Faculty Bibliography

BACKGROUND/UNASSIGNED:There are currently no validated clinical biomarkers of postacute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE/UNASSIGNED:To investigate clinical laboratory markers of SARS-CoV-2 and PASC. DESIGN/UNASSIGNED:Propensity score-weighted linear regression models were fitted to evaluate differences in mean laboratory measures by prior infection and PASC index (≥12 vs. 0). (ClinicalTrials.gov: NCT05172024). SETTING/UNASSIGNED:83 enrolling sites. PARTICIPANTS/UNASSIGNED:RECOVER-Adult cohort participants with or without SARS-CoV-2 infection with a study visit and laboratory measures 6 months after the index date (or at enrollment if >6 months after the index date). Participants were excluded if the 6-month visit occurred within 30 days of reinfection. MEASUREMENTS/UNASSIGNED:Participants completed questionnaires and standard clinical laboratory tests. RESULTS/UNASSIGNED:levels was attenuated after participants with preexisting diabetes were excluded. Among participants with prior infection, no meaningful differences in mean laboratory values were found between those with a PASC index of 12 or higher and those with a PASC index of zero. LIMITATION/UNASSIGNED:Whether differences in laboratory markers represent consequences of or risk factors for SARS-CoV-2 infection could not be determined. CONCLUSION/UNASSIGNED:Overall, no evidence was found that any of the 25 routine clinical laboratory values assessed in this study could serve as a clinically useful biomarker of PASC. PRIMARY FUNDING SOURCE/UNASSIGNED:National Institutes of Health.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.

OBJECTIVES/OBJECTIVE:The objective of this study was to determine factors associated with negative disease-related stigma after hemorrhagic stroke. MATERIALS AND METHODS/METHODS:Patients with non-traumatic hemorrhage (ICH or SAH) admitted between January 2015 and February 2021 were assessed by telephone 3-months after discharge using the Quality of Life in Neurological Disorders (Neuro-QoL) Negative Disease-Related Stigma Short Form inventory. We evaluated the relationship between disease-related stigma (T-score >50) and pre-stroke demographics, admission data, and poor functional outcome (3-month mRS score 3-5 and Barthel Index <100). RESULTS:We included 89 patients (56 ICH and 33 SAH). The median age was 63 (IQR 50-69), 43 % were female, and 67 % graduated college. Admission median GCS score was 15 (IQR 13-15) and APACHE II score was 12 (IQR 9-17). 31 % had disease-related stigma. On univariate analysis, disease-related stigma was associated with female sex, non-completion of college, GCS score, APACHE II score, and 3-month mRS score (all p < 0.05). On multivariate analysis, disease-related stigma was associated with female sex (OR = 3.72, 95 % CI = 1.23-11.25, p = 0.02) and 3-month Barthel Index <100 (OR = 3.46, 95 % CI = 1.13-10.64, p = 0.03) on one model, and female sex (OR = 3.75, 95 % CI = 1.21-11.58, p = 0.02) and 3-month mRS score 3-5 (OR = 4.23, 95 % CI = 1.21-14.75, p = 0.02) on a second model. CONCLUSION/CONCLUSIONS:Functional outcome and female sex are associated with disease-related stigma 3-months after hemorrhagic stroke. Because stigma may negatively affect recovery, there is a need to understand the relationship between these factors to mitigate stroke-related stigma.

INTRODUCTION/BACKGROUND:The Accreditation Council for Graduate Medical Education (ACGME) approved the first pain medicine fellowship programs over three decades ago, designed around a pharmacological philosophy. Following that, there has been a rise in the transition of pain medicine education toward a multidisciplinary interventional model based on a tremendous surge of contemporaneous literature in these areas. This trend has created variability in clinical experience and education amongst accredited pain medicine programs with minimal literature evaluating the differences and commonalities in education and experience of different pain medicine fellowships through Program Director (PD) experiences. This study aims to gather insight from pain medicine fellowship program directors across the country to assess clinical and interventional training, providing valuable perspectives on the future of pain medicine education. METHODS:This study involved 56 PDs of ACGME-accredited pain fellowship programs in the United States. The recruitment process included three phases: advanced notification, invitation, and follow-up to maximize response rate. Participants completed a standard online questionnaire, covering various topics such as subcategory fields, online platforms for supplemental education, clinical experience, postgraduate practice success, and training adequacy. RESULTS:Surveys were completed by 39/56 (69%) standing members of the Association of Pain Program Directors (APPD). All PDs allowed fellows to participate in industry-related and professional society-related procedural workshops, with 59% encouraging these workshops. PDs emphasized the importance of integrity, professionalism, and diligence for long-term success. Fifty-four percent of PDs expressed the need for extension of fellowship training to avoid supplemental education by industry or pain/spine societies. CONCLUSION/CONCLUSIONS:This study highlights the challenge of providing adequate training in all Pain Medicine subtopics within a 12-month pain medicine fellowship. PDs suggest the need for additional training for fellows and discuss the importance of curriculum standardization.

We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with appropriate differential regularization. Our approach does not require prior estimation of the covariance structure of the functional predictors, which is computationally prohibitive in our application setting. We provide a theoretical analysis of the out-of-sample prediction error of the proposed model and explore the finite sample performance in a simulation setting. We apply the proposed method to a pooled dataset from Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative. Through this application, we identify discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease that are consistent with the existing neuroscience literature.

PURPOSE OF REVIEW/OBJECTIVE:This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders. RECENT FINDINGS/RESULTS:Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.

