Citations

Abstract Oscillations in the high gamma and ripple frequency ranges are known to coordinate local hippocampal and neocortical neuronal assemblies during memory encoding and recall. Here, we explored spatiotemporal dynamics and the role of global coordination of these fast oscillatory discharges across the sensory and associational cortical areas in distinct phases of memory processing. Individual bursts of high frequency oscillations were detected in intracranial recordings from epilepsy patients remembering word lists for immediate free recall. We found constant coincident bursting across visual and higher order processing areas, peaking before recall and elevated during encoding of words. This global co-bursting was modulated by memory processing, engaged approximately half of the recorded electrode sites, and clustered into a sequence of multiple consecutive bursting discharges. Our results suggest a general role of global coincident high frequency oscillations in organizing large-scale information processing across the brain necessary especially, but not exclusively, for memory functions.

Objective The objective is to provide evidence‐based and expert guidance for the screening, treatment, and management of lupus nephritis. Methods The Core Team developed clinical questions for screening, treatment, and management of lupus nephritis using the PICO format (population, intervention, comparator, and outcome). Systematic literature reviews were completed for each PICO question, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the quality of evidence and to formulate recommendations. The Voting Panel achieved a consensus ≥70% on the direction (for or against) and strength (strong or conditional) of each recommendation. Results We present 28 graded recommendations (7 strong, 21 conditional) and 13 ungraded, consensus‐based good practice statements for the screening and management of lupus nephritis. Our recommendations focus on the unifying principle that lupus nephritis therapy is continuous and ongoing, rather than consisting of discrete induction/initial and maintenance/subsequent therapies. Therapy should include pulse glucocorticoids followed by oral glucocorticoid taper and two additional immunosuppressive agents for 3–5 years for those achieving complete renal response. Conclusion This guideline provides direction for clinicians regarding screening and treatment decisions for management of lupus nephritis. These recommendations should not be used to limit or deny access to therapies, as treatment decisions may vary due to the unique clinical situation and personal preferences of each individual patient.

ImportanceUnderstanding the relationship between patients’ clinical characteristics and outcomes is fundamental to medicine. When critically ill patients die after withdrawal of life-sustaining therapy (WLST), the inability to observe the potential for recovery with continued aggressive care could bias future clinical decisions and research.ObjectiveTo quantify the frequency with which experts consider patients who died after WLST following resuscitated cardiac arrest to have had recovery potential if life-sustaining therapy had been continued.Design, Setting, and ParticipantsThis prospective cohort study included comatose adult patients (aged ≥18 years) treated following resuscitation from cardiac arrest at a single academic medical center between January 1, 2010, and July 31, 2022. Patients with advanced directives limiting critical care or who experienced cardiac arrest of traumatic or neurologic etiology were excluded. An international cohort of experts in post-arrest care based on clinical experience and academic productivity was identified. Experts reviewed the cases between August 24, 2022, and February 11, 2024.ExposurePatients who died after WLST.Main Outcome and MeasuresThree or more experts independently estimated recovery potential for each patient had life-sustaining treatment been continued, using a 7-point numerical ordinal scale. In the primary analysis, which involved the patient cases with death after WLST, a 1% or greater estimated recovery potential was considered to be clinically meaningful. In secondary analyses, thresholds of 5% and 10% estimated recovery probability were explored.ResultsA total of 2391 patients (median [IQR] age, 59 [48-69] years; 1455 men [60.9%]) were included, of whom 714 (29.9%) survived to discharge. Cases of uncertain outcome (1431 patients [59.8%]) in which WLST preceded death were reviewed by 38 experts who rendered 4381 estimates of recovery potential. In 518 cases (36.2%; 95% CI, 33.7%-38.7%), all experts believed that recovery potential was less than 1% if life-sustaining therapies had been continued. In the remaining 913 cases (63.8%; 95% CI, 61.3%-66.3%), at least 1 expert believed that recovery potential was at least 1%. In 227 cases (15.9%; 95% CI, 14.0%-17.9%), all experts agreed that recovery potential was at least 1%, and in 686 cases (47.9%; 95% CI, 45.3%-50.6%), expert estimates differed at this threshold.Conclusions and RelevanceIn this cohort study of comatose patients resuscitated from cardiac arrest, most who died after WLST were considered by experts to have had recovery potential. These findings suggest that novel solutions to avoiding deaths based on biased prognostication or incomplete information are needed.

