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e24024 Background: Patients with active cancer undergoing trans catheter aortic valvular replacement (TAVR) face a higher risk of adverse outcomes due to the interplay between cancer and cardiovascular disease. Although TAVR is increasingly used in high-risk populations, data on outcomes for these patients remain limited. This systematic review and meta-analysis aim to evaluate the clinical outcomes associated with TAVR in patients with active cancer compared to non-cancer controls, providing insights to inform clinical decision-making in this unique group. Methods: A systematic search of major databases (PubMed, Embase, OVID, Web of Science, Scopus) was conducted to identify studies comparing clinical outcomes of TAVR between patients with active cancer and those without cancer. Key outcomes included measures of mortality, procedural complications, and post-procedure adverse events. The Mantel-Haenszel method was used for pooled analysis, applying random-effects or fixed-effects modeling based on heterogeneity assessed by Higgins I². Statistical significance was set at a p-value < 0.05. Results: Twelve studies involving 5,73,192 patients (67,684 active cancer, 505508 control) were included. Patients with active cancer undergoing TAVR have significantly higher risks of short-term mortality (OR 1.31, 95% CI [1.15, 1.49], p < 0.0001), long-term mortality (OR 2.24, 95% CI [1.85, 2.70], p < 0.0001), and major bleeding complications (OR 1.80, 95% CI [1.68, 1.92], p < 0.0001). There were no statistically significant differences in cardiovascular mortality (OR 1.33, 95% CI [0.94, 1.89], p = 0.11), myocardial infarction (OR 1.60, 95% CI [0.82, 3.11], p = 0.17), stroke (OR 0.61, 95% CI [0.28, 1.35], p = 0.22), AKI (OR 0.92, 95% CI [0.49, 1.73], p = 0.80), or the need for permanent pacemaker implantation (OR 1.12, 95% CI [0.85, 1.48], p = 0.42). Conclusions: Patients with active cancer undergoing TAVR face significantly higher risks of mortality and major bleeding compared to non-cancer controls. However, the risks of cardiovascular mortality, MI, stroke, AKI, and the need for permanent pacemaker implantation are comparable to those of non-cancer controls. Careful risk assessment and management are crucial for optimizing outcomes in this vulnerable population, and further studies are needed to refine treatment strategies. Odds ratios of all outcomes. Outcomes OR Confidence Interval (95% CI) p-value Short term mortality 1.31 [1.15, 1.49] < 0.0001 Long term mortality 2.24 [1.85, 2.70] < 0.00001 Major bleeding 1.8 [1.68, 1.92] < 0.00001 Cardiovascular mortality 1.33 [0.94, 1.89] 0.11 Myocardial Infarction 1.6 [0.82, 3.11] 0.17 Stroke 0.61 [0.28, 1.35] 0.22 Acute Kidney Injury 0.92 [0.49, 1.73] 0.8 Need for permanent pacemaker implantation 1.12 [0.85, 1.48] 0.42

e13637 Background: Large language models (LLMs) like GPT-4 (OpenAI) and Claude Opus (Anthropic) showed high accuracy in medical multiple-choice exams, but data on their oncology-specific clinical reasoning and performance is limited. This study evaluates their accuracy and clinical reasoning on breast oncology multiple-choice questions (MCQs) from the American Society of Clinical Oncology (ASCO) question bank. Methods: Using OpenAI and Anthropic Application Programming Interface (APIs), questions were tested without additional prompts under consistent settings (GPT-4 and Claude Opus; Temperature = 0, Tokens = Max). Each question was tested three times to assess precision. Then, Chain-of-thought (COT) prompting was applied to promote LLMs stepwise reasoning to increase their accuracy. Accuracy before and after COT prompting was compared. Incorrect responses were reviewed by board-certified medical oncologists speclized in breast cancer, who scored reasoning clarity, bias, and clinical relevance. Qualitative feedback was descriptively analyzed. Results: A total of 273 breast oncology MCQs were evaluated across the two LLMs. GPT-4 achieved an initial accuracy of 81.3% (222/273; 95% CI: 