Citations

Klippel–Trenaunay (KT) syndrome and Cardiac Sarcoidosis (CS) are two distinct medical conditions that rarely coexist, each presenting unique challenges in diagnosis and management. Here, we present a rare case of a 30-year-old male with a history of KT syndrome complicated by recurrent deep vein thrombosis, who presented with symptoms of acute heart failure including shortness of breath, fatigue, dizziness, palpitations, and chest pain and was subsequently diagnosed with isolated CS. We discuss the importance of thorough clinical evaluation and multimodal diagnostic approaches in this complex scenario with overlapping symptoms and diagnostic dilemmas.

7522 Background: Movement and neurocognitive treatment emergent adverse events (MNTs), cranial nerve palsies (CNPs) and immune-effector cell enterocolitis (IEC-EC) are late onset toxicities associated with cilta-cel and are associated with significant morbidity and mortality. We aimed to identify predisposing risk factors to allow for formulating risk-reduction strategies. Methods: Retrospective study of MM pts who received FDA approved cilta-cel at Mayo Clinic between Feb 2022-Dec 2024. Interrogated factors included demographics, disease and treatment characteristics, and selected lab values at different time points. In odds ratio (OR) analyses, pts were divided by toxicity type (1) No delayed toxicity (2) MNT (3) IEC-EC without MNT (4) isolated CNP. Differences between pts in the MNT, IEC-EC and CNP groups were compared separately in a pairwise manner vs. those with no delayed toxicity. Results: Of 235 pts, 26 (11%) had delayed toxicity [MNT = 8 (3%), IEC-EC = 9 (4%), CNP = 14 (6%)]; 3 pts had both MNT/IEC-EC and 2 had both CNP/IEC-EC. Median onset from CAR-T infusion for MNT, IEC-EC and CNP was 19 (range 8-96), 106 (35-169) and 21 (7-43) days respectively. Of 164 evaluable pts, those who had ICANS had increased odds for any delayed toxicity (OR 4.7; 95% CI 1.6-13.8). Pts who had ICANS (OR 7.7; 1.6-36.9), HLH/MAS (OR 6.2; 1.1-6.7) and Ferritin >400mcg/L at time of lymphodepletion (OR 6.3; 1.5-32) had increased odds for MNTs. Pts who received alkylator-based bridging therapy had increased odds (OR 6.1; 1.1-35) for IEC-EC. Analysis of serial post-CAR infusion lab values showed that peak ALC was the most significant variable with differences between groups shown in table. Median time to peak ALC was 12 days (IQR 11-13). ROC analysis determined peak ALC >3 x 10 9 /L as a meaningful cut point. Pts with peak ALC >3.0 x 10 9 /L had increased odds of developing any delayed toxicity (OR 9.3; 3.5-29.3), MNT and CNP (table). Absolute risk of any delayed toxicity was 33% in pts with peak ALC >3 x 10 9 /L vs 5% without. At this threshold, number needed to treat was 3.6. Conclusions: Real-world data shows that cilta-cel associated delayed toxicity is seen in ~10% of patients, with specific toxicity rates between 4-8%. Factors associated with these toxicities predominantly occur in the post CAR-T infusion period. Given that ALC expansion in the first 2 weeks correlate with CAR-T expansion, peak ALC is potentially predictive of toxicity and 3 x 10 9 /L may be an interventional threshold for primary prophylaxis. Peak ALC by toxicity groups. No ToxN=138 MNTN=8 CNPN=12 IEC-ECN=6 p value Peak ALC (10 9 /L), median (IQR) 1.8 (1.0-4.1) 6.2 (4.8-14.4) 4.3 (3.2-6.7) 3.6 (1.9-5.2) <0.001 1 ALC >3 x 10 9 /L, n (%) 43 (31%) 7 (88%) 10 (83%) 4 (67%) <0.001 2 Odd Ratios vs no Tox 3 , 95% CI 15.5 (2.6-293.7) 11.0 (2.8-73.9) 4.4 (0.8-25.0) 1 Wilcoxon/Kruskal-Wallis Tests, 2 Chi square test, 3 Logistic regression analysis.

12011 Background: Patients (pts) with high HCT-Comorbidity Index scores (HCT-CI of ≥3), older age (≥65 years), and/or who are frail per gait speed (<0.8 meters/second) have increased morbidity and mortality after allo-HCT compared to younger and healthier counterparts. We report the phase II analysis (PIIA) and interim phase III primary outcome analysis (PIIIA) of a seamless phase II/III prospective, randomized clinical trial conducted at 11 transplant centers to test new approaches to improve quality of life (QOL) in this population. Methods: Phase II compared specialist-administered supportive and palliative care (SPC), patient-administered management of comorbidities (MC, e.g. physical exercise, stress reduction, etc), both (SPC+MC), and usual care (UC) for change in Functional Assessment of Cancer Therapy – Bone Marrow Transplantation (FACT-BMT) QOL scores from baseline to day 90 (D90). Pts deceased prior to D90 were assigned FACT-BMT score=0. The winning phase II arm moved forward versus UC in phase III. Results: PIIA was done after enrolling 35 pts to each of the 4 study arms. Calculating the difference between FACT-BMT scores on D90 minus baseline, excluding missing data, indicated that only SPC resulted in a small improvement in QOL compared to either MC or SPC+MC (Table); hence SPC was the winning phase II arm. The PIIIA was conducted after enrolling 158 SPC pts and 153 UC pts. The SPC and UC arms were well balanced with median age (both 68 years), HCT-CI ≥3 (49% vs 55%), frailty per gait speed (13% vs 12%), female sex (41% vs 34%), non-white race (9.5% vs 11.5%), and Hispanic or unreported ethnicity (7.7% vs 5.7%), respectively. After median follow-up of 362 days, 45 pts had died in each arm. The mean QOL difference between D90 and baseline was 2.83 for SPC and 4.27 for UC (difference of differences, -1.44, 95% CI of difference, -5.03 to 2.14, p=0.43). Fitting a generalized linear model that included baseline, D30, and D90 values and testing the null hypothesis that the slope of scores differs between SPC and UC resulted in p=0.85. Using the Kaplan Meier method, no difference in survival was observed across arms (HR 1.13 [0.75-1.73]). Conclusions: Specialist-administered palliative care showed no meaningful improvement in QOL, nor a survival advantage, compared to usual care in frail, older and comorbid allo-HCT recipients, resulting in the cessation of this Phase II/III trial. Analysis of the whole patient population for primary and secondary outcomes is in progress. Clinical trial information: NCT03870750 . Change in FACT-BMT (D90 value minus baseline value) comparing the 4 arms of phase II. Group Mean difference (sd) Median difference (range) SPC (n=28) -2.93 (28.52) 0.25 (-102 to 38.22) CM (n=18) -18.37 (33.80) -9.50 (-98.33 to 11) SPC+CM (n=27) -15.76 (32.69) -7.67 (-90 to 32)

Importance Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure. Objective To estimate the worldwide burden of SAH. Design, Setting, and Participants Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021. Exposures SAH and 14 modifiable risk factors. Main Outcomes and Measures Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor–specific population attributable fractions (PAFs). Results In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution. Conclusions and Relevance Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.

Background Artificial intelligence (AI) and large language models (LLMs), are potential tools for enhancing healthcare delivery and clinical research. Presently, there is a scarcity of research regarding the viewpoints of medical students toward LLMs. This study aims to explore the perceptions of LLMs and their applications among this demographic. Methods A cross-sectional study was done using an online survey. It was designed using Google Forms and circulated from July 2023 to August 2023. The target population included medical students from Ecuador, Ethiopia, India, Mauritius, Pakistan, United Arab Emirates (UAE), and the United States (USA). Results A total of 1180 responses were collected from 10 medical colleges across 7 countries. The UAE had the largest number of responses (31.7%), followed by Ecuador (17.6%), and Mauritius (13.7%). And 77.4% respondents were already aware about LLMs before attempting the survey, most popularly, ChatGPT. Participants were most familiar with the specific use of LLMs in research (46%), and the lowest familiarity was seen in the use of LLMs in healthcare (37%). More than half of the participants (52%) support the use of LLMs healthcare, yet a significant number of them remained neutral (37%) or disagreed (31%) on LLMs being safe in this context. Conclusion While most of the medical students are aware of LLMs and their applications, the multinational survey demonstrated hesitancy among medical students in fully adopting LLMs into healthcare practice and clinical research. This study prompts further exploration of medical students’ attitudes and acceptance concerning the integration of LLMs for research methodologies, ethical considerations, and healthcare practice.

Abstract Introduction The new preference-informed allocation (PIA) system introduced for the 2024 UK cohort Foundation Programme (UKFPO) marks a shift away from the traditional meritocratic ‘ranking’ used in previous years. Instead of appointment to Foundation Programme places, PIA is a computer-generated allocation and deanery preferencing system. This change has raised numerous concerns among both students and clinicians. Aims To investigate the opinions of medical students on the new UKFPO PIA system. Methodology An online questionnaire was distributed to medical students graduating in 2024, 2025, or 2026 across the UK. Results In total, 2297 responses were collected and 2288 were included in the study. Overall, 51.6% (n = 1183) of respondents felt the PIA system was unfair, 76.3% (n = 1746) felt they had lost control of their application, and 46.3% (n = 1049) had noticed a negative effect on their physical or mental health. Notably, 48.2% (n = 1094) of students who responded are now considering a career outside the National Health Service (NHS). Conclusions Overall, the PIA system falls short of students’ expectations and has led to record numbers of students considering careers outside the NHS. Further changes to this system are needed and should aim to address fairness and equity while rewarding students for their hard work. According to these data, the PIA system risks further deteriorating workforce morale and attrition rates.

Cardiac tumors, though rare, present significant diagnostic and therapeutic challenges due to their heterogeneous nature and anatomical complexity. This narrative review synthesizes current evidence on prevalence, diagnostic modalities, and management strategies for primary and metastatic cardiac tumors. Echocardiography, cardiac MRI, and CT remain cornerstone imaging tools for differentiating tumors from non-neoplastic masses, while advances in PET/CT and tissue characterization techniques refine staging and treatment planning. Surgical resection with clear margins (R0) is critical for resectable tumors, particularly benign myxomas, though malignant tumors like sarcomas require multimodal approaches combining surgery, radiotherapy, and systemic therapies. Emerging strategies such as heart autotransplantation and staged resections offer promise for complex cases, while oligometastatic disease management highlights the role of stereotactic radiotherapy and immunotherapy. Key challenges include standardizing resection margins, optimizing neoadjuvant therapies, and addressing high recurrence rates in malignancies. Future directions emphasize integrating AI-driven imaging analysis, molecular biomarkers, and genomic profiling to personalize therapies, alongside global registries to enhance data on rare tumors. Equitable access to advanced diagnostics and multidisciplinary collaboration are essential to improve outcomes. This review underscores the need for standardized guidelines, technological innovation, and patient-centered research to address gaps in cardiac oncology.

Abstract The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic–clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource‐limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.