Mitochondrial fusion requires the sequential merger of four bilayers to two. The outer-membrane solute carrier protein SLC25A46 interacts with both the outer and inner-membrane dynamin family GTPases Mfn1/2 and Opa1. While SLC25A46 levels are known to affect mitochondrial morphology, how SLC25A46 interacts with Mfn1/2 and Opa1 to regulate membrane fusion is not understood. In this study, we use crosslinking mass-spectrometry and AlphaFold 2 modeling to identify interfaces mediating a SLC25A46 interactions with Opa1 and Mfn2. We reveal that the bundle signaling element of Opa1 interacts with SLC25A46, and present evidence of a Mfn2 interaction involving the SLC25A46 cytosolic face. We validate these newly identified interaction interfaces and show that they play a role in mitochondrial network maintenance.

OBJECTIVES/OBJECTIVE:Hospital readmissions are associated with poor health outcomes including illness severity and medical complications. The objective of this study was to identify characteristics associated with 30-day post-stroke readmission in an academic urban hospital network. MATERIALS AND METHODS/METHODS:We collected data on patients admitted with stroke from 2017 through 2022 who were readmitted within 30 days of discharge and compared them to a subset of non-readmitted stroke patients. Chart review was used to collect demographics, characteristics of the stroke, co-morbid conditions, in-hospital complications, and post-discharge care. Univariate analyses followed by regression analysis were used to assess characteristics associated with post-stroke readmission. RESULTS:We identified 4743 patients with stroke (18 % hemorrhagic, mean age 70.1 (standard deviation (SD) 17.2), 47.3 % female) discharged from the stroke services, of whom 282 (5.9 %) patients were readmitted within 30 days of index hospitalization. Univariate analyses identified 18 significantly different features between admitted and readmitted patients. Regression analysis revealed characteristics associated with readmission included private insurance (odds ratio (OR) 0.4, confidence interval (CI) 0.3-0.6, p < 0.001), comorbid peripheral vascular disease (PVD) (OR 2.7, CI 1.3-5.5, p = 0.009), malignancy (OR 1.6, CI 1.0-2.6, p = 0.04), seizure (OR 3.4, CI 1.4-8.2, p = 0.007), thrombolytic administration (OR 0.4, CI 0.2-0.7, p = 0.003), undergoing thrombectomy (OR 5.4, CI 2.9-10.1, p < 0.001), and higher discharge modified Rankin Scale score (OR 1.2, CI 1.0-1.3, p = 0.047). CONCLUSIONS:Our data demonstrate that thrombectomy, high discharge Rankin score, comorbid malignancy, seizure or PVD, and lack of thrombolytic administration or private insurance predict readmission.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Participants with treatment-resistant epilepsy who are randomized to add-on placebo and remain in a trial for the typical 3 to 5-month maintenance period may be at increased risk of adverse outcomes. A novel trial design has been suggested, time to prerandomization monthly seizure count (T-PSC), which would limit participants' time on ineffective therapy. We reanalyzed 11 completed trials to determine whether the primary efficacy conclusions at T-PSC matched each of the original, longer trials. METHODS:A total of 11 double-blind, placebo-controlled trials of levetiracetam, brivaracetam, lacosamide, topiramate, and lamotrigine for either focal-onset or generalized-onset epilepsy were selected. We evaluated the group-level and individual-level efficacy of treatments including the median percent reduction (MPR) in seizure frequency and 50% responder rate (50RR) at T-PSC, time to second seizure, and time to first seizure compared with the full-length trial. RESULTS:The primary efficacy conclusions of 10 of the 11 trials would have been the same with a T-PSC design compared with the traditional design (the exception of lamotrigine had a very high initial placebo response). As a proportion of the full-length effect size, 90% of the MPR and 85% of the 50RR were seen at T-PSC (95% CI 73%-113% and 65%-110%, respectively). Using the T-PSC design, the time on blinded treatment was at least 312 participant-years shorter (40% of total duration) and 142,000 seizures occurred during this time (60% of total seizures). By contrast, the time to first or second seizure designs reproduced group-level effect size, but the primary efficacy conclusions of each trial and individual-level efficacy correspondence were fair to poor. DISCUSSION/CONCLUSIONS:These results support the use of this trial design for new epilepsy medication trials because this reanalysis of 11 randomized controlled trials demonstrated that observation until T-PSC was sufficient to demonstrate efficacy while potentially improving participant safety by reducing the time of exposure to placebo and inadequate treatment. Despite analysis of 11 trials including 3,619 participants, we did not observe a significant reduction in the group-level effect size, which is directly related to statistical power. The next step is to evaluate whether T-PSC is sufficient to evaluate safety as measured by adverse events.

An existing scale of personal growth in caregivers of people with multiple sclerosis (MS) was expanded for use with an acquired brain injury (ABI) population, and was modified following additional psychometric analyses. A cross-sectional online survey was administered to 315 caregiving partners of persons with MS and 310 family caregivers of persons with ABI. Principal component analysis (PCA) performed on the original 32-item instrument yielded a 4-component, 17-item solution with correlated subscales with solid psychometric properties. Subscales were labelled Appreciation, Positivity, Adjustment, and Spirituality. Secondary PCA conducted revealed three subscales (five items each) correlated moderately while the fourth, Spirituality, remained distinct. The sum of the three five-item subscales may serve as a total score. Reliability analysis yielded acceptable-to-high internal consistency. Comparisons of the PGS with existing instruments demonstrated its discriminant/convergent validity. Two kinds of latent class analyses were conducted on the 15-item PGS to identify three latent classes that spanned the neurologic groups, revealing that measurement invariance was held for the instrument in this sample. An instrument with sound psychometric properties was established, designed to assess personal growth in caregivers of individuals with ABI or MS. Future work should explore its value in other populations and as a metric of changes over time.