Faculty Bibliography
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Department/Unit:Neurology
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24282
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
PMCID:9722782
PMID: 36470898
ISSN: 2041-1723
CID: 5381682
Tribute to Steven Schachter, MD [Letter]
PMID: 36463058
ISSN: 1525-5069
CID: 5383822
The Utopia of Disability Inclusion in the Rehabilitation Sciences: An Insider's Perspective
The inclusion of people with disabilities has historically been, and continues to be, challenging work. In the health professions, the practice of inclusion should promote and advance the dissemination of efforts to decrease the impact of societal and physical barriers on the lives of people with disability, as well as promote innovative approaches to effectively foster an inclusive society. In addition to a focus on concepts such as the intact abilities of people with disabilities and the facilitation of community participation, an important shift in inclusion-related research requires listening to the lived experience of individuals with disabilities. Listening to their lived experiences and including the valuable insights gleaned from their insider's perspective can enrich efforts to evaluate clinical and educational programs, define population needs, and set research agendas and rehabilitation goals. Building on seminal work from Tamara Dembo, Beatrice Wright, and Margaret Brown, this communication from the Disability Representation Task Force at the American Congress of Rehabilitation Medicine also explores how healthcare providers living with a disability can make a significant contribution to rehabilitation treatment by analyzing how their own experience applies to clinical practice.
PMID: 36473220
ISSN: 1945-404x
CID: 5381692
IN-HOME-PDCaregivers: The effects of a combined home visit and peer mentoring intervention for caregivers of homebound individuals with advanced Parkinson's disease
INTRODUCTION/BACKGROUND:Family caregivers of people with advanced Parkinson's Disease (PD) are at high risk of caregiver strain, which independently predicts adverse patient outcomes. We tested the effects of one year of interdisciplinary, telehealth-enhanced home visits (IN-HOME-PD) with 16 weeks of peer mentoring on caregiver strain compared with usual care. METHODS:We enrolled homebound people with advanced PD (PWPD) and their primary caregiver as IN-HOME-PD dyads. We trained experienced PD family caregivers as peer mentors. Dyads received four structured home visits focused on advanced symptom management, home safety, medications, and psychosocial needs. Starting at approximately four months, caregivers spoke weekly with a peer mentor for 16 weeks. We compared one-year change in caregiver strain (MCSI, range 0-72) with historical controls, analyzed intervention acceptability, and measured change in anxiety, depression, and self-efficacy. RESULTS:Longitudinally, IN-HOME-PD caregiver strain was unchanged (n = 51, 23.34 (SD 9.43) vs. 24.32 (9.72), p = 0.51) while that of controls worsened slightly (n = 154, 16.45 (10.33) vs. 17.97 (10.88), p = 0.01). Retention in peer mentoring was 88.2%. Both mentors and mentees rated 100% of mentoring calls useful, with mean satisfaction of 91/100 and 90/100, respectively. There were no clinically significant improvements in anxiety, depression, or self-efficacy. CONCLUSIONS:Interdisciplinary telehealth-enhanced home visits combined with peer mentoring mitigated the worsening strain observed in caregivers of less advanced individuals. Mentoring was met with high satisfaction. Future caregiver-led peer mentoring interventions are warranted given the growing, unmet needs of PD family caregivers. TRIAL REGISTRATION/BACKGROUND:NCT03189459.
PMID: 36446676
ISSN: 1873-5126
CID: 5383572
Moving towards a taxonomy of cognitive impairments in epilepsy: application of latent profile analysis to 1178 patients with temporal lobe epilepsy
In efforts to understand the cognitive heterogeneity within and across epilepsy syndromes, cognitive phenotyping has been proposed as a new taxonomy aimed at developing a harmonized approach to cognitive classification in epilepsy. Data- and clinically driven approaches have been previously used with variability in the phenotypes derived across studies. In our study, we utilize latent profile analysis to test several models of phenotypes in a large multicentre sample of patients with temporal lobe epilepsy and evaluate their demographic and clinical profiles. For the first time, we examine the added value of replacing missing data and examine factors that may be contributing to missingness. A sample of 1178 participants met the inclusion criteria for the study, which included a diagnosis of temporal lobe epilepsy and the availability of comprehensive neuropsychological data. Models with two to five classes were examined using latent profile analysis and the optimal model was selected based on fit indices, posterior probabilities and proportion of sample sizes. The models were also examined with imputed data to investigate the impact of missing data on model selection. Based on the fit indices, posterior probability and distinctiveness of the latent classes, a three-class solution was the optimal solution. This three-class solution comprised a group of patients with multidomain impairments, a group with impairments predominantly in language and a group with no impairments. Overall, the multidomain group demonstrated a worse clinical profile and comprised a greater proportion of patients with mesial temporal sclerosis, a longer disease duration and a higher number of anti-seizure medications. The four-class and five-class solutions demonstrated the lowest probabilities of a group membership. Analyses with imputed data demonstrated that the four-class solution was the optimal solution; however, there was a weak agreement between the missing and imputed data sets for the four-Class solutions (κ = 0.288, P < 0.001). This study represents the first to use latent profile analysis to test and compare multiple models of cognitive phenotypes in temporal lobe epilepsy and to determine the impact of missing data on model fit. We found that the three-phenotype model was the most meaningful based on several fit indices and produced phenotypes with unique demographic and clinical profiles. Our findings demonstrate that latent profile analysis is a rigorous method to identify phenotypes in large, heterogeneous epilepsy samples. Furthermore, this study highlights the importance of examining the impact of missing data in phenotyping methods. Our latent profile analysis-derived phenotypes can inform future studies aimed at identifying cognitive phenotypes in other neurological disorders.
PMCID:9692194
PMID: 36447559
ISSN: 2632-1297
CID: 5383582
Perspective: Lumbar adhesive arachnoiditis (AA)/ Chronic AA (CAA) are clinical diagnoses that do not require radiographic confirmation
BACKGROUND/UNASSIGNED:Our hypothesis was that lumbar adhesive arachnoiditis (AA)/chronic lumbar AA (CAA) are clinical diagnoses that do not require radiographic confirmation. Therefore, patients with these syndromes do not necessarily have to demonstrate significant radiographic abnormalities on myelograms, MyeloCT studies, and/or MR examinations. When present, typical AA/CAA findings may include; central or peripheral nerve root/cauda equina thickening/clumping (i.e. latter empty sac sign), arachnoid cysts, soft tissue masses in the subarachnoid space, and/or failure of nerve roots to migrate ventrally on prone MR/Myelo-CT studies. METHODS/UNASSIGNED:We reviewed 3 articles and 7 clinical series that involved a total of 253 patients with AA/CAA to determine whether there was a significant correlation between these clinical syndromes, and myelographic, Myelo-CT, and/or MR imaging pathology. RESULTS/UNASSIGNED:We determined that patients with the clinical diagnoses of AA/CAA do not necessarily exhibit associated radiographic abnormalities. However, a subset of patients with AA/CAA may show the classical AA/CAA findings of; central or peripheral nerve root/cauda equina thickening/clumping (empty sac sign), arachnoid cysts, soft tissue masses in the subarachnoid space, and/or failure of nerve roots to migrate ventrally on prone MR/ Myelo-CT studies. CONCLUSION/UNASSIGNED:Patients with clinical diagnoses of AA/CAA do not necessary show associated neuroradiagnostic abnormalities on myelograms, Myelo-CT studies, or MR. Rather, the clinical syndromes of AA/CAA may exist alone without the requirement for radiolographic confirmation.
PMCID:9699873
PMID: 36447842
ISSN: 2229-5097
CID: 5383602
Life stressors significantly impact long-term outcomes and post-acute symptoms 12-months after COVID-19 hospitalization
BACKGROUND:Limited data exists evaluating predictors of long-term outcomes after hospitalization for COVID-19. METHODS:We conducted a prospective, longitudinal cohort study of patients hospitalized for COVID-19. The following outcomes were collected at 6 and 12-months post-diagnosis: disability using the modified Rankin Scale (mRS), activities of daily living assessed with the Barthel Index, cognition assessed with the telephone Montreal Cognitive Assessment (t-MoCA), Neuro-QoL batteries for anxiety, depression, fatigue and sleep, and post-acute symptoms of COVID-19. Predictors of these outcomes, including demographics, pre-COVID-19 comorbidities, index COVID-19 hospitalization metrics, and life stressors, were evaluated using multivariable logistic regression. RESULTS:Of 790 COVID-19 patients who survived hospitalization, 451(57%) completed 6-month (N = 383) and/or 12-month (N = 242) follow-up, and 77/451 (17%) died between discharge and 12-month follow-up. Significant life stressors were reported in 121/239 (51%) at 12-months. In multivariable analyses, life stressors including financial insecurity, food insecurity, death of a close contact and new disability were the strongest independent predictors of worse mRS, Barthel Index, depression, fatigue, and sleep scores, and prolonged symptoms, with adjusted odds ratios ranging from 2.5 to 20.8. Other predictors of poor outcome included older age (associated with worse mRS, Barthel, t-MoCA, depression scores), baseline disability (associated with worse mRS, fatigue, Barthel scores), female sex (associated with worse Barthel, anxiety scores) and index COVID-19 severity (associated with worse Barthel index, prolonged symptoms). CONCLUSIONS:Life stressors contribute substantially to worse functional, cognitive and neuropsychiatric outcomes 12-months after COVID-19 hospitalization. Other predictors of poor outcome include older age, female sex, baseline disability and severity of index COVID-19.
