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Migraine with aura(MwA) is associated with an increased risk of stroke and adverse vascular outcomes compared to those with migraine without aura (MwoA). AI-ECG prediction models developed at our institution can evaluate the probability of atrial fibrillation (AF) and estimate a patient’s age based on a normal sinus rhythm (NSR) ECG. Delta age, AI-ECG estimated age minus chronological age, indicates endothelial dysfunction and is associated with cardiovascular mortality. We aim to assess how AI-ECG prediction model outputs, specifically the AF probability and delta age, are associated with adverse vascular outcomes in patients with migraine. Adult patients diagnosed with MwA and MwoA from 2000-2020 with at least one digital, standard 12-lead ECG were identified. The first ECG that showed NSR is used as the index ECG. Patients with any adverse vascular outcomes before the index ECG were excluded. Adverse vascular outcomes used as endpoints include acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism, AF, and carotid artery dissection. A total of 31301 patients (13783 MwA, 17518 MwoA) were included in our analysis. The mean age at the time of the index ECG was 44.3 (14.5), and the average follow-up time was 71.6 (69.8) months. Based on the index ECG, the mean AF probability was significantly higher in MwA compared to MwoA (2.39 vs 2.06, p< 0.001), while the delta age was lower in MwA compared to MwoA (3.7 vs 4.2, p <.001). The prevalences of all vascular outcomes are summarized in Table 1. Multivariate Cox regression models showed that a higher AF prediction model output (every 10% increase), as well as a higher delta age (every 10-year increase), are associated with an increased risk of composite vascular outcomes (HR1.20, 95%CI 1.17-1.23, p<.001, and HR1.19, 95%CI 1.14-1.23, p < .001) [Table 2]. The Kaplan-Meier curve of adverse vascular outcome-free survival by AF prediction model output, using 1% as the cutoff (HR 1.49, 95% CI 1.41, 1.57, p<.001), and by delta age, using 2-year as the cutoff (HR 1.14, 95% CI 1.08, 1.21, p <. 001) are presented in Figure 1. Having aura, older age, male, coronary artery disease, congestive heart failure, diabetes, hypertension, and tobacco use were also associated with a higher risk of adverse vascular outcomes. Our results demonstrated the potential of using AI-ECG AF prediction model output and delta age to identify migraine patients at risk for adverse vascular outcomes.

Abstract Background Moderate-severe mitral annulus calcification (MAC) and left ventricular (LV) diastolic dysfunction are both linked to increased risks of heart failure and mortality. Assessing LV diastolic function (LVDF) in moderate-severe-MAC by echocardiography is frequently challenging and unreliable. We recently validated an AI-ECG model for LVDF. We aimed to determine if this tool can predict long-term mortality in patients with moderate-severe-MAC and whether it can help establish MAC-specific echocardiographic LVDF parameter cutoffs. Methods This retrospective study included all patients from the AI-ECG LVDF study test cohort who underwent comprehensive transthoracic echocardiography confirming moderate-severe-MAC and ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022. AI-ECG LVDF was determined based on the index ECG. Patients were categorized based on AI-ECG into groups representing normal/grade-1 LVDF, grade-2 LVDF, or grade-3 LVDG (DF-1, DF-2, and DF-3, respectively). Results Of 2,506 patients with moderate-severe-MAC [mean age 77, 64.1% females], 709 (28.3%), 1,510 (60.3%), and 287 (11.5%) were classified as DF-1, DF-2, and DF-3, respectively. Worse LVDF grade was associated with older age, male sex, lower LV ejection fraction, and higher rates of noncardiovascular and cardiovascular comorbidities. Over a median follow-up of 2.8 years, 1,059 (42.3%) patients died. In multivariable survival analysis, DF-2 [adjusted hazard ratio (aHR) 1.43, 95% confidence interval (95%CI) 1.21-1.68] and DF-3 (aHR 1.54, 95%CI 1.22-1.93) were independently associated with a higher risk of death (figure). Findings were also consistent after adjusting to mitral inflow gradients. When comparing LVDF echocardiographic parameters in controls without MAC matched by age, sex, left ventricular ejection fraction, and AI-ECG DF grade, significant differences were observed. Patients with moderate to severe MAC generally exhibited lower e' velocities, higher TR velocities, elevated E/e' ratios, and increased LAVi. Interestingly, the E/A ratio between MAC patients and their matched controls across AI-ECG LVDF grades remained consistent. Conclusion AI-ECG LVDF predicts mortality risk in patients with moderate-severe-MAC, regardless of mitral inflow gradients. Utilizing AI-ECG LVDF might aid in the risk stratification of patients with significant MAC.

