Faculty Bibliography

Mobile applications are widely used in epilepsy, although their impact on clinical effectiveness (CE) and their feasibility, acceptability, and usability (FAU) remain unclear. We conducted a systematic review investigating CE and FAU of epilepsy mobile applications using MEDLINE and Embase from database inception to June 21, 2024. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting standards. The protocol was registered on PROSPERO (CRD42019134848). In duplicate, we determined study quality using the Newcastle-Ottawa Quality Assessment Scale (NOQAS) and the Joanna Briggs Critical Appraisal Checklist (to determine eligibility for inclusion), risk of bias using the Cochrane Risk of Bias tool, and usability study quality using the 15-point Silva scale. We identified 8953 studies, of which 20 were included. Twelve (60.0%) addressed CE, nine (45.0%) acceptability, five (25.0%) usability, and eight (40.0%) feasibility. Five (25.0%) evaluated CE and FAU. Studies comprised prospective cohort (n = 9, 45.0%), pilot (n = 3, 15.0%), randomized controlled trial (n = 7, 35.0%), and pre/post (n = 1, 5.0%) designs. Most apps were used for self-management or to enhance education or communication between patients and providers. Cohort studies demonstrated fair quality (median NOQAS score = 5, interquartile range [IQR] = 5.0-5.8), whereas of seven randomized controlled trials, four (57.1%) had some concern for bias. Usability studies demonstrated high quality (median Silva score = 10, IQR = 10-11). Apps were predominantly intended for patient use (n = 9, 75.0%). Symptom reporting and medication management were the most common app targets in both CE and FAU studies (n = 8, 66.7%; n = 9, 69.2%), although FAU studies more frequently used monitoring or tracking (n = 10, 76.9%) and reminder setting (n = 10, 76.9%) than CE apps (n = 7, 58.3%). Investigations of application use most commonly studied CE and patient-facing apps. Additional high-quality evidence is necessary to evaluate the CE and FAU of app use in epilepsy to work toward the standardization of FAU metrics and development of implementation guidelines.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

BACKGROUND:Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT). METHODS:We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication. RESULTS:The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75). CONCLUSIONS:We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

Fatigue is a common and often debilitating feature of multiple sclerosis (MS) that lacks reliably effective treatment options for most patients. Transcranial direct current stimulation (tDCS), a safe and well-tolerated type of noninvasive brain stimulation, is a low-cost and home-based approach with the potential to reduce fatigue in MS. We conducted a double-blind, sham-controlled, randomized clinical trial to compare active vs. low-dose (sham) tDCS paired with computer-based cognitive training, delivered as a home-based intervention, to reduce MS-related fatigue. Participants with MS-related fatigue, but without depression, were stratified by neurologic disability using the Extended Disability Status Scale (EDSS) and randomized to complete 30 daily sessions over six weeks of either active or sham tDCS paired with online cognitive training (BrainHQ). The primary outcome was the change in PROMIS Fatigue score from baseline to the end of the intervention. A total of 117 participants were randomized, with 92% completing all treatment sessions. Both groups showed significant reductions in fatigue, with no significant difference between them. This suggests that tDCS does not provide any additional benefit over cognitive training alone in reducing fatigue, but confirms the feasibility and tolerance of this home-based intervention.

OBJECTIVE:This study examines the American Headache Society First Contact-Headache in Primary Care program metrics to date in order to assess the program's reach and provide direction for future initiatives. BACKGROUND:Approximately 4 million primary care office visits annually are headache-specific encounters. Therefore, it is important that primary care providers are knowledgeable about headache management. Recognizing the need, the American Headache Society First Contact designed the comprehensive First Contact-Headache in Primary Care program with input from an advisory board comprised of a diverse group of physicians and advanced practice providers with backgrounds in family and internal medicine, pediatrics, obstetrics and gynecology, and neurology. This is the first study to assess the reach of the program and critically examine how to best meet the needs of clinicians and patients going forward. METHODS:We report descriptive statistics for the First Contact website metrics from October 2020 to June 2023 and grand rounds program data from May 2020 to December 2023. We also conducted a cross-sectional analysis of survey data from presentations conducted at two large national family medicine symposia, as well as a thematic analysis of the question: "Please indicate what areas of your practice could be enhanced or improved with additional education?" RESULTS:The First Contact program homepage was the second most visited page on the American Headache Society website (>100,000 views). A total of 20 podcast episodes were created for the program (>3500 plays). The First Contact program held 99 events (72 institutional grand rounds, 22 State-level meetings, and five national meetings), reaching >7000 clinicians. The institutional grand rounds and state-level meetings were held across 27 States and Washington D.C. Only 31.9% (30/94) of First Contact program events (excluding national meetings) occurred in the West census region, which has the fewest headache subspecialists and lowest headache subspecialist density in the United States. When examining survey data of participants who attended the two virtual national family medicine symposia (39.3% response rate, N = 636/1620), 85.7% (544/635) reported being "completely confident" or "very confident" in their ability to recognize and accurately diagnose patients presenting with a primary complaint of headache and 81.5% (517/634) reported being "completely confident" or "very confident" in their ability to develop evidence-based treatment plans that are tailored to the needs of individual patients. The use of diagnostic tools to recognize patients with migraine (60.4%, 384/636) and translating standards of care to the practice setting (42.5%, 270/636) were the most reported intended changes by participants. Most participants reported that program content was of clinical relevance and would improve their patients' outcomes (90.5% [571/631] and 90.6% [572/631], respectively). Over three-quarters (77.8%, 495/636) of participants reported areas of their practice that can be improved by additional education specifically regarding workflow, diagnosis, and management. CONCLUSION/CONCLUSIONS:This study evaluates one of the first national initiatives for primary care education. Data from the two First Contact Family Medicine national symposia indicate the program is generally well received with most participants reporting improved confidence and intention to implement key changes in practice to improve care for patients with headache; however, there remain areas of exploration for education that could further enhance participant experience and expand the reach of the initiatives. Areas for future programming include continued education on multifactorial approaches to headache treatment and suggestions for addressing cost, insurance, and time constraints. Also, future work may examine where the First Contact program might focus initiatives based on specific areas of need in headache care, such as geographic "desert" areas, racial and ethnic disparities, and uninsured/underinsured populations.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Sex steroid hormones have been demonstrated to affect the immune system in multiple sclerosis (MS), and puberty may trigger MS activity. We aimed to evaluate the association between menarche and disease course in pediatric MS through comparison of relapse rates across premenarche, perimenarche, and postmenarche periods. METHODS:This is a retrospective analysis of a prospectively followed female cohort with pediatric-onset MS in the US Network of Pediatric MS Centers database. Perimenarche was considered the period from 1 year before to 1 year after the estimated menarche date based on menarche integer age. Relapses were collected prospectively. Negative binomial and repeated-measures Cox regression models were used to assess the association of pubertal development stage with relapse rate, adjusted for race, body mass index, and disease-modifying therapy (DMT). RESULTS:< 0.001). DISCUSSION/CONCLUSIONS:Onset of puberty may be a time of increase in disease activity and may require consideration of a change in therapeutic approach. Menarche age was used as a surrogate for puberty, and future studies measuring sex steroid hormones may be informative.

