Faculty Bibliography

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Chronic systemic inflammation has been hypothesized to be a mechanistic factor leading to post-acute cognitive dysfunction after COVID-19. However, little data exist evaluating longitudinal inflammatory markers. METHODS:We conducted a secondary analysis of data collected from the CONTAIN randomized trial of convalescent plasma in patients hospitalized for COVID-19, including patients who completed an 18-month assessment of cognitive symptoms and PROMIS Global Health questionnaires. Patients with pre-COVID-19 dementia/cognitive abnormalities were excluded. Trajectories of serum cytokine panels, D-dimer, fibrinogen, C-reactive peptide (CRP), ferritin, lactate dehydrogenase (LDH), and absolute neutrophil counts (ANCs) were evaluated over 18 months using repeated measures and Friedman nonparametric tests. The relationships between the area under the curve (AUC) for each inflammatory marker and 18-month cognitive and global health outcomes were assessed. RESULTS:< 0.05), with the exception of IL-1β, which remained stable over time. There were no significant associations between the AUC for any inflammatory marker and 18-month cognitive symptoms, any neurologic symptom, or PROMIS Global Physical or Mental health T-scores. Receipt of convalescent plasma was not associated with any outcome measure. DISCUSSION/CONCLUSIONS:At 18 months posthospitalization for COVID-19, cognitive abnormalities were reported in 27% of patients, and below average PROMIS Global Mental and Physical Health scores occurred in 24% and 51%, respectively. However, there were no associations with measured inflammatory markers, which decreased over time.

BACKGROUND AND PURPOSE/OBJECTIVE:Non-O blood types are known to be associated with thromboembolic complications (TECs) in population-based studies. TECs are known drivers of morbidity and mortality in intracerebral hemorrhage (ICH) patients, yet the relationships of blood type on TECs in this patient population are unknown. We sought to explore the relationships between ABO blood type and TECs in ICH patients. METHODS:Consecutive adult ICH patients enrolled into a prospective observational cohort study with available ABO blood type data were analyzed. Patients with cancer history, prior thromboembolism, and baseline laboratory evidence of coagulopathy were excluded. The primary exposure variable was blood type (non-O versus O). The primary outcome was composite TEC, defined as pulmonary embolism, deep venous thrombosis, ischemic stroke or myocardial infarction, during the hospital stay. Relationships between blood type, TECs and clinical outcomes were separately assessed using logistic regression models after adjusting for sex, ethnicity and ICH score. RESULTS:Of 301 ICH patients included for analysis, 44% were non-O blood type. Non-O blood type was associated with higher admission GCS and lower ICH score on baseline comparisons. We identified TECs in 11.6% of our overall patient cohort. . Although TECs were identified in 9.9% of non-O blood type patients compared to 13.0% in O blood type patients, we did not identify a significant relationship of non-O blood type with TECs (adjusted OR=0.776, 95%CI: 0.348-1.733, p=0.537). The prevalence of specific TECs were also comparable in unadjusted and adjusted analyses between the two cohorts. In additional analyses, we identified that TECs were associated with poor 90-day mRS (adjusted OR=3.452, 95% CI: 1.001-11.903, p=0.050). We did not identify relationships between ABO blood type and poor 90-day mRS (adjusted OR=0.994, 95% CI:0.465-2.128, p=0.988). CONCLUSIONS:We identified that TECs were associated with worse ICH outcomes. However, we did not identify relationships in ABO blood type and TECs. Further work is required to assess best diagnostic and prophylactic and treatment strategies for TECs to improve ICH outcomes.

The Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) was developed more than 25 years ago as an instrument to monitor functional change over time in patients with ALS. It has since been revised and extended to meet the needs of high data quality in ALS trials (ALSFRS-R), however a full re-validation of the scale was not completed. Despite this, the scale has remained a primary outcome measure in clinical trials. We convened a group of clinical trialists to discuss and explore opportunities to improve the scale and propose alternative measures. In this meeting report, we present a call to action on the use of the ALSFRS-Revised scale in clinical trials, focusing on the need for (1) harmonization of the ALSFRS-R administration globally, (2) alignment on a set of recommendations for clinical trial design and statistical analysis plans (SAPs), and (3) use of additional outcome measures.