Objective: Multicancer Detection (MCD) tests, such as the GRAIL Galleri, offer a novel approach to cancer screening by detecting cancer-specific methylation patterns in cell-free DNA through a single blood sample. This study evaluated an 18-month implementation of MCD testing in a tertiary ambulatory internal medicine clinic. Patients and Methods: Between June 2022 and November 2023, 2244 asymptomatic (without symptoms attributed to cancer) patients underwent MCD testing. The study focused on operational workflows, patient and physician education, and diagnostic follow-up of positive results. Standardized materials, including electronic health record (EHR) workflows, FAQs, and diagnostic pathways, were developed to facilitate implementation. Challenges included managing false positives, patient anxiety, costs, and ethical considerations. Results: Of the 2244 patients tested, 17 (0.76%) had positive results, and 15 underwent further diagnostic evaluation. Cancer was confirmed in 11 (73.3%) patients, including cases of breast, colon, esophageal, lymphoma, ovarian, and pancreatic cancers. Four patients had no identifiable malignancy despite comprehensive work-up. Conclusions: MCD testing is feasible in routine clinical workflows, with 73% of positive cases yielding cancer diagnoses. While promising, further research is required to assess long-term outcomes, cost-effectiveness, and optimal implementation strategies of cancer interception in broader healthcare settings.

Objective: This retrospective observational study conducted in a general ICU aimed to evaluate the hemodynamic impact of IV paracetamol by monitoring blood pressure, urine output, and core temperature post-administration. Design: The study was designed as a retrospective observational study in a general ICU setting. Setting: The study was conducted in a general intensive care unit (ICU). Patients: Data from 498 patients receiving IV paracetamol in various clinical contexts were analyzed. Interventions: Patients received IV paracetamol for analgesia and fever reduction as part of their clinical care. Measurements and main results: The study analyzed the hemodynamic effects of IV paracetamol by monitoring blood pressure, urine output, and core temperature post-administration. A significant decrease in mean systolic and diastolic blood pressure values was observed across different patient subgroups, notably 45–60 min post-infusion. An increase in noradrenaline dosage and a decrease in urine output indicated a decline in end-organ perfusion following IV paracetamol administration. Multivariate analysis identified associations between clinical factors (such as general anesthesia and cardiac conditions) and changes in blood pressure. Conclusions: While IV paracetamol remains a valuable therapeutic option for pain and fever management, especially in hypertensive patients with specific conditions like traumatic brain injury and cerebral vascular accident, careful monitoring, and individualized dosing strategies are recommended in critically ill patients to maintain hemodynamic stability and optimize clinical outcomes. These findings contribute to enhancing our understanding of IV paracetamol hemodynamic effects and inform evidence-based practices for its use in ICU settings.

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI model that analyzes voice to noninvasively predict aspiration. Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. Patients were classified into three groups: aspirators (Penetration-Aspiration Scale, PAS 6–8), probable (PAS 3–5), and non-aspirators (PAS 1–2) based on video fluoroscopic swallow (VFSS) findings. Multivariate analysis evaluated patient demographics, history of head and neck surgery, radiation, neurological illness, obstructive sleep apnea, esophageal disease, body mass index, and vocal cord dysfunction. Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. The model was then validated using an independent, external database. Results/Anticipated Results: Aspirators were found to have quantifiably worse quality of sound with higher jitter and shimmer but lower harmonics noise ratio. NAM modeling classified aspirators and non-aspirators as distinct groups (aspirator NAM risk score 0.528+0.2478 (mean + std) vs. non-aspirator (control) risk score of 0.252+0.241 (mean + std); p Discussion/Significance of Impact: We report the use of voice as a novel, noninvasive biomarker to detect aspiration risk using machine learning techniques. This tool has the potential to be used for the safe and early detection of aspiration in a variety of clinical settings including intensive care units, wards, outpatient clinics, and remote monitoring.

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: As a priority area in translational science, rural health research can benefit from informatics methods for conducting thematic and environment scans. This study demonstrates an efficient approach to gaining insights about the rural health research literature by automated bibliometrics analysis. Methods/Study Population: We developed an automated pipeline to retrieve the 1972–2023 PubMed publications indexed with the MeSH terms “Rural Health” and “United States”. The article metadata in XML format were downloaded and parsed, including title, year, journal, author institutions, and MeSH terms. Each institution address was augmented by Google Maps API to obtain the county and latitude/longitude coordinates. Summary statistics were computed for the publication years, journals, author departments, and locations. A topic network was generated from the frequent co-occurring MeSH terms. The institutions were linked to Rural-Urban Continuum Codes and labeled on a map to visualize their geographic distribution. Results/Anticipated Results: A total of 4564 articles on rural health were analyzed. Two salient peaks of publications were revealed, one around 1978 and the other around 1993. The top author departments include Family Medicine, Nursing, Pediatrics, and Epidemiology. The five leading institutions reside in Chapel Hill, Minneapolis, Iowa City, Seattle, and Atlanta. The geographic distribution shows few institutions that reside in deep rural areas are well published on rural health, although the most scholarly productive institutions do seem adjacent to some moderately rural pockets. The frequently identified topics pertain to age group, study design, and specific concepts such as health services accessibility. Discussion/Significance of Impact: The two publication peaks in history were likely linked to certain policy milestone or seminal publication. Primary care and epidemiology departments have been most active in rural health research. Of concern, the geographic distribution of authors suggests under-investment in rural institutions.