BACKGROUND:Brain arteriovenous malformations (AVMs) are abnormal connections between feeding arteries and draining veins, associated with significant risks of haemorrhage, seizures and other neurological deficits. Preoperative embolization is commonly used as an adjunct to microsurgical resection, with the aim of reducing intraoperative complications and improving outcomes. However, the efficacy and safety of this approach remain controversial. METHODS:This study is a subanalysis of the Multicenter International Study for Treatment of Brain AVMs consortium. We retrospectively analysed 486 patients with brain AVMs treated with microsurgical resection between January 2010 and December 2023. Patients were divided into two groups: those who underwent microsurgery alone (n=245) and those who received preoperative embolization, followed by microsurgery (n=241). Propensity score matching was employed, resulting in 288 matched patients (144 in each group). The primary outcomes were rates of complete AVM obliteration and functional outcomes (measured by the modified Rankin Scale (mRS)). Secondary outcomes included complication rates, mortality, hospital length of stay and postsurgical rupture. RESULTS:After matching, the complete obliteration rate was 97% with no significant difference between the microsurgery-only group and the preoperative embolization group (p=0.12). The proportion of patients with an mRS score of 0-2 at the last follow-up was similar in both groups (83% vs 84%; p=0.67). The median hospital stay was significantly longer for the embolisation group (9 days vs 7 days; p=0.017). Complication rates (24% vs 22%; p=0.57) and mortality rates (4.9% vs 2.1%; p=0.20) were comparable between the two groups. No significant differences were observed in postsurgical rupture, recurrence or retreatment rates. CONCLUSIONS:In this large multicentre study, preoperative embolization did not significantly improve AVM obliteration rates, functional outcomes or reduce complications compared with microsurgery alone.

BACKGROUND:The goal of precision oncology is to find effective therapeutics for every patient. Through the inclusion of emerging therapeutics in a high-throughput drug screening platform, our functional genomics pipeline inverts the common paradigm to identify patient populations that are likely to benefit from novel therapeutic strategies. APPROACH/METHODS:Utilizing drug screening data across a panel of 46 cancer cell lines from 11 tumor lineages, we identified an ovarian cancer-specific sensitivity to the first-in-class CRL4 inhibitors KH-4-43 and 33-11. CRL4 (i.e., Cullin-4 RING E3 ubiquitin ligase) is known to be dysregulated in a variety of cancer contexts, making it an attractive therapeutic target. Unlike proteasome inhibitors that are associated with broad toxicity, CRL4 inhibition offers the potential for tumor-specific effects. RESULTS:We observed that CRL4 inhibition negatively regulates core gene signatures that are upregulated in ovarian tumors and significantly slowed tumor growth as compared to the standard of care, cisplatin, in OVCAR8 xenografts. Building on this, we performed combination drug screening in conjunction with proteomic and transcriptomic profiling to identify ways to improve the antitumor effects of CRL4 inhibition in ovarian cancer models. CRL4 inhibition consistently resulted in activation of the mitogen-activated protein kinase (MAPK) signaling cascade at both the transcriptomic and protein levels, suggesting that survival signaling is induced in response to CRL4 inhibition. These observations were concordant with the results of the combination drug screens in seven ovarian cancer cell lines that showed CRL4 inhibition cooperates with MEK inhibition. Preclinical studies in OVCAR8 and A2780 xenografts confirmed the therapeutic potential of the combination of KH-4-43 and trametinib, which extended overall survival and slowed tumor progression relative to either single agent or the standard of care. CONCLUSIONS:Together, these data demonstrate the prospective utility of functional modeling pipelines for therapeutic development and underscore the clinical potential of CRL4 inhibition in the ovarian cancer context. HIGHLIGHTS/CONCLUSIONS:A precision medicine pipeline identifies ovarian cancer sensitivity to CRL4 inhibitors. CRL4 inhibition induces activation of MAPK signalling as identified by RNA sequencing, proteomics, and phosphoproteomics. Inhibitor combinations that target both CRL4 and this CRL4 inhibitor-induced survival signalling enhance ovarian cancer sensitivity to treatment.