BACKGROUND:For elderly patients with high-grade gliomas, 3-week hypofractionated radiotherapy (HFRT) is noninferior to standard long-course radiotherapy (LCRT). We analyzed real-world utilization of HFRT with and without systemic therapy in Medicare beneficiaries treated with RT for primary central nervous system (CNS) tumors using Centers for Medicare & Medicaid Services data. METHODS:Radiation modality, year, age (65-74, 75-84, or ≥85 years), and site of care (freestanding vs hospital-affiliated) were evaluated. Utilization of HFRT (11-20 fractions) versus LCRT (21-30 or 31-40 fractions) and systemic therapy was evaluated by multivariable logistic regression. Medicare spending over the 90-day episode after RT planning initiation was analyzed using multivariable linear regression. RESULTS:From 2015 to 2019, a total of 10,702 RT courses (ie, episodes) were included (28% HFRT; 65% of patients aged 65-74 years). A considerable minority died within 90 days of RT planning initiation (n=1,251; 12%), and 765 (61%) of those received HFRT. HFRT utilization increased (24% in 2015 to 31% in 2019; odds ratio [OR], 1.2 per year; 95% CI, 1.1-1.2) and was associated with older age (≥85 vs 65-74 years; OR, 6.8; 95% CI, 5.5-8.4), death within 90 days of RT planning initiation (OR, 5.0; 95% CI, 4.4-5.8), hospital-affiliated sites (OR, 1.4; 95% CI, 1.3-1.6), conventional external-beam RT (vs intensity-modulated RT; OR, 2.7; 95% CI, 2.3-3.1), and no systemic therapy (OR, 1.2; 95% CI, 1.1-1.3; P<.001 for all). Increasing use of HFRT was concentrated in hospital-affiliated sites (P=.002 for interaction). Most patients (69%) received systemic therapy with no differences by site of care (P=.12). Systemic therapy utilization increased (67% in 2015 to 71% in 2019; OR, 1.1 per year; 95% CI, 1.0-1.1) and was less likely for older patients, patients who died within 90 days of RT planning initiation, those who received conventional external-beam RT, and those who received HFRT. HFRT significantly reduced spending compared with LCRT (adjusted β for LCRT = +$8,649; 95% CI, $8,544-$8,755), whereas spending modestly increased with systemic therapy (adjusted β for systemic therapy = +$270; 95% CI, $176-$365). CONCLUSIONS:Although most Medicare beneficiaries received LCRT for primary brain tumors, HFRT utilization increased in hospital-affiliated centers. Despite high-level evidence for elderly patients, discrepancy in HFRT implementation by site of care persists. Further investigation is needed to understand why patients with short survival may still receive LCRT, because this has major quality-of-life and Medicare spending implications.

Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.

BACKGROUND:Observational studies looking at clinical a++nd MRI outcomes of treatments in pediatric MS, could assess current treatment algorithms, and provide insights for designing future clinical trials. OBJECTIVE:To describe baseline characteristics and clinical and MRI outcomes in MS patients initiating ocrelizumab and fingolimod under 18 years of age. METHODS:MS patients seen at 12 centers of US Network of Pediatric MS were included in this study if they had clinical and MRI follow-up and started treatment with either ocrelizumab or fingolimod prior to the age of 18. RESULTS:Eighty-seven patients initiating fingolimod and 52 initiating ocrelizumab met the inclusion criteria. Before starting fingolimod, mean annualized relapse rate was 0.43 (95 % CI: 0.29 - 0.65) and 78 % developed new T2 lesions while during treatment it was 0.12 (95 % CI: 0.08 - 1.9) and 47 % developed new T2 lesions. In the ocrelizumab group, the mean annualized relapse rate prior to initiation of treatment was 0.64 (95 % CI: 0.38-1.09) and a total of 83 % of patients developed new T2 lesions while during treatment it was 0.09 (95 % CI: 0.04-0.21) and none developed new T2 lesions. CONCLUSION/CONCLUSIONS:This study highlights the importance of evaluating current treatment methods and provides insights about the agents in the ongoing phase III trial comparing fingolimod and ocrelizumab.