826 Background: After initial response to first line therapy with imatinib, GISTs commonly progress due to secondary resistance mutations in KIT. As the KIT mutation targeting profiles of bezuclastinib (type I TKI) and sunitinib are distinct, when combined they inhibit a broad spectrum of secondary KIT mutations. Herein we report extended experience of patients treated with 2 nd line combination bezuclastinib and sunitinib, including response assessment and safety. Methods: Peak (NCT05208047), a global randomized Phase 3, open-label study, aims to evaluate efficacy and safety of bezuclastinib + sunitinib vs sunitinib in GIST pts with imatinib intolerance or resistance. In Part 1a of the 3-part study, the dose of an optimized formulation of bezuclastinib was escalated in serial cohorts to achieve a target exposure comparable to that achieved at the RP2D established in the prior Phase 1b/2a study. Part 1b evaluated the interaction between bezuclastinib + sunitinib. Key inclusion: adult with locally advanced, metastatic and/or unresectable GIST, ≥1 measurable lesion according to modified RECIST v1.1, and ECOG PS 0 to 2. Part 1 completed enrollment in Apr 2023. Based upon PK and safety, a dose of bezuclastinib 600 mg QD + sunitinib 37.5 mg QD was selected for Part 2. All Part 1 data reported herein are as of Aug 2023; updated safety and efficacy will be presented. Results: Part 1 has completed enrollment with 19 pts in Part 1a and 23 in 1b. In Part 1a, Cohort 1 included 5 pts starting at bezuclastinib 300 mg QD + sunitinib 37.5 mg QD; Cohort 2 included 14 pts at bezuclastinib 600 mg QD + sunitinib 37.5 mg QD. Pt characteristics in Part 1a+1b: median age - 60 yrs (range: 33-77); 81% men; 98% ECOG PS 0-1; 98% metastatic and 2% locally advanced. The majority of TEAEs were low grade and reversible with low rate (38%) of Grade 3+ events. There were limited (24%) dose reductions and infrequent (n=2) discontinuations due to TEAEs. Three pts experienced serious AEs possibly associated with study medications. In pts evaluable for response (received ≥1 cycle of study treatment and had assessments at baseline and post-baseline [n=40]), the objective response rate (ORR) was 20% (6 confirmed PRs, 2 unconfirmed PRs). Data remain immature to estimate median PFS for Part 1 pts. In a subset of pts with prior imatinib only (n=6), the closest approximation for pts enrolling in Part 2, the ORR was 33% (2 confirmed PRs). The majority of 2nd-line pts remain on treatment past 12 months. Conclusions: Data from Peak Part 1 show an encouraging safety and tolerability profile generally consistent with published sunitinib monotherapy experience. ORR in evaluable pts from Part 1 was 20%; ORR in 2 nd line pts was 33%. Part 2 of the Peak study is actively enrolling pts globally at the selected dose of bezuclastinib 600 mg QD + sunitinib 37.5 mg QD versus sunitinib 37.5 mg QD. Clinical trial information: NCT05208047 .

566 Background: Immune checkpoint inhibitors (ICIs) have become the preferred treatment for unresectable hepatocellular carcinoma (HCC). Conflicting data exist regarding the prognostic value of immune-related adverse events (irAEs) in these patients. We aimed to explore potential the association between irAEs and prognosis in HCC patients treated with ICIs. Methods: We searched PubMed, Scopus, Web of Science, and CENTRAL databases for articles published from inception to June 2024 using keywords including ICI, HCC, and irAEs. Two reviewers independently screened studies for eligibility and relevant data. A random effects model was used for statistical analysis, and subgroup analysis was performed by the grade of irAEs. Results: Of 3,028 studies, 25 (n=4,116 patients) met criteria for inclusion. Atezolizumab plus bevacizumab was the most common treatment regimen (n=10 studies). About half (49.5%) of patients experienced irAEs. irAEs were associated with increased objective response rate (ORR) (pooled OR: 1.72; 95% CI: 1.36 – 2.19, I 2 =39%), higher complete response rate (pooled OR: 1.45; 95% CI: 1.21-1.74, I 2 =74%), and longer progression-free survival (PFS) (pooled HR: 0.66; 95% CI: 0.52-0.84, I 2 =71%). Although irAEs were associated with longer overall survival, this difference was not statistically significant (pooled HR: 0.83; 95% CI: 0.62 – 1.11, I 2 =73%). Subgroup analysis showed a survival benefit for patients with grade 1-2 irAEs (pooled HR: 0.50; 95% CI: 0.36-0.67, I 2 =0%) but not for those with grade 3-4 irAEs (pooled HR: 0.95; 95% CI: 0.24-3.72, I 2 =84%). Conclusions: The development of irAEs is associated with a favorable prognosis in HCC, including improved PFS and higher ORR. A survival benefit was noted in patients with mild irAEs, but not in those with severe irAEs. Future studies are needed to determine if a differential survival benefit relates to the continuation vs. discontinuation of ICI therapy.

