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Background: Atrial fibrillation (AF) significantly impacts quality of life (QoL) and is often observed in patients with transthyretin amyloid cardiomyopathy (ATTR-CM). Acoramidis achieves near-complete (≥90%) transthyretin (TTR) stabilization and is approved in the USA, the UK, Europe and Japan for the treatment of ATTR-CM in adults. In the phase 3 ATTRibute-CM study (NCT03860935), acoramidis treatment significantly reduced the time to all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) at Month 30, and resulted in improved 6-minute walk distance (6MWD), Kansas City Cardiomyopathy Questionnaire-Overall Summary (KCCQ-OS) scores and N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels relative to placebo. The clinical efficacy of acoramidis has not yet been reported in patients with ATTR-CM and with an AF or atrial flutter (AFL) diagnosis at baseline. Research Question: How does acoramidis treatment affect ACM or first CVH, 6MWD, KCCQ-OS scores, and NT-proBNP levels at Month 30 in participants with ATTR-CM with or without AF/AFL diagnosis at baseline? Methods: The ATTRibute-CM study design has been previously published. This post hoc analysis included participants in the modified intention-to-treat population (N=611) grouped by AF/AFL diagnosis at baseline (defined as recorded AF medical history or the presence of AF or AFL on an ECG at enrollment). The rate of ACM or first CVH at Month 30 was compared between groups using a stratified Cox proportional hazards model. Changes from baseline to Month 30 in 6MWD, KCCQ-OS scores and NT-proBNP levels were summarized descriptively. Results: Overall, 62.8% (384/611) of participants had an AF/AFL diagnosis at baseline (acoramidis: 255/409, placebo: 129/202). These participants had lower mean 6MWD and KCCQ-OS scores and higher median NT-proBNP levels than those without AF/AFL diagnosis at baseline. Treatment with acoramidis reduced ACM or first CVH, slowed the decline in 6MWD and KCCQ-OS scores and blunted the rise in NT-proBNP compared with placebo, regardless of AF/AFL diagnosis at baseline ( Table ). Conclusions: Acoramidis improved clinical outcomes (ACM, CVH), functional status, QoL and NT-proBNP levels relative to placebo in participants with ATTR-CM, regardless of AF/AFL diagnosis at baseline.

Introduction: Transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) arises from amyloidogenic aggregates of destabilized TTR protein in variant (ATTRv-CM) or acquired wild-type TTR (ATTRwt-CM). The p.V142I (V122I) variant is the most common pathogenic TTR allele in the USA and has higher mortality vs wild type. Acoramidis, an oral TTR stabilizer achieving ≥90% stabilization, is approved in USA, UK, Europe, and Japan for treating ATTR-CM in adults. In ATTRibute-CM (NCT03860935), acoramidis showed a reduction in all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) vs placebo (PBO), with consistent benefit in ATTRwt-CM and ATTRv-CM (all variants). We report clinical outcomes in participants with p.V142I ATTRv-CM. Research Question: What was the efficacy of acoramidis for clinical outcomes (ACM, CVH) in participants with p.V142I ATTRv-CM in ATTRibute-CM? Methods: In ATTRibute-CM, 632 participants were randomized 2:1 to receive acoramidis HCl 800 mg or PBO twice daily for 30 months. All participants enrolled in open-label extension (OLE) received acoramidis only. Exploratory post hoc analyses were done in the p.V142I ATTRv-CM subgroup. Time-to-event analyses used a stratified Cox proportional hazards model using treatment group as an explanatory factor and baseline 6MWD as a covariate, stratified by randomization factors: serum NT-proBNP and eGFR. ACM was analyzed at Month 42 (ATTRibute-CM 30 months + 12 months OLE). Results: Of the 59 patients with ATTRv-CM reported at randomization, 35 (59.3%) had p.V142I (23 acoramidis, 12 PBO) with comparable baseline characteristics; 4 were homozygous for p.V142I (1 acoramidis, 3 PBO). Through Month 30, ACM/first CVH occurred in 43.5% (10/23) of acoramidis vs 83.3% (10/12) in PBO (HR 0.311; 95% CI 0.120–0.805; Figure 1 ). Through Month 42, ACM occurred in 26.1% (6/23) of continuous acoramidis vs 66.7% (8/12) in PBO to acoramidis (HR 0.307; 95% CI 0.097–0.973; Figure 2 ). Conclusions: In participants with p.V142I ATTRv-CM, acoramidis use was associated with 69% risk reduction in ACM/first CVH through Month 30 and ACM through Month 42 vs PBO. This is the first report of clinical benefit of this magnitude observed in this high-risk population. These findings have biologic plausibility and may reflect the near-complete stabilization observed experimentally with acoramidis in p.V142I ATTR fibrils. These observations require confirmation in larger cohort studies.