76.3%-85.5%), compared to Claude Opus, which achieved an accuracy of 79.5% (217/273; 95% CI: 74.3%-83.9%). The Chi-squared test for difference between the models before chain-of-thought (COT) prompting yielded a p-value of 0.59, indicating no statistically significant difference in accuracy between GPT-4 and Claude Opus prior to COT prompting. After COT prompting, the performance of the two models diverged significantly. GPT-4 saw a net decline in accuracy, decreasing by a net of 1 correct answer, resulting in an overall accuracy of 80.95% (221/273). In contrast, Claude Opus experienced a notable improvement, with a net of 19 additional correct answers, leading to an accuracy of 86.4% (236/273). A statistical analysis using a Chi-squared test revealed a difference in accuracy between the two models of 5.5% (p = 0.08), demonstrating that the improvement in accuracy for Claude Opus after COT prompting was borderline statistically significant compared to GPT-4. Thematic analysis of oncologists' feedback revealed that the most common reasons for incorrect answers were reliance on outdated guidelines, misinterpretation of clinical trial data, and failure to consider multidisciplinary or patient-specific approaches in clinical decision-making. Conclusions: Although AI models can achieve high scores on multiple-choice exams, they still require human supervision. These models rely on potentially outdated training data and lack the ability to individualize patient care or apply data from clinical trials. especially in unique or unconventional/non textbook scenarios.

e16072 Background: Gastric cancer is a leading cause of mortality worldwide, with pronounced variations in outcomes across populations. This study investigates U.S. trends in age-adjusted mortality rates (AAMRs) for gastric cancer from 1999 to 2023, examining differences by gender, race, region, and urbanization to uncover critical patterns and inequities. Methods: Data were extracted from the CDC WONDER database using ICD-10 code C16 to identify gastric cancer-related deaths in adults aged 25 and older. AAMRs per 1,000,000 population were calculated and stratified by year, gender, race/ethnicity, geographic region, and urban-rural classification. Trends were analyzed using Joinpoint regression to determine annual percentage changes (APC) and 95% confidence intervals (CI). Statistical significance was set at p < 0.05. Results: From 1999 to 2023, there were 312,300 gastric cancer deaths (185,391 men, 126,909 women). Overall, AAMRs declined consistently, with APCs of -3.4 for the period 1999 to 2007, -2.2 for 2007 to 2018, and -1.2 for 2018 to 2023. Men showed a more pronounced decline, with APCs of -3.6 for 1999 to 2009 and -2.2 for 2009 to 2023, compared to women, who had APCs of -3.1 for 1999 to 2008, -1.9 for 2008 to 2018, and -0.8 for 2018 to 2023. Non-Hispanic Black populations experienced a decline in AAMR with an APC of -3.3 for 1999 to 2020, while Hispanic/Latino and Asian/Pacific Islander populations showed declines with APCs of -2.8 for 1999 to 2011 and -3.8 for 1999 to 2020, respectively. Metropolitan areas saw AAMR reductions with APCs of -3.3 for 1999 to 2008 and -2.2 for 2008 to 2020, while non-metropolitan areas had larger initial declines with an APC of -3.6 for 1999 to 2006 but slower decreases with an APC of -2.0 for 2006 to 2020. Geographically, the Northeast had the highest AAMR (62.9), followed by the West (61.7), South (57.4), and Midwest (50.9). Hawaii recorded the highest state-level AAMR (96.5), while Wyoming had the lowest (36.6). States in the top 90th percentile included Alaska, California, and New York, whereas Utah, Idaho, and Iowa were among the lowest. Conclusions: Gastric cancer mortality in the U.S. has declined over the past two decades, but the pace of reduction has slowed in recent years. Significant disparities persist across gender, racial, and geographic groups, underscoring the need for targeted interventions to address these inequities and improve outcomes in vulnerable populations.

