Rosenthal, Julie

Abstract Aims Pre-capillary pulmonary hypertension (PHTN), typically due to intrinsic pulmonary pathology, requires right heart catheterization (RHC) for diagnosis. ePLAR, the echocardiographic pulmonary to left atrial ratio [calculated: tricuspid regurgitation maximum velocity (TRVmax) divided by mitral valve E to e′ wave ratio (E/e′)], has been demonstrated as a promising non-invasive surrogate for characterization of pre-capillary PHTN. As such, this study aimed to further evaluate the diagnostic utility of ePLAR in a large cohort of patients with PHTN. Methods and results All RHCs between 2018 and 2023 across three tertiary centres were retrospectively reviewed. Patients with PHTN were characterized as pre-capillary or post-capillary (isolated or combined). Concurrent ePLAR was calculated. ROC analysis evaluated the accuracy of ePLAR in detecting pre-capillary PHTN. Overall, 2235 patients had PHTN by RHC, mean age 63.5 ± 14.7 years, 53.5% male. Of these, 43.3% had pre-capillary PHTN. ROC analysis demonstrated ePLAR to have good accuracy in detection of pre-capillary PHTN (AUC 0.760), with a cutoff of 0.20 m/s demonstrating a sensitivity and specificity of 77% and 60% respectively. This persisted on sensitivity analysis for contemporaneous investigations and for right ventricular function. Conclusion In this large cohort, ePLAR was validated as an accurate tool for identifying patients with pre-capillary PHTN. This widely available, non-invasive, and reproducible parameter can have an important role in the work up of PHTN and may guide the need for invasive testing.

Background: Among cardiac amyloidosis subtypes, transthyretin cardiac amyloidosis (ATTR) has gained increased recognition with the development of disease-specific therapies. Tafamidis, a selective oral stabilizer of the transthyretin tetramer, has emerged as an effective treatment. In the broader heart failure landscape, sodium-glucose cotransporter 2 inhibitors (SGLT2i) have become cornerstone therapies across the ejection fraction spectrum, improving survival and reducing hospitalizations in both HFrEF and HFpEF. However, the clinical benefit of combining Tafamidis with SGLT2i in ATTR remains largely unknown. Hypothesis: Combination therapy with Tafamidis and SGLT2i provides added clinical benefits in patients with ATTR cardiac amyloidosis. Methods: This retrospective cohort study included adults with confirmed ATTR cardiac amyloidosis diagnosed via 99mTc-pyrophosphate scintigraphy and treated with Tafamidis alone or with an SGLT2i across three academic centers from January 2019 to February 2025. Baseline characteristics and outcomes were collected from medical records. The primary endpoint was all-cause mortality. Kaplan-Meier analysis and multivariable Cox proportional hazards modeling were used, adjusting for age, sex, atrial fibrillation, diabetes, hypertension, hyperlipidemia, ejection fraction (EF), and glomerular filtration rate (GFR). Results: Among the 1,211 patients included in the 3-year follow-up analysis,946 received Tafamidis monotherapy and 265 received a combination of Tafamidis and an SGLT2i. The mean age of the cohort was 71.9 years (SD ±9.4), and 87.3% were male. A total of 142 mortality events occurred, with a higher cumulative mortality rate observed in the tafamidis-only group (16.3%) compared to the combination group (9.4%) (log-rank p = 0.042). In the multivariable Cox proportional hazards model, combination therapy with Tafamidis and an SGLT2i was independently associated with a significantly lower risk of mortality (adjusted HR =0.576, 95% CI:0.334–0.992, p =0.04). Additionally, increasing age (HR =1.049, 95% CI:1.024–1.074, p < 0.001) was associated with higher mortality, while EF(HR =0.971, 95% CI:0.958–0.984, p< 0.001) and GFR (HR =0.975, 95% CI:0.965-0.985, p< 0.001) were with lower mortality. Conclusion: Combination therapy with Tafamidis and SGLT2i was associated with improved survival compared to Tafamidis alone in ATTR cardiac amyloidosis. Further prospective trials are needed to validate these findings and guide therapy optimization.