PMCID:9637014
PMID: 36379135
ISSN: 1878-5883
CID: 5383312
PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training
Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.
PMCID:9681023
PMID: 36420347
ISSN: 2767-3170
CID: 5384332
Disability outcomes in early-stage African American and White people with multiple sclerosis
BACKGROUND:Factors driving differences in disease burden between African American and White people with multiple sclerosis (pwMS) remain unclear. Here, we explored whether differences in disability outcomes could be observed after controlling for major sociodemographic factors and comorbidities, and assessed the presence of a possible interaction between MS and race. METHODS:In this cross-sectional study, 120 pwMS within 6 years from disease onset and 82 healthy controls between 18 and 70 years of age, self-identified as either African American or White, were prospectively enrolled. Inclusion criteria for pwMS were: diagnosis of MS according to the revised McDonald criteria, relapsing-remitting phenotype and Expanded Disability Status Scale (EDSS) < 6.5. Study outcomes included: (i) global disability (EDSS); (ii) quantitative mobility and leg function (Timed 25 Foot Walk Test-T25FWT); (iii) quantitative finger dexterity (9-Hole Peg Test-9HPT); (iv) cognitive efficiency and speed performance (Symbol Digit Modalities Test-SDMT). Differences in disability outcomes were assessed employing multivariable linear regression models. Based on their association with MS or disability, covariates included age, gender, race, years of education, total income, body mass index, comorbidities. The interaction between MS and race on disability outcomes was estimated via relative excess risk of interaction and attributable proportion. RESULTS:Accounting for age, gender, total income, education, body mass index and comorbidities, African American pwMS showed significantly worse performances in manual dexterity and cognition than White pwMS (White pwMS coeff. 3.24, 95% CI 1.55, 4.92 vs African American pwMS coeff. 5.52, 95% CI 3.55, 7.48 and White pwMS coeff. -5.87, 95% CI -8.86, -2.87 vs African American pwMS coeff. -7.99, 95% CI -11.58,-4.38). MS and race independently contributed to the observed gradient in disability severity. CONCLUSIONS:Complex social disparities and systemic racism might contribute to clinical heterogeneity in MS.
PMID: 36399964
ISSN: 2211-0356
CID: 5385012
Association of hyperglycemia and molecular subclass on survival in IDH-wildtype glioblastoma
BACKGROUND/UNASSIGNED:Hyperglycemia has been associated with worse survival in glioblastoma. Attempts to lower glucose yielded mixed responses which could be due to molecularly distinct GBM subclasses. METHODS/UNASSIGNED:Clinical, laboratory, and molecular data on 89 IDH-wt GBMs profiled by clinical next-generation sequencing and treated with Stupp protocol were reviewed. IDH-wt GBMs were sub-classified into RTK I (Proneural), RTK II (Classical) and Mesenchymal subtypes using whole-genome DNA methylation. Average glucose was calculated by time-weighting glucose measurements between diagnosis and last follow-up. RESULTS/UNASSIGNED:= .02). Methylation clustering did not identify unique signatures associated with high or low glucose levels. Metabolomic analysis of 23 tumors showed minimal variation across metabolites without differences between molecular subclasses. CONCLUSION/UNASSIGNED:Higher average glucose values were associated with poorer OS in RTKI and Mesenchymal IDH-wt GBM, but not RTKII. There were no discernible epigenetic or metabolomic differences between tumors in different glucose environments, suggesting a potential survival benefit to lowering systemic glucose in selected molecular subtypes.
PMCID:9653172
PMID: 36382106
ISSN: 2632-2498
CID: 5384812