e16001 Background: Esophageal carcinoma ranks among the top 10 malignancies in the world, contributing to high morbidity and mortality. With radiotherapy being one of the most administered treatment options, different variations of administration have evolved. Two such variations include intensity-modulated radiotherapy and 3D conformational radiotherapy. These modalities have been investigated for their efficacy and rate of adverse events. However, conclusions from the trials conducted have been variable. In this systematic review and meta-analysis, we aim to collate these findings to assess the exposure of cancerous cells and rates of adverse events following IMRT or 3D-CRT. Methods: A comprehensive literature search was conducted across three databases for all articles published from inception till September 2024. All primary studies evaluating the comparative efficacy of IMRT vs 3D-CRT were included. Outcomes assessed include dose volume histograms (DVHs) at heart and lung, Dmax at the spinal cord, and irradiation toxicity. Data analysis was conducted in RevMan 5.4 using a random-effects model. Results: A total of twenty records were included in the study, with a sample size of 3090 patients. Radiation dosage, estimated as DVHs, was equal across both regimens at heart (MD: 2.67; 95%CI: –0.15 to 5.48; P = 0.06), except at 40 Gy of radiation, where a lower DVH was noted among patients being administered IMRT (MD: 6.64; 95%CI: 0.25 to 13.03; P = 0.04). At the lung, a higher DVH was noted, especially for doses of 20 Gy or above, on using IMRT. Similarly, a lower dosage in the spinal cord was noted in the IMRT group (MD: 2.87; 95%CI: 1.04 to 4.70; P = .002). Consequently, a lower risk of esophagitis was noted among the IMRT group (OR: 1.54; 95%CI: 1.14 to 2.07; p = 0.009]). While considering patient outcomes, the odds of survival were higher in the IMRT group at 3 years post-treatment (OR: 0.53; 95%CI: 0.31 to 0.92; P = 0.02). Conclusions: IMRT was superior to 3D-CRT in terms of volume of tissue irradiated and lower spinal toxicity. A higher chance of survival was noted in the IMRT group. Further trials are needed to strengthen these findings. Outcome Mean Difference (MD) 95% confidence interval (CI) P value Mean dosage at lung 1.14 0.6, 1.68 <0.0001 Mean dosage at heart 2.67 -0.15, 5.48 0.06 Radiation esophagitis Odds ratio, OR: 1.38 1.09, 1.76 0.009 Radiation pneumonitis OR:1.37 0.62, 3.01 0.43 3-year OS OR: 0.69 0.56, 0.84 0.0003 5-year OS OR: 0.53 0.31, 0.92 0.02 Maximum dosage at spinal cord 2.87 1.04, 4.7 0.0002

e23101 Background: Liver cell carcinoma (LCC) and hepatic failure (HF) are major contributors to global adult mortality. Identifying trends and disparities in LCC and HF-related deaths is critical for addressing public health challenges and guiding prevention strategies. This study examines national trends in LCC and HF-related mortality from 1999 to 2023, with an emphasis on demographic, geographic, and temporal disparities in age-adjusted mortality rates (AAMRs). Methods: Using the CDC WONDER database (1999–2023), this descriptive study identified deaths attributed to LCC (ICD-10 code C22.0) and HF (ICD-10 code K72.9) among adults aged 25 and older. Mortality data were stratified by gender, race/ethnicity, census region, urban-rural classification, and state. Age-adjusted mortality rates (AAMRs) per 1,000,000 individuals were calculated, and trends were analyzed using Joinpoint regression to determine annual percent changes (APCs) with 95% confidence intervals (CIs). Statistical significance was set at p < 0.05, and the study followed STROBE guidelines. Results: From 1999 to 2023, there were 22,963 deaths attributed to LCC and HF (Men: 17,822; Women: 5,141). Overall, AAMRs declined from 1999 to 2007 (APC: -2.4), stabilized between 2007 and 2017 (APC: 0.2), and declined again from 2017 to 2023 (APC: -3.5). Men experienced a steady AAMR decline from 1999 to 2010 (APC: -2.1), followed by stabilization (2010–2017, APC: 0.7), and further decline (2017–2023, APC: -4.4). Women