis was performed to investigate the association between Lp(a) levels and bioprosthetic valve degeneration. RESULTS: In total, 210 cases were included (mean age 74.1±9.4 years, 72.4% males). Median time between baseline and follow-up echocardiography was 4.4 (IQR 3.7) years. Bioprostheses degeneration was observed in 33 (15.7%) patients at follow-up. Median serum levels of Lp(a) were significantly higher in patients affected by degeneration versus non-affected cases: 50.0 (IQR 72.0) vs 15.6 (IQR 48.6) mg/dL, p=0.002. In the regression analysis, high Lp(a) levels (≥30 mg/dL) were associated with degeneration both in a univariable analysis (HR 3.6, 95% CI 1.7 to 7.6, p=0.001) and multivariable analysis adjusted by other risk factors for bioprostheses degeneration (HR 4.4, 95% CI 1.9 to 10.4, p=0.001). CONCLUSIONS: High serum Lp(a) is associated with bAV degeneration. Prospective studies are needed to confirm these findings and to investigate whether lowering Lp(a) levels could slow bioprostheses degradation.

The electrocardiographic manifestations of pectus excavatum before and after surgical correction.

BACKGROUND: Pectus excavatum (PEx) can cause cardiopulmonary limitations due to cardiac compression and displacement. There is limited data on electrocardiogram (ECG) alterations before and after PEx surgical repair, and ECG findings suggesting cardiopulmonary limitations have not been reported. The aim of this study is to explore ECG manifestations of PEx before and after surgery including associations with exercise capacity. METHODS: A retrospective review of PEx patients who underwent primary repair was performed. ECGs before and after surgical correction were evaluated and the associations between preoperative ECG abnormalities and cardiopulmonary function were investigated. RESULTS: In total, 310 patients were included (mean age 35.1 ± 11.6 years). Preoperative ECG findings included a predominant negative P wave morphology in V1, and this abnormal pattern significantly decreased from 86.9% to 57.4% (p < 0.001) postoperatively. The presence of abnormal P wave amplitude in lead II (>2.5 mm) significantly decreased from 7.1% to 1.6% postoperatively (p < 0.001). Right bundle branch block (RBBB) (9.4% versus 3.9%, p < 0.001), rsr' patterns (40.6% versus 12.9%, p < 0.001), and T wave inversion in leads V1-V3 (62.3% vs 37.7%, p < 0.001) were observed less frequently after surgery. Preoperative presence of RBBB (OR = 4.8; 95%CI 1.1-21.6) and T wave inversion in leads V1-3 (OR = 2.3; 95%CI 1.3-4.2) were associated with abnormal results in cardiopulmonary exercise testings. CONCLUSION: Electrocardiographic abnormalities in PEx are frequent and can revert to normal following surgery. Preoperative RBBB and T wave inversion in leads V1-3 suggested a reduction in exercise capacity, serving as a marker for the need for further cardiovascular evaluation of these patients.

Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography.

Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.

Aortic Valve Calcium Score by Computed Tomography as an Adjunct to Echocardiographic Assessment-A Review of Clinical Utility and Applications.

Aortic valve stenosis (AS) is increasing in prevalence due to the aging population, and severe AS is associated with significant morbidity and mortality. Echocardiography remains the mainstay for the initial detection and diagnosis of AS, as well as for grading of severity. However, there are important subgroups of patients, for example, patients with low-flow low-gradient or paradoxical low-gradient AS, where quantification of severity of AS is challenging by echocardiography and underestimation of severity may delay appropriate management and impart a worse prognosis. Aortic valve calcium score by computed tomography has emerged as a useful clinical diagnostic test that is complimentary to echocardiography, particularly in cases where there may be conflicting data or clinical uncertainty about the degree of AS. In these situations, aortic valve calcium scoring may help re-stratify grading of severity and, therefore, further direct clinical management. This review presents the evolution of aortic valve calcium score by computed tomography, its diagnostic and prognostic value, as well as its utility in clinical care.

Minimally invasive repair of pectus excavatum in adults: a review article of presentation, workup, and surgical treatment.

Pectus excavatum (Pex) is one of the most common congenital deformities of the chest wall, with pectus constituting 90% of all chest wall deformities and excavatum being reported in almost 1:400 to 1:1,000 live births with predominant occurrence in males up to five times more than in females. Depending on the severity, presentation varies from mild cosmetic complaints to life limiting cardiopulmonary symptoms. Patients may develop symptoms as they age, and these symptoms may worsen over the years. A technique for minimally invasive repair for pectus excavatum (MIRPE) was introduced with the concept of temporarily implanting metal bars to correct the deformity. This has rapidly become the standard of care for the pediatric and adolescent patients. The use of MIRPE in adults, however, has been slower to adopt and more controversial. This is largely due to the increased calcification and rigidity of the chest wall in adults which can make the repair more complex and lead to a higher risk of complications. We present a literature review of the presentation, workup, and surgical treatment of adult patients with Pex undergoing MIRPE. Adult patients can, with advanced preoperative evaluations and technique modifications, undergo a highly successful repair resulting in symptom resolution and satisfying results.

Three-Dimensional Transesophageal Echocardiography in Percutaneous Catheter-Based Cardiac Interventions.

Cardiac structural and valve interventions have remained surgical procedures for several decades. The ability to directly visualize the region of interest during surgery made imaging of these structures pre- and postsurgery a secondary tool to compliment surgical visualization. The last two decades, however, have seen rapid advances in catheter-based percutaneous structural heart interventions (SHIs). Due to the "blind" nature of these interventions, imaging plays a crucial role in the success of these procedures. Fluoroscopy is used universally in all percutaneous cardiac SHIs and helps primarily in the visualization of catheters and devices. However, success of these procedures requires visualization of intracardiac soft tissue structures. Due to its portable nature and rapid ability to show cardiac structures online, transesophageal echocardiography (TEE) has become an integral tool for guidance for all percutaneous SHI. Transcatheter aortic valve replacement-one of the earliest catheter-based procedures-while initially dependent on TEE, has largely been replaced by preprocedural cardiac CT for accurate assessment of valve sizing. Developments in echocardiography now allow live three-dimensional (3D) visualization of cardiac structures mimicking surgical anatomy during TEE. Besides showing actual 3D intracardiac structures, 3D-TEE allows visualization of the interaction of intracardiac catheters and devices with soft tissue cardiac structures, thereby becoming a "second pair of eyes" for the operator. Real-time 3D-TEE now plays an important role complementing multiplane two dimensional and biplane TEE during such interventions. In this review, we discuss the incremental role of 3D-TEE during various SHIs performed today.