Introduction: Patients with variant transthyretin (TTR) amyloid cardiomyopathy (ATTRv-CM) experience early onset disease, rapid progression, and poor outcomes due to amyloid aggregated from destabilized tetrameric TTR. Acoramidis, a highly selective, oral TTR stabilizer, achieves ≥90% TTR stabilization and is approved in the USA, EU, Japan, and UK for adults with wild-type (ATTRwt-CM) or ATTRv-CM. In the phase 3 ATTRibute-CM study and its open-label extension (OLE), acoramidis reduced both the risk of all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) through Month 30 and risk of ACM through Month 42 by 36%. Research Question: Is acoramidis efficacy in ATTRv-CM consistent with that observed in the overall ATTRibute-CM population? Methods: In ATTRibute-CM, participants with ATTR-CM were randomized 2:1 to acoramidis HCl 800 mg or PBO, twice daily (BID) for 30 months. After Month 30, all participants enrolled in the OLE received acoramidis HCl 800 mg BID. Genotype (ATTRwt-CM or ATTRv-CM) was captured at randomization; efficacy was analyzed separately in ATTRv-CM. Time-to-event analyses used a stratified Cox model with terms for treatment, baseline 6MWT, genotype, genotype×treatment interaction, stratified by randomization NT-proBNP and eGFR levels. ACM was analyzed at Month 42 (ATTRibute-CM 30 months + OLE 12 months). Results: The ATTRv-CM group comprised 59 patients (39 acoramidis, 20 PBO). Baseline characteristics were mostly similar in both groups ( Table ). The three most common variants reported were p.V142I (n=35), p.I88L (n=7), and p.T80A (n=5). Compared with PBO, acoramidis reduced the risk of ACM/first CVH through Month 30 by 59% (acoramidis, 46.2%; PBO, 75.0%; HR 0.41; 95% CI 0.21–0.81; P =0.011; Fig 1 ) and ACM through Month 42 by 59% (continuous acoramidis, 30.8%; PBO to acoramidis, 60.0%; HR 0.41; 95% CI 0.19–0.93; P =0.032; Fig 2 ) , with Kaplan–Meier curves separating at Months 5 and 10, respectively. Conclusions: In ATTRibute-CM, acoramidis use was associated with clinical benefit in the ATTRv-CM group as demonstrated by a 59% risk reduction in both ACM/first CVH at Month 30, and ACM at Month 42 compared to PBO. These previously unreported findings support the hypothesis that near complete TTR stabilization observed experimentally with acoramidis across multiple TTR mutations translates into improvement in long-term outcomes. Further validation by individual TTR variant is warranted.