showed a sharp AAMR decrease from 1999 to 2006 (APC: -3.8) and a relatively stable trend thereafter (APC: -0.1). Non-Hispanic Blacks people experienced an overall AAMR decline from 1999 to 2020 (APC: -0.9). Among Non-Hispanic Whites people, AAMRs decreased initially (1999–2006, APC: -2.6), rose slightly (2006–2018, APC: 0.5), and declined further (2018–2020, APC: -7.3). Regional AAMRs varied, with the West having the highest overall AAMR (5.6; 95% CI: 5.4–5.7) and the Midwest the lowest (3.2; 95% CI: 3.1–3.3). Hawaii reported the highest state AAMR (10.3; 95% CI: 8.9–11.6), while Arkansas had the lowest (2.4; 95% CI: 2.0–2.9). Urban-rural disparities revealed a sharper AAMR decline in metropolitan areas (1999–2005, APC: -3.9) and the non-metropolitan areas showed a slight increase (1999–2020, APC: 1.3). Conclusions: LCC and HF-related mortality rates have shown significant declines over the past two decades, with variations across gender, race, and geographic locations. Men and urban populations experienced steeper reductions in AAMRs, while rural populations exhibited slower improvements or slight increases in mortality. These findings highlight the need for targeted public health interventions to address disparities and further reduce LCC and HF-related mortality in vulnerable populations.

e16237 Background: Alcohol use (AU) and Liver cell carcinoma (LCC) are significant contributors to adult mortality worldwide. Examining trends and disparities in AU and LCC-related deaths is essential for understanding public health challenges and formulating targeted interventions. This study investigates national mortality patterns related to AU and LCC in the United States from 1999 to 2023, emphasizing demographic, geographic, and temporal disparities. Methods: This descriptive analysis used the CDC WONDER database (1999–2023) to identify AU-related deaths (ICD-10 codes E24.4, F10, G32.2, G62.1, G72.1, I42.6, K70, K85.2, K86.0, R78.0, T51, X65, Y15) and LCC (ICD-10 code C22.0) in adults aged 25 and older. Age-adjusted mortality rates (AAMRs) per 1,000,000 individuals were calculated, stratified by gender, race/ethnicity, census region, urban-rural classification, and state. Trends were analyzed using Joinpoint regression to compute annual percent changes (APCs) and 95% confidence intervals (CIs). Statistical significance was set at p < 0.05, and analyses adhered to STROBE guidelines. Results: From 1999 to 2023, a total of 21,007 deaths were attributed to LCC and AU (Men: 18,836 Women: 2,171). AAMRs showed a stable trend from 1999 to 2003 (APC: 0.0; 95% CI: -8.7 to 4.9) but increased significantly from 2003 to 2019 (APC: 6.1; 95% CI: 5.7 to 7.4), stabilizing from 2019 to 2023 (APC: -0.6; 95% CI: -3.2 to 1.3). Men's AAMRs decreased from 1999 to 2003 (APC: -1.1; 95% CI: -10.4 to 3.4), then rose sharply from 2003 to 2019 (APC: 6.1; 95% CI: 5.6 to 7.1). For women, AAMRs increased significantly, particularly between 2013 and 2017 (APC: 12.4; 95% CI: -9.5 to 25.1). Non-Hispanic Black and White populations experienced notable increases in AAMRs, while Hispanic people showed an initial decline followed by an increase post-2004 (APC: 4.7; 95% CI: 3.7 to 7.9). The West had the highest regional AAMR (5.3; 95% CI: 5.2 to 5.5), while the Northeast recorded the lowest (2.1; 95% CI: 2.0 to 2.2). New Mexico had the highest state AAMR (8.4; 95% CI: 7.4 to 9.4), and Mississippi the lowest (1.2; 95% CI: 0.9 to 1.5). AAMRs rose more sharply in non-metropolitan areas from 1999-2020 (APC: 6.6; 95% CI: 5.9 to 7.8) compared to metropolitan regions from 2004 to 2020 (APC: 5.4; 95% CI: 4.9 to 6.5). Conclusions: AU and LCC-related mortality rates have increased significantly since 2003, with distinct gender, racial, and geographic disparities. Men and rural populations faced higher mortality increases, with notable variations across states and regions. These findings underscore the need for focused public health efforts to mitigate these disparities and address the growing burden of LCC and AU-related mortality.

Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus on Multimodal Large Language Models (MLLMs). We evaluated their ability to analyze facial skin quality, volume, symmetry, and adherence to aesthetic standards such as neoclassical facial canons and the golden ratio. Methods: We evaluated four MLLMs—ChatGPT-4o, ChatGPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet—using two evaluation forms and 15 diverse facial images generated by a Generative Adversarial Network (GAN). The general analysis form evaluated qualitative skin features (texture, type, thickness, wrinkling, photoaging, and overall symmetry). The facial ratios form assessed quantitative structural proportions, including division into equal fifths, adherence to the rule of thirds, and compatibility with the golden ratio. MLLM assessments were compared with evaluations from a plastic surgeon and manual measurements of facial ratios. Results: The MLLMs showed promise in analyzing qualitative features, but they struggled with precise quantitative measurements of facial ratios. Mean accuracy for general analysis were ChatGPT-4o (0.61 ± 0.49), Gemini 1.5 Pro (0.60 ± 0.49), ChatGPT-4 (0.57 ± 0.50), and Claude 3.5 Sonnet (0.52 ± 0.50). In facial ratio assessments, scores were lower, with Gemini 1.5 Pro achieving the highest mean accuracy (0.39 ± 0.49). Inter-rater reliability, based on Cohen’s Kappa values, ranged from poor to high for qualitative assessments (κ > 0.7 for some questions) but was generally poor (near or below zero) for quantitative assessments. Conclusions: Current general purpose MLLMs are not yet ready to replace manual clinical assessments but may assist in general facial feature analysis. These findings are based on testing models not specifically trained for facial analysis and serve to raise awareness among clinicians regarding the current capabilities and inherent limitations of readily available MLLMs in this specialized domain. This limitation may stem from challenges with spatial reasoning and fine-grained detail extraction, which are inherent limitations of current MLLMs. Future research should focus on enhancing the numerical accuracy and reliability of MLLMs for broader application in plastic surgery, potentially through improved training methods and integration with other AI technologies such as specialized computer vision algorithms for precise landmark detection and measurement.

Background/objective: HCM is a structural disorder of the myocardium that leads to sudden cardiac death in young adults. We synthesized an updated understanding of the role of Cardiac Myosin Inhibitors (CMIs) in HCM by pooling data from RCTs. Methods: We identified six published RCTs, involving 826 participants. Data were extracted pertaining to study characteristics; primary outcomes of interest–(1) change from baseline in resting left ventricular outflow tract (LVOT) peak gradient, (2) change from baseline in Valsalva LVOT peak gradient, and (3) improvement of ≥1 NYHA class–and secondary outcomes. These were pooled using Review Manager 5.4, employing a random-effects model, and reported as odds ratios (ORs) or mean differences (MDs). Results: We found statistically significant between-group difference favoring CMIs in change from baseline in LVOT peak gradient: at rest (MD −39.33; −53.01 to −25.64), post-Valsalva (MD −48.99; −53.96 to −44.03), and post-exercise (MD −37.11; −44.34 to −29.87); ≥1 NYHA class improvement (OR 4.10; 2.79–6.02), change from baseline in peak oxygen uptake (MD −37.11; −44.34 to −29.87), LVOT gradient ≤30 mm hg (RR 14.89; 7.47–29.67), participants eligible for septal reduction therapy (RR 0.26; 0.18–0.36), and change from baseline in KCCQ-CSS score (MD 8.54; 5.36–11.71). Subgrouping by intervention type (mavacamten vs. aficamten) revealed non-significant results for all primary outcomes. Conclusions: CMIs can contribute to improving key efficacy outcomes for patients with HCM while reducing incidence of SRT.