Background: Light chain (AL) amyloidosis is a rare and understudied plasma cell disorder. It is caused by abnormal misfolding of monoclonal immunoglobulin light chains, leading to extracellular deposition of amyloid, ultimately resulting in organ dysfunction and death. Since AL amyloidosis is uncommon, therapies are adapted from the more common plasma cell disorders like multiple myeloma. Daratumumab, a monoclonal antibody targeting CD38, was first tested in several prospective and retrospective studies in AL patients with relapsed refractory disease. The ANDROMEDA study (Kastritis E et al, NEJM 2021) established daratumumab in combination with CyBorD (Cyclophosphamide-Bortezomib-Dexamethasone) followed by daratumumab maintenance as the new standard of care compared to CyBorD alone for newly diagnosed patients. Consequently, daratumumab maintenance has become universally offered to all newly diagnosed AL amyloidosis patients. Notably, there was no second randomization before the initiation of maintenance therapy in ANDROMEDA. Hence, a critical question that remains is whether the superior efficacy observed in the investigational arm of the ANDROMEDA trial is solely a result of the addition of daratumumab during the induction phase or if the maintenance phase contributed to the favorable outcomes in the investigational arm. We hypothesize that a shorter duration of maintenance would prove non-inferior to the standard duration of daratumumab maintenance in patients with AL amyloidosis that have responded to Daratumumab-CyBorD induction. To answer this question we are conducting a pragmatic phase 2 randomized trial assessing the optimal duration of daratumumab maintenance in patients with AL amyloidosis who have responded to Daratumumab-CyBorD induction. Study Design, Patients and Methods: We use broadened eligibility criteria consistent with pragmatic trial design. The estimated total enrollment is up to 96 patients. The main eligibility criteria are patients with biopsy-proven AL Amyloidosis who have completed 6 cycles of Dara-CyBorD-based induction treatment and achieved an adequate response to induction (defined as hematological complete response (CR), hematological very good partial response (VGPR), or hematological partial response (PR) with at least one organ response). Patients in whom bortezomib and/or cyclophosphamide were omitted from induction due to toxicity concerns or adverse effects are allowed. The major exclusion criteria are patients who were concomitantly diagnosed with multiple myeloma based on International Myeloma Working Group (IMWG) criteria. This phase II, open-label, multicenter trial (NCT05898646) randomizes patients who have attained an adequate response to 6 cycles of Dara-CyBorD-based induction in a 1:1 ratio, using a dynamic allocation procedure that balances the marginal distributions of the stratification factors (age, cardiac stage, and t(11;14) status). Randomization is to either single-agent daratumumab maintenance of 6 cycles duration (experimental arm) or 18 cycles of daratumumab maintenance (control arm) (cycle = 28 days). Treatment is allowed to be given at a local facility After completing maintenance treatment, patients will follow up every 3 months for up to 36 months from registration. The primary endpoint of the trial is to evaluate the non-inferiority of a shorter (6 cycles) daratumumab maintenance measured by event-free survival (EFS) compared to the standard maintenance of 18 cycles in newly diagnosed AL amyloidosis who receive 6 cycles of daratumumab-CyBorD based induction. EFS in this study is defined as time from registration to any of the following events: hematological progression, organ progression, subsequent anti-plasma cell therapy in the absence of hematological progression but with persistent organ dysfunction or death, whichever occurs first. Secondary endpoints are depth of hematological and organ response, bone marrow measurable residual disease negativity rate, duration of response, quality of life, and survival. By investigating whether a shorter duration of maintenance is non-inferior to the standard duration, we aim to inform clinical practice and potentially reduce the toxicities associated with prolonged maintenance therapy, including hypogammaglobulinemia, recurrent infections, the need for intravenous gammaglobulin replacement, and financial toxicity.

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.

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.

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 &gt;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.

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.

Background: Despite advancements in the management of cardiac amyloidosis, the contemporary estimates of the cardiovascular deaths (CVDs) in amyloidosis are not well-established. Objective: This study sought to assess the longitudinal trends of CVDs with comorbid amyloidosis and analyze the disparities in mortality in terms of race, gender, and geographical location. Methods: We queried the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research database and performed serial cross-sectional analyses of national death certificate data for cardiovascular mortality with comorbid amyloidosis among patients aged 25 and above. Diseases of the circulatory system (ICD-10 I00-I99) were listed as the underlying cause of death, and amyloidosis (ICD-10 E85) as contributing cause of death. We calculated age-adjusted mortality rates (AAMRs) per 1,000,000 individuals and determined the trends over time by estimating the annual percent change using the Joinpoint Regression Program. Results: In the 22-year study period, 4,145 CVDs with comorbid amyloidosis in the United States were identified between 1999 and 2020. The AAMRs increased significantly from 0.69 per 1,000,000 individuals in 1999 to 2.13 per 1,000,000 individuals in 2020 (p< 0.05), with an annual percent increase of +3.92. The AAMRs for the span of 22 years were higher in males than females (1.66 vs. 0.75, p<0.05). When stratified by race, African American populations had the highest cumulative AAMR (2.34) compared to other racial groups. In terms of urbanization, the AAMR was significantly higher in the urban regions compared to the rural areas (1.15 vs. 0.91, p<0.05). Conclusion: The AAMRs from CVDs with comorbid amyloidosis have increased significantly over the past two decades, with significant disparities in male and African American individuals. This emphasises the need of further research in the development of effective treatments for amyloid heart disease.

Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Disease-modifying therapies in both light chain and transthyretin amyloidosis have improved patient functional status and survival. Conceivably, as heart failure may progress despite amyloid therapies, more patients may be considered for heart transplantation. In earlier eras, extra-cardiac amyloid deposits significantly reduced post-heart transplant patient survival and functional status compared to the non-amyloid population. In the modern era, transplant centres have reported improved outcomes in amyloidosis as patient selection has grown more stringent. Importantly, systematic candidate evaluation should assess the degree of extra-cardiac involvement, the effectiveness of disease-modifying therapies and downstream effects on patients’ nutrition and frailty. This review outlines such an overall approach while also considering that organ-specific selection criteria may vary between individual transplant centres. A methodical approach to patient evaluation will promote better understanding of the prevalence and severity of extra-cardiac disease in amyloidosis patients referred for heart transplantation and of any disparities in decision outcomes in this population.