Introduction: Diastolic dysfunction is a hallmark of heart failure with preserved ejection fraction, yet echocardiographic grading is challenging in patients with hypertrophic or amyloid cardiomyopathies due to variable structural and functional remodeling. Consequently, there is a need for additional diagnostic tools to identify patients at increased risk of diastolic dysfunction and enhanced filling pressure. Research Question: What is the accuracy of an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to predict diastolic dysfunction in hypertrophic and amyloid cardiomyopathy? Methods: We included patients with diverse phenotypes of hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis and validated Mayo Clinic's AI-enabled ECG model to grade diastolic function (normal: grade 0/1; abnormal: grade 2/3). Echocardiographic analysis of systolic (LVEF) and diastolic function was performed within 14 days of ECG recording. Diastolic function was graded based on E/e’ (>14), left atrial volume index (>34 mL/m2), pulmonary venous atrial reversal flow duration (≥30 ms), and peak tricuspid regurgitation velocity (>2.8 m/s). Filling pressure was considered increased if ≥3 parameters were abnormal, inconclusive if 2 were abnomal, and normal if ≥3 were normal. Echocardiograms with severe mitral regurgitation were excluded. Results: We identified and matched 840 ECGs from non-obstructive HCM patients (n=125, age 58±19y, 70% male), 145 ECGs from obstructive HCM patients (n=29, age 56±18y, 66% male), 225 ECGs from apical HCM patients (n=33, age 66±14y, 61% male), and 382 ECGs from cardiac amyloidosis patients (n=49, age 70±13y, 57% male). The AI-enabled ECG model predicted diastolic dysfunction with an accuracy of 65% (non-obstructive HCM), 67% (obstructive HCM), 65% (apical HCM), and 75% (cardiac amyloidosis), with sensitivities of 94% and 97% for obstructive HCM and cardiac amyloidosis, respectively, and high positive and negative predictive values for cardiac amyloidosis. Systolic dysfunction (LVEF≤40%) was more common in cardiac amyloidosis (29%) than in all HCM patients (5%). Conclusion: The AI-enabled ECG model demonstrates moderate accuracy but high sensitivity for detecting diastolic dysfunction and increased filling pressure, especially in obstructive HCM and cardiac amyloidosis. The AI-enabled ECG is a useful, widely scalable, and low cost tool to identify patients at increased risk for diastolic dysfunction.

Introduction: Acoramidis, an oral transthyretin (TTR) stabilizer achieving near-complete (≥90%) TTR stabilization, is now approved in the USA, EU, Japan, and UK for the treatment of TTR amyloid cardiomyopathy (ATTR-CM). In the 30-month phase 3 ATTRibute-CM study, acoramidis reduced all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) vs placebo. Through Month 42 (M42) of the open-label extension (OLE; 30 months ATTRibute-CM + 12 months OLE), the clinical benefit of acoramidis was sustained, resulting in a 36% and 43% risk reduction in ACM and ACM/first CVH, respectively, compared with the placebo to acoramidis group Research Question: Are the long-term clinical benefits of acoramidis on ACM and ACM/first CVH in ATTR-CM over 42 months consistent across patient subgroups? Methods: ATTRibute-CM study design has been previously described. At the end of the 30-month double-blind period, patients enrolled into OLE continued acoramidis or switched from placebo to acoramidis; all patients received open-label acoramidis HCI 800 mg BID. Data cut was performed after 12 months in OLE. Subgroup analysis for ACM and ACM/first CVH through M42 were conducted for randomization stratification factors (genotype, NT-proBNP, and eGFR levels) and other pre-specified subgroups. ACM included death due to any cause, cardiac mechanical assist device placement, and heart transplant. CVH included CV hospitalizations (≥24 h) and urgent visits (<24 h) for decompensated heart failure requiring IV diuretics and was adjudicated by an independent Clinical Events Committee. Results: Overall, 389 patients enrolled in the OLE (263 continuous acoramidis and 126 placebo to acoramidis). The analyses of ACM/first CVH ( Figure 1 ) and ACM ( Figure 2 ) through M42 consistently favored patients initially randomized to acoramidis across all patient subgroups, including those based on sex, age, country, ATTR-CM genotype, baseline NT-proBNP, baseline eGFR, NYHA class, and NAC stage. The largest effect size for the reduction in ACM/first CVH (HR <0.50) was observed in the following subgroups: female, non-Caucasian patients, variant ATTR-CM, or NAC stage 2. Conclusions: The long-term benefit of acoramidis was consistently shown in patients initially randomized to acoramidis treatment compared with placebo to acoramidis switch after 30 months across multiple clinically relevant subgroups, underscoring the importance of early initiation of acoramidis to reduce the long-term risk of ACM or CVH.

