Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis.

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.

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.

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.

Case report: JC polyomavirus nephropathy in simultaneous heart-kidney transplantation: the role of viral-specific in situ hybridization staining.

Introduction: JC polyomavirus (JCPyV) is a ubiquitous virus that can be latent in the brain and the kidney. It is the etiologic agent responsible for progressive multifocal leukoencephalopathy, a fatal, demyelinating disease of the central nervous system, and rarely causes polyomavirus nephropathy in immunocompromised kidney transplant recipients. Case description: We present the first case of JCPyV nephropathy in a simultaneous heart-kidney transplant patient, where viral-specific in situ hybridization staining of the kidney tissue was utilized to confirm the diagnosis. The patient was diagnosed 6 years after simultaneous heart-kidney transplantation and was treated with immunosuppression reduction and intravenous immunoglobulin. Discussion: JCPyV nephropathy should be considered in the differential diagnosis of kidney allograft injury, particularly, with suggestive light microscopy histologic features in the absence of BK polyomavirus viremia and/or viruria. In addition to obtaining JCPyV PCR in the blood, in situ hybridization staining may have a utility in confirming the diagnosis. To date, we lack effective JCPyV-specific therapies, and prompt initiation of immunosuppression reduction remains the mainstay of treatment.

Comprehensive approach to cardiac amyloidosis care: considerations in starting an amyloidosis program.

Amyloidosis is a multisystem disease which continues to present in later stages due to delayed diagnosis. Once the disease is identified, the coordination of ongoing care and treatment becomes complex and often involves multiple specialists. As knowledge of the disease grows, healthcare providers within institutions have organized to create comprehensive amyloidosis programs to better serve patients in the region. In this review, we present considerations in starting a cardiac amyloidosis program from two institutions that have recently started such programs. Identification of multidisciplinary stakeholders, development of overarching program goals, creation of institutional buy-in, and emphasis on program growth and development are tenets of a successful program. The creation and growth of an amyloidosis program has the potential to raise awareness for the disease and benefit patients and institutions alike.

ATTR Amyloidosis: Current and Emerging Management Strategies: JACC: CardioOncology State-of-the-Art Review.

Transthyretin cardiac amyloidosis (ATTR-CA) is increasingly diagnosed owing to the emergence of noninvasive imaging and improved awareness. Clinical penetrance of pathogenic alleles is not complete and therefore there is a large cohort of asymptomatic transthyretin variant carriers. Screening strategies, monitoring, and treatment of subclinical ATTR-CA requires further study. Perhaps the most important translational triumph has been the development of effective therapies that have emerged from a biological understanding of ATTR-CA pathophysiology. These include recently proven strategies of transthyretin protein stabilization and silencing of transthyretin production. Data on neurohormonal blockade in ATTR-CA are limited, with the primary focus of medical therapy on judicious fluid management. Atrial fibrillation is common and requires anticoagulation owing to the propensity for thrombus formation. Although conduction disease and ventricular arrhythmias frequently occur, little is known regarding optimal management. Finally, aortic stenosis and ATTR-CA frequently coexist, and transcatheter valve replacement is the preferred treatment approach.

Treatment of AL Amyloidosis: Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) Consensus Statement 2020 Update.

Immunoglobulin light chain (AL) amyloidosis is a clonal plasma cell disorder leading to progressive and life-threatening organ failure. The heart and the kidneys are the most commonly involved organs, but almost any organ can be involved. Because of the nonspecific presentation, diagnosis delay is common, and many patients are diagnosed with advanced organ failure. In the era of effective therapies and improved outcomes for patients with AL amyloidosis, the importance of early recognition is further enhanced as the ability to reverse organ dysfunction is limited in those with a profound organ failure. As AL amyloidosis is an uncommon disorder and given patients' frailty and high early death rate, management of this complex condition is challenging. The treatment of AL amyloidosis is based on various anti-plasma cell therapies. These therapies are borrowed and customized from the treatment of multiple myeloma, a more common disorder. However, a growing number of phase 2/3 studies dedicated to the AL amyloidosis population are being performed, making treatment decisions more evidence-based. Supportive care is an integral part of management of AL amyloidosis because of the inherent organ dysfunction, limiting the delivery of effective therapy. This extensive review brings an updated summary on the management of AL amyloidosis, sectioned into the 3 pillars for survival improvement: early disease recognition, anti-plasma cell therapy, and supportive care.

Machine learning helps predict long-term mortality and graft failure in patients undergoing heart transplant.

OBJECTIVE: We aimed to develop a risk prediction model using a machine learning to predict survival and graft failure (GF) 5 years after orthotopic heart transplant (OHT). METHODS: Using the International Society of Heart and Lung Transplant (ISHLT) registry data, we analyzed 15,236 patients who underwent OHT from January 2005 to December 2009. 342 variables were extracted and used to develop a risk prediction model utilizing a gradient-boosted machine (GBM) model to predict the risk of GF and mortality 5 years after hospital discharge. After excluding variables missing at least 50% of the observations and variables with near zero variance, 87 variables were included in the GBM model. Ten fold cross-validation repeated 5 times was used to estimate the model's external performance and optimize the hyperparameters simultaneously. Area under the receiver operator characteristic curve (AUC) for the GBM model was calculated for survival and GF 5 years post-OHT. RESULTS: The median duration of follow-up was 5 years. The mortality and GF 5 years post-OHT were 27.3% (n = 4161) and 28.1% (n = 4276), respectively. The AUC to predict 5-year mortality and GF is 0.717 (95% CI 0.696-0.737) and 0.716 (95% CI 0.696-0.736), respectively. Length of stay, recipient and donor age, recipient and donor body mass index, and ischemic time had the highest relative influence in predicting 5-year mortality and graft failure. CONCLUSION: The GBM model has a good accuracy to predict 5-year mortality and graft failure post-OHT.