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200 results on '"Jenkins, Nathaniel W."'

2. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.

Author

Ding, Yiming, Sohn, Jae Ho, Kawczynski, Michael G, Trivedi, Hari, Harnish, Roy, Jenkins, Nathaniel W, Lituiev, Dmytro, Copeland, Timothy P, Aboian, Mariam S, Mari Aