Citations

Dermatoneuro syndrome (DNS) is a rare diagnosis of exclusion characterised by fever, altered mental status and seizures in patients with scleromyxoedema. We present a complex case of a patient presenting with a fever, coma, seizures, rigidity and deep T2 fluid-attenuated inversion recovery hyperintensities in the context of both scleromyxoedema and influenza A with extensive, negative infectious workup. This case highlights the shared clinical features of DNS and influenza-associated encephalopathy, the latter being the eventual diagnosis for our patient. We review the DNS literature focusing on presentation, adequacy of investigation, treatment and prognosis. Finally, we provide an initial workup for patients with suspected DNS, including nasopharyngeal influenza PCR.

Objectives/Goals: Identifying and indexing rare disease studies is labor intensive, especially in research centers with a large number of trials. To address this gap, we applied natural language processing (NLP) and visualization techniques to develop an efficient pipeline and user-friendly web interface. Our goal is to offer the rare disease study identification (RDSI) tool for adoption by other sites. Methods/Study Population: The RDSI retrieves study information (short and long titles, study abstract) from the IRB system. These descriptive fields are then processed by the MetaMap Lite NLP program for identifying disease terms and standardizing them to UMLS concepts. By terminology identifier mapping, the diseases intersecting with concepts in rare disease databases (Genetic and Rare Disease program and Orphanet) are further scored to pinpoint studies that focus on a rare disease. The web interface displays a scatter bubble chart as an overview of all the rare diseases, with each bubble size proportional to the number of studies for that disease. In addition to the visual navigation, users can search studies by disease name, PI, or IRB number. Search results contain detailed study information as well as the evidence used by algorithms of the pipeline. Results/Anticipated Results: The RDSI identification results and functions were verified manually and spot-checked by several study investigators. The web interface is a self-contained solution available to our staff for various use cases like reporting or environment scan. We have built in a versioning mechanism that logs the date of each major result in the process. Therefore, even as the rare disease data sources evolve over time, we will be able to preserve any historical context or perform updates as needed. The RDSI outputs are replicated to Mayo Clinic’s enterprise data warehouse daily, allowing tech-savvy users to leverage any useful intermediate results at the backend. We anticipate the performance of the rare disease identification to be further enhanced by employing the advancements in AI technology. Discussion/Significance of Impact: The RDSI represents an informatics solution that offers efficiency in identifying and navigating rare disease clinical studies. It features the use of public databases and open-source tools, manifesting return on investment from the broad translational science ecosystem. These considerations are informative and adoptable by other institutions.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Objectives/Goals: This project will enrich our understanding of basal breast carcinogenesis, highlighting the distinct biological differences in breast cancer risk between non-Hispanic Black (NHB) or non-Hispanic White (NHW) women, but also has the potential to provide real-world solutions thereby contributing to reducing health disparities in breast cancer outcomes Methods/Study Population: In the biological investigation, I am utilizing a unique cohort of normal breast tissues from Mayo Clinic patients who underwent breast reduction surgery that have self-identified as NHB or NHW premenopausal women. From these tissues, we are able to subculture human mammary epithelial cells, which we will use for our consequent experiments. For the qualitative study, I aim to recruit up to 40–50 eligible NHB women identified at increased risk for breast cancer and conduct semi-structured qualitative interviews informed by the National Institute on Minority Health and Health Disparities Research Framework. Results/Anticipated Results: Gene expression profiling on our cohort of 15 NHB and 40 NHW premenopausal women identified a gene expression signature in NHB women indicative of elevated Hedgehog signaling, a key factor in triple-negative breast cancer (TNBC) development and progression. I hypothesize that persistent activation of Hedgehog signaling within NHB women instigates the proliferation and transformation of basal stem cells within the breast, thus fueling TNBC development. Understanding the perceptions and experiences of NHB women that are identified as being at an increased risk for BC will aid in the identification of barriers and facilitators to their risk reduction care and in turn provide implementation strategies that could contribute to alleviating the racial disparity seen in TNBC morbidity, mortality, and preventative care. Discussion/Significance of Impact: The significance of this project lies in its dual approach, uncovering basal breast cancer mechanisms in a high-risk group using normal breast tissue and evaluating the perspectives from NHB women to enhance the practical relevance of our findings.

Objectives/Goals: This proposal outlines the successful deployment of a research training initiative to support the formation of a Learning Healthcare System. Mayo Clinic Health System (MCHS) rural providers were offered the opportunity to the fundamentals of clinical research via Clinical and Translational Science Awards core curriculum, mentorship, and an online seminar series. Methods/Study Population: MCHS funded 4 key introductory research courses: 1) Manuscript Writing, 2) Grant Writing, 3) Basic Biostatistics, and 4) Essentials of Clinical and Translations Science Program. In addition to course offerings, a Research Interest Group was formed to guide novice rural researchers on topic selection and study design. This cultivated interest to create a 16-month clinical research webinar series offering CME credits. Subsequently, an internal MCHS RFA was launched seeking early-stage investigator pilot proposals focused on rural health. Results/Anticipated Results: In 2023, over 140 MCHS providers enrolled in 324 CCaTS research courses. This training led to the submission of 53 proposals to the inaugural MCHS 2023 RFA, of which 15 were awarded. Additionally, 14 MCHS extramural grants were submitted in 2023. Training efforts expanded in 2024 to include an online research seminar series covering various study topics and providing CME credit, with an approximate attendance up to 196 attendees per session. The second annual MCHS RFA resulted in 4 internal awards, with an additional 22 extramural grant submissions. These collective efforts have increased the number of MCHS first and last author publications and the number of MCHS providers with academic rank. Discussion/Significance of Impact: Leadership’s commitment of resources to educate, mentor, and engage clinicians was crucial to our success and demonstrated a strong return on investment. To maximize impact in community-based practice, continued commitment is needed in the form of protected research time, funding, and research administration support of projects of interest

Objectives/Goals: The operation of a clinical trials unit involves multifaceted tasks and stakeholders. A competent information system is critical to daily operations while ensuring smooth conduct of clinical research. We share 15 years of experience in the design and implementation of such a system at Mayo Clinic to inform other institutions with similar interests. Methods/Study Population: The Informatics team collaborated closely with nurse leaders and elicited input from additional stakeholders including nurse unit coordinators, lab managers, schedulers, investigators, study coordinators, and regulatory specialists throughout the phases of system design, development and continuous enhancements, and expansion. The stakeholders offered insights on the corresponding requirements throughout the study life cycle, from engaging with the study sponsor, operational review for protocol execution, development of study budgets, human subject protection and risk mitigation, data management and integration, to outcome monitoring, and regulatory reporting. The activities were then translated into functional components and implemented as a seamless and effective solution. Results/Anticipated Results: Patient safety, scientific rigor, operation automation, efficiency, and regulatory requirements were all considered in developing an integrated system, or the clinical research trials unit (CRTU) Tools. Our institution has leveraged the system for essential tasks from the study start-up, visit scheduling and execution, specimen collection and tracking, to individual protocol metrics and billing. We adopted a measure-as-we-go methodology so that data such as visit census, resource usage, and protocol deviation are tracked and collected during routine use of the system. Specifically, an issues/concerns/exceptions (ICE) tool is used for quality control and patient safety. Moreover, data quality greatly benefits from a task dictionary, standardizing the study activities that can be ordered and executed. Discussion/Significance of Impact: The implementation of a well-rounded clinical trials unit information system not only improves the operation efficiency and team productivity but also ensures scientific rigor and contributes to patient safety. We believe the experience can be informative to other institutions. More details will be shared in the poster.

Introduction: Online self-scheduling of medical appointments is increasingly common. An automated waitlist can be used for patients who desire an earlier appointment time if one becomes available after they are scheduled. Our study examines outcomes of an automated waitlist and self-rescheduling process. Methods: We studied outcomes of an automated waitlist self-rescheduling process in which patients with existing appointments elected to be placed on an automated waitlist for an earlier appointment offer. When software found earlier dates for the same visit type, patients were then notified through an automated process and could self-reschedule. We reviewed appointments for which patients were sent new offers when earlier appointment slots were found. We compared the accepted appointment offers with the original scheduled appointments and determined the number of days that the appointment had been moved up. Results: Spanning the calendar year 2023 there were 1 019 698 appointment offers generated by an automated waitlist process for 229 998 appointments and sent to 164 248 patients. The waitlist process automatically found open appointments as they became available and sent the first new appointment offer within 2 days after being placed on the waitlist for 74 736 (32.5%) of the 229 998 waitlisted appointments. Patients sent back at least 1 response for 104 554 (45.4%) of the waitlisted appointments. Of the responses, 56 636 accepted one of the sent offers for an accept rate of 24.6% (56 636/229 998). For accepted, moved-up visits, appointments were self-rescheduled earlier by a mean of 22.6 days (95%CI; 22.2, 22.9, P < .0001). Conclusion: New appointments can be successfully self-rescheduled using an automated waitlist process that allows patients to accept or decline new appointment offers. This process can increase the efficiency of scheduling and decrease appointment wait time for patients desiring more timely access to healthcare. In addition, this process can be successfully applied across several different appointment type categories.

AbstractINTRODUCTIONAlzheimer's disease (AD) pathology causes corticobasal syndrome (CBS) in 21%–50% of patients. Studies have assessed hypometabolism in CBS according to β‐amyloid (A) positron emission tomography (PET), but the understanding of the association of both AD‐tau (T) and A with hypometabolism is incomplete.METHODSThirty‐three CBS patients and 45 controls underwent fluorodeoxyglucose (FDG), flortaucipir, and Pittsburgh compound‐B PET and were classified as A± and T±. FDG‐PET uptake was extracted for 12 regions‐of‐interest in dominant (most affected) and non‐dominant hemispheres and compared across A/T groups.RESULTSA+T+ patients had greater hypometabolism in temporo‐parieto‐occipital cortices than A+T‐ and A‐T‐ groups, with no differences observed between the A+T‐ and A‐T‐ groups. FDG asymmetry was more accentuated in A+T+ patients. Medial temporal and basal ganglia metabolism were similar across AT groups.DISCUSSIONAmyloid and tau positivity contribute synergistically to hypometabolism and asymmetry in temporo‐parieto‐occipital cortices in CBS, with AD‐like patterns of hypometabolism observed only in A+T+ patients.Highlights Amyloid (A) and tau PET (T) status can be used to stratify CBS patients. A+T+ CBS patients show more hypometabolism in temporo‐parieto‐occipital cortices. Medial temporal metabolism (typical AD pattern) is similar across AT groups. Parieto‐occipital cortices should be assessed when investigating AT pathology in CBS. Amyloid and tau positivity contribute synergistically to hypometabolism and asymmetry in CBS.

Abstract Germinal center B-cell lymphomas frequently exhibit monoallelic loss-of-function mutations in the histone acetyltransferases CREBBP and p300, which contribute to lymphoma development, disrupt normal germinal center biology, and promote immune evasion. This study evaluates YF2, a histone acetyltransferase activator that increases CREBBP/p300 activity, as a potential therapeutic approach for B-cell lymphoma. YF2 binds the bromodomain of CREBBP/p300, increasing their auto-acetylation and enzymatic activity. It also induces cytotoxicity in B-cell lymphoma cell lines, with a stronger response in those carrying CREBBP / EP300 mutations. Additionally, YF2 increases the acetylation of key CREBBP/p300 substrates, including H3K27, p53, and BCL6, leading to enhanced apoptosis and altered B-cell diTerentiation. YF2 is well tolerated in vivo and extends survival in both cell line- and patient-derived xenograft mouse models. Moreover, YF2 exposure upregulates antigen-presentation markers and reshapes the immune microenvironment, strengthening responses to immune checkpoint blockade. Taken together, these findings support pharmacologic activation of CREBBP/p300 with YF2 as a compelling therapeutic strategy for B-cell lymphoma. Significance YF2 stimulates CREBBP/p300 activity to normalize B-cell diTerentiation and enhance immune recognition, oTering a potential approach to limit lymphoma progression and strengthen immunotherapy responses.