Abstract Introduction The rapidly ageing population and multimorbidity are associated with increased emergency department (ED) visits by older adults. In the ED, older adults have higher risk of hospitalization, functional and cognitive decline, and mortality. Boarding, holding admitted patients in the ED awaiting a hospital bed, exacerbates these negative outcomes, which disproportionately affect older adults. Methods We conducted a cross-sectional analysis to investigate US boarding trends by age using clinical administrative data from 5 health systems and publicly available NHAMCS data from 2018 to 2024. Results Boarding ≥3 h in the ED has increased across diverse hospital types, with oldest adults (85+) facing the greatest risk (System 4: IRR [95% CI] = 1.18 [1.15-1.20], System 5: IRR [95% CI] = 1.20 [1.17-1.23], System 3 [Community Hospital]: IRR [95% CI] = 1.25 [1.19-1.33]). These results were recapitulated at the national level in NHAMCS (IRR [95% CI] = 1.30 [1.05-1.61]). Discussion The trend of increased boarding has serious implications for patients, caregivers, and health systems. The 2025 CMS Age-Friendly Hospital Measure offers opportunities to improve processes and procedures to mitigate the negative effects of hospital boarding on older patients. We highlight opportunities to address this challenge, including ongoing quality improvement initiatives, bed prioritization algorithms, and alternate admission pathways.

Abstract Background and Aims Accurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for echocardiography-based CA detection was assessed. Methods Utilizing a multisite, multiethnic dataset (n = 2612, 52% CA), a convolutional neural network was trained to differentiate CA from phenotypic controls using transthoracic apical four-chamber video clips. External validation was conducted globally across 18 sites including 597 CA cases and 2122 controls. Classification accuracy was assessed on the entire external validation dataset, and subgroup analyses were performed both on technetium pyrophosphate scintigraphy referrals, and individuals matched for age, sex, and wall thickness. Model accuracy was also compared with the transthyretin CA score and the increased wall thickness score within a subset of older heart failure with preserved ejection fraction patients with increased wall thickness. Results Cardiac amyloidosis patients and controls displayed similar age, sex, race, and comorbidities. After the removal of uncertain AI predictions (13%), model discrimination and classification were excellent for the entire external validation dataset [area under the receiver operating characteristic curve (AUROC) 0.93, sensitivity 85%, specificity 93%], irrespective of CA subtype (sensitivity: light-chain = 84%, wild-type transthyretin = 85%, and hereditary transthyretin = 86%). Performance was maintained in subgroup analysis in patients clinically referred for technetium pyrophosphate scintigraphy imaging (AUROC 0.86, sensitivity 77%, specificity 86%) and matched patients (AUROC 0.92, sensitivity 84%, specificity 91%). The AI model (AUROC 0.93) also outperformed transthyretin CA score (AUROC 0.73) and increased wall thickness (AUROC 0.80) scores. Conclusions This AI screening model—using only an apical four-chamber view—effectively differentiated CA from other causes of increased left ventricular wall thickness.

Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of CA and challenges that hinder adoption of these tools. Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2451 patients. Four studies used ECG data, 8 used echocardiography data, and 1 used both. The CA gold standard was typically defined as a CA diagnosis in an institutional or other database but the requirements for these diagnoses were heterogenous across studies, and many did not distinguish among CA subtypes. AI model development varied considerably, and only 4 studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 study reported model performance across strata of sex, age, race, and CA type. Persistent challenges to AI adoption include usability, cost, value added, electronic health record/information technology interoperability, patient‐related factors, regulation, and privacy and liability. Published studies on AI for improved identification of CA show favorable performance measures but numerous methodologic and other challenges must be addressed before these tools are more widely adopted.