Search

Your search keyword '"Ashley Gerald"' showing total 5 results
Start Over Author "Ashley Gerald"
5 results on '"Ashley Gerald"'

1. APDM gait and balance measures fail to predict symptom progression rate in Parkinson's disease

Author

D. Campbell Dewey, Shilpa Chitnis, Morgan C. McCreary, Ashley Gerald, Chadrick H. Dewey, Alexander Pantelyat, Ted M. Dawson, Liana S. Rosenthal, and Richard B. Dewey

Subjects
Parkinson's disease, gait, balance, disease progression, clinical trials, Neurology. Diseases of the nervous system, RC346-429
Abstract

Parkinson's disease (PD) results in progressively worsening gait and balance dysfunction that can be measured using computerized devices. We utilized the longitudinal database of the Parkinson's Disease Biomarker Program to determine if baseline gait and balance measures predict future rates of symptom progression. We included 230, 222, 164, and 177 PD subjects with 6, 12, 18, and 24 months of follow-up, respectively, and we defined progression as worsening of the following clinical parameters: MDS-UPDRS total score, Montreal Cognitive Assessment, PDQ-39 mobility subscale, levodopa equivalent daily dose, Schwab and England score, and global composite outcome. We developed ridge regression models to independently estimate how each gait or balance measure, or combination of measures, predicted progression. The accuracy of each ridge regression model was calculated by cross-validation in which 90% of the data were used to estimate the ridge regression model which was then tested on the 10% of data left out. While the models modestly predicted change in outcomes at the 6-month follow-up visit (accuracy in the range of 66–71%) there was no change in the outcome variables during this short follow-up (median change in MDS-UPDRS total score = 0 and change in LEDD = 0). At follow-up periods of 12, 18, and 24 months, the models failed to predict change (accuracy in the held-out sets ranged from 42 to 60%). We conclude that this set of computerized gait and balance measures performed at baseline is unlikely to help predict future disease progression in PD. Research scientists must continue to search for progression predictors to enhance the performance of disease modifying clinical trials.

Published
2022
Full Text
View/download PDF

3. Effect of Urate-Elevating Inosine on Early Parkinson Disease Progression: The SURE-PD3 Randomized Clinical Trial

Author

Tanya Simuni, Holly A. Shill, Matthew Brodsky, Marcie Rabin, Michael A. Schwarzschild, Kenneth Marek, Cheryl Waters, Cindy Casaceli, Steven A. Gunzler, Stephen G. Reich, Codrin Lungu, Sarah Elizabeth Zauber, Kellie Keith, Shyamal H. Mehta, Dariush Mozaffarian, Valerie Suski, Marie Saint-Hilaire, John L. Goudreau, Alice Rudolph, Ruth B. Schneider, Aaron Daley, Eric A. Macklin, Zoltan Mari, Grace F. Crotty, Andres Deik, Alberto J. Espay, Ashley Laroche, Sherri Mosovsky, Joohi Jimenez-Shahed, Mark S. LeDoux, Cynthia Poon, Ashley Gerald, John C. Morgan, Carolyn Peterson, Joseph H. Friedman, David Klements, Robert A. Hauser, Doozie Russell, David Simon, Kathrin LaFaver, Vanessa K. Hinson, Richard B. Dewey, Melissa Ainslie, Jason Aldred, Tiago A. Mestre, John Y. Fang, Liana S. Rosenthal, Grace Bwala, Raymond C. James, Binit B. Shah, Gearoid M. McMahon, Ariane Park, Rajeev Kumar, Lin Zhang, Ivan Bodis-Wollner, Mya C. Schiess, Katherine F. Callahan, David Oakes, Kelvin L. Chou, Melissa Kostrzebski, Roger Kurlan, Lisa Gauger, Albert Y. Hung, Melissa Bixby, Ira Shoulson, Michael Soileau, James T. Boyd, Peter A LeWitt, Burton L. Scott, Claire Henchcliffe, Patricia Kaminski, Alberto Ascherio, Cornelia Kamp, Lindsay Pothier, Anwar Ahmed, Jill Ciccarello, David J. Houghton, April Langhammer, Rebecca Fitzgerald, Maureen A. Leehey, Anthony E. Lang, Carmen Serrano, Martha McGraw, David Shprecher, Jennifer Durphy, Aleksandar Videnovic, Danish Bhatti, Christine Hunter, Amber Servi Ratel, J. Antonelle de Marcaida, Christopher G. Goetz, Emily Houston, Rajesh Pahwa, Chadwick W. Christine, Gary C. Curhan, Irene Litvan, Christopher A. Beck, Leslie J. Cloud, Patrick Bolger, Karen Thomas, Natividad Stover, Karen Blindauer, Sushrut S. Waikar, and Susan R. Criswell

Subjects
medicine.medical_specialty, business.industry, Dopaminergic, Unified Parkinson's disease rating scale, General Medicine, Placebo, law.invention, Clinical trial, Randomized controlled trial, law, Internal medicine, Severity of illness, medicine, Inosine, business, Adverse effect, medicine.drug, Original Investigation
Abstract

IMPORTANCE: Urate elevation, despite associations with crystallopathic, cardiovascular, and metabolic disorders, has been pursued as a potential disease-modifying strategy for Parkinson disease (PD) based on convergent biological, epidemiological, and clinical data. OBJECTIVE: To determine whether sustained urate-elevating treatment with the urate precursor inosine slows early PD progression. DESIGN, PARTICIPANTS, AND SETTING: Randomized, double-blind, placebo-controlled, phase 3 trial of oral inosine treatment in early PD. A total of 587 individuals consented, and 298 with PD not yet requiring dopaminergic medication, striatal dopamine transporter deficiency, and serum urate below the population median concentration (

Published
2021

4. Prediction of individual progression rate in Parkinson's disease using clinical measures and biomechanical measures of gait and postural stability

Author

Albert Montillo, Vyom Raval, Ashley Gerald, Kevin P. Nguyen, and Richard B. Dewey

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, medicine.medical_specialty, Computer Science - Machine Learning, Parkinson's disease, 02 engineering and technology, Disease, Neurological disorder, Quantitative Biology - Quantitative Methods, Article, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Gait (human), Physical medicine and rehabilitation, 0202 electrical engineering, electronic engineering, information engineering, medicine, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Quantitative Methods (q-bio.QM), Artificial neural network, business.industry, Disease progression, medicine.disease, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Postural stability, 020201 artificial intelligence & image processing, Progression rate, Neurons and Cognition (q-bio.NC), business, 030217 neurology & neurosurgery
Abstract

Parkinson's disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study was to develop a model using clinical measures and biomechanical measures of gait and postural stability to predict an individual's PD progression over two years. Data from 160 PD subjects were utilized. Machine learning models, including XGBoost and Feed Forward Neural Networks, were developed using extensive model optimization and cross-validation. The highest performing model was a neural network that used a group of clinical measures, achieved a PPV of 71% in identifying fast progressors, and explained a large portion (37%) of the variance in an individual's progression rate on held-out test data. This demonstrates the potential to predict individual PD progression rate and enrich trials by analyzing clinical and biomechanical measures with machine learning., Comment: 5 pages, 4 figures, IEEE ICASSP conference submission

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results