Pereyra, Milagros

A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.

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.

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.

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.

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.

Abstract 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.

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.

Current management of patients with congenital heart disease has increased their survival into adulthood. This is accompanied by potential cardiac complications, including pulmonary hypertension associated with congenital heart disease (PAH-CHD). PAH-CHD constitutes a challenging subgroup of pulmonary hypertension and requires expert management to improve quality of life and prognosis. Novel agents have shown a significant improvement in morbidity and mortality in patients with pulmonary arterial hypertension. However, the long-term effects of these medications on PAH-CHD patients remain somewhat uncertain, necessitating treatment plans largely founded on the clinical experience of the healthcare providers. The aim of this review is to summarize the current evidence and future perspectives regarding treatment strategies for PAH-CHD to help better guide management of this complex disease.

Introduction: Cardiac amyloidosis (CA) has been suggested to be common (up to 16%) in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). CA has poorer outcomes and its assessment in all TAVR patients may be costly and challenging. Hypothesis: CA may be screened for with an artificial intelligence (AI) clinical predictive model applied to pre-TAVR ECGs, and high probability (>50%) for CA may be associated with worse clinical outcomes. Methods: In this retrospective analysis of the Mayo institutional National Cardiovascular Disease Registry (NCDR)-TAVR, patients undergoing a TAVR between Jan-2012 and Dec-2017 were included. Pre-TAVR ECGs were analyzed by the AI predictive model for CA embedded in the EMR. EMRs were reviewed to assess for a clinical diagnosis of CA in those identified as high probability (>50%) by the AI tool. Clinical outcomes on 1 year follow-up were extracted from the NCDR-TAVR database and Kaplan-Meier curves were created to compare patients with high CA probability (≥50%) versus those with low probability (<50%). Results: Of 1070 patients who underwent TAVR (mean age 81.1± 8.5 years, 58.2% male), 263 (24.6%) had ≥50% probability for CA on pre-procedure ECG. Only 18 (1.7%) of these had clinical diagnosis of CA. In the survival analysis, high probability of CA by AI tool was associated with higher all-cause mortality (HR 1.60, 95%CI 1.09-2.34, p=0.01) and greater HF admissions (HR 1.59, 95%CI 1.08-2.35, p<0.01). Neither TIA/stroke nor myocardial infarction were statistically significant. MACE (TIA/stroke, myocardial infarction, heart failure admissions) was higher in the ≥50% CA probability group (HR 1.68, 95%CI 1.17-2.40, p<0.01), (Figure) . Conclusion: CA is clinically underdiagnosed in patients undergoing TAVR. AI applied to pre-TAVR ECGs suggesting higher probability for CA identifies a subgroup at higher risk of clinical events, who may benefit from further diagnostic evaluation for CA.

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.