AbstractBackgroundThere is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease. However, the association between EKG‐based age and underlying neuropathology has not been studied.MethodWe used a cohort of 336 participants from the Mayo Clinic Study of Aging, a population‐based prospective study of residents in Olmsted County, Minnesota, with antemortem EKG and post‐mortem brain autopsy (Table 1 & 2). Age was predicted from EKG using a validated convolutional neural network algorithm. We used ordinal logistic regression to identify associations between lower neuropathologic scores of AD (Thal phase, neuritic plaque score, Braak stage, and overall AD neuropathologic change) and the Age‐Gap, defined as EKG‐predicted age minus chronological age. Results were controlled for true age, sex, and APOE e4 carrier status. Neuropathologic scales were grouped into none, low, intermediate, and high categories. Observations were weighted by the inverse of years between last EKG and time of death to account for the time difference between acquisition of EKG and autopsy.ResultWe found that an increasing Age‐Gap (i.e. older predicted age) was inversely associated with lower overall AD neuropathologic change (OR 0.93 [0.89, 0.97], p = 0.002). Investigation of the components of AD neuropathologic change showed that an increasing Age‐Gap was also inversely associated with lower amyloid metrics such as Thal phase (OR 0.85 [0.80, 0.89], p < 0.001) and neuritic plaque scores (OR 0.91 [0.87, 0.96], p < 0.001), but not tau metrics like Braak staging (OR 1.05 [1.00, 1.10], p = 0.05).ConclusionIn this work, we show that older predicted age on EKG, a noninvasive biomarker associated with vascular and biological aging, is associated with higher amyloid burden and neuropathologic change of AD.

Background: Follicular dendritic cell sarcoma (FDCS) is a rare low- to intermediate-grade sarcoma, typically presenting as a slowly growing, painless mass composed of spindle-shaped cells in a whorled pattern. First described in 1986, FDCS has since been identified in several hundred cases. FDCS originates from mesenchymal dendritic cells and is distinct from other histiocytic neoplasms. Its management aligns more with soft tissue sarcomas. Epidemiologically, FDCS is exceedingly rare, constituting less than 0.4% of soft tissue sarcomas. Methods: With IRB approval, we identified patients with biopsy-proven FDCS treated at Mayo Clinic. Chart review extracted clinical characteristics, laboratory testing, treatment data, and clinical outcomes, compiled into Excel. Data analysis was performed through RStudios. Descriptive statistics of patient characteristics, such as gender and stage of disease, were summarized in tables. We compared overall survival (OS) through Kaplan Meier curves, stratifying patients into cohorts based on topics of interest, such as differences in survival in nodal vs. extranodal disease or differences in survival among different chemotherapy regimens. Results: Forty patients met the inclusion criteria, of biopsy proved FCDS. Median age at diagnosis was 57.7 years (SD 14). Men comprised 45% of patients. Locations of malignancy were abdomen and pelvis (32.5%), thoracic and pulmonary regions (32.5%), lymph nodes and neck (20.0%), musculoskeletal (2.5%), and other locations (12.5%). Primary therapies included surgical resection (42.5%), systemic chemotherapy (32.5%), no treatment (10.0%), radiation (2.5%), with data missing for 12.5% of cases. Concurrent or preceding Castleman's Disease was seen in 7.5% (3/40) of patients; one case preceded FCDS, and two were concurrent. Of these patients with Castleman's Disease, one was resected and the other two were not. Two cases were unicentric, and one was multicentric. First-line systemic chemotherapy was most often gemcitabine and docetaxel, with 9/40 patients receiving this regimen. Ifosfamide and doxorubicin were utilized in 3/40 patients. 2/4 patients had received doxorubicin alone. 2/4 patients had received CHOP. The response rate for patients with gemcitabine and docetaxel was complete response for 2/9 (22%) patients, partial response for 2/9 (22%), stable disease for 2/9 (22%), progressive disease for 1/9 (11%), and missing for 2/9 (22%), giving an overall response rate of 67%. For ifosfamide and doxorubicin, one patient had partial response (33%), another had stable disease (33%), and the last patient's data was missing (33%), adding up to a 66% overall response rate. For CHOP, one patient had progressive disease, and the other had partial response. For doxorubicin, both patients had stable disease. At a median follow-up of 29.5 months (IQR 9.2-75.2 months), the median OS was 160.68 months for the group with both nodal and extranodal disease versus 39.95 months for the extranodal disease-only group. The median OS of the nodal disease alone was not reached, with an estimated 2-year OS of 75%. To evaluate the impact of various primary therapies on outcomes in patients with advanced disease, we conducted a survival analysis by therapy used (no treatment, radiation, surgical resection, and systemic chemotherapy). Patients receiving no treatment had a median survival time of 28.9 months, while those treated with radiation had a median survival time of 19.3 months. Patients undergoing surgical resection had an undetermined median survival time, but notably, 80% were alive at the end of the time period analyzed in this study with a median follow-up time of 60.7 months. Those receiving systemic chemotherapy had a median survival time of 30.7 months. Conclusions: This project is one of the largest known single-center studies dedicated to FDCS. We show that the incidence of preceding or concurrent Castleman's Disease is relatively low. Gemcitabine and docetaxel was the most utilized chemotherapy regimen, but there weren't enough data to conclude if this regimen had a better response rate compared to others. Surgical resection of advanced disease is associated with superior outcomes. When systemic therapy is needed, the median survival was lower.

Abstract Background and Aims Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk. Methods An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap &lt; 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed. Results The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), −.1 (6.0), 4.7 (8.7), and −1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24–2.78), 1.89 (1.46–2.43), 1.90 (1.55–2.33), and 1.76 (1.67–1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47–3.37), 1.94 (1.39–2.70), 1.58 (1.06–2.35), and 1.79 (1.62–1.97) in these cohorts compared with the normal group. Conclusions The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.

Abstract Aims The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from ‘Findings’ to ‘Impressions.’ Against cardiologist-generated Impressions, the models’ performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (P &lt; 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores vs. clinical utility (r = 0.42) and automatic metrics showed insensitivity (0–5% drop) to changes in measurement numbers. Conclusion EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

Background: We previously developed deep-learning algorithms that identify coronary artery disease (CAD) risk based on (i) coronary artery calcium (CAC), (ii) obstructive CAD by angiography, and (iii) left ventricular akinesis in ≥1 segment by echocardiogram, using a 12-lead electrocardiogram (CAD ECG-AI). We tested the hypothesis that those with increased probability of CAD by CAD ECG-AI algorithms will have an increased risk of atherosclerotic cardiovascular disease (ASCVD) events and would refine the AHA Predicting Risk of Cardiovascular Disease Events (PREVENT TM ) equations’ predictive capabilities. Methods: We assessed a group of consecutive patients who sought primary care in Olmsted County, MN, between 1997 and 2003. Passive follow-up was conducted using the Rochester Epidemiology Project's record linkage system. Patients included met the same criteria of the original PREVENT TM equations. The probability output of each CAD ECG-AI algorithm was used to predict ASCVD (fatal and non-fatal myocardial infarction and ischemic stroke) and ASCVD-Plus [further including percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and all-cause mortality]. Events were validated in duplicate. Cox proportional hazard models adjusted for age, sex, and risk factors while modeling each CAD ECG-AI algorithm as an individual and additive predictor (see footnote). PREVENT TM risk categories stratified analysis to evaluate the effect of the CAD ECG-AI models on predicted ASCVD risk. Results: We included 21,193 patients, with mean ± SD age 51.6 ± 12.0, 54% women and 95% white. After 14.2±12.4 years follow-up, 1,351 (6.4%) developed ASCVD, and 2,808 (13.2%) had ASCVD-Plus. The risk of ASCVD and ASCVD-Plus increased with positivity for the CAD ECG-AI algorithms after adjustments for age, sex, and PREVENT TM equations factors, all p for trend <0.001 (data not shown). In additive analyses, the risk of ASCVD and ASCVD-Plus increased with increased CAD ECG-AI factors, all p for trend <0.001 ( Fig. A-B ). Furthermore, the CAD ECG-AI algorithm enhanced the predictive capabilities of PREVENT TM equations across most risk subgroups ( Fig. C-E ). Conclusions: The deep-learning CAD ECG-AI algorithms displayed an independent and additive association with long-term ASCVD and ASCVD-Plus events in the community and improved the PREVENT TM predicted ASCVD risk. The CAD ECG-AI algorithms could help identify individuals at risk in primary prevention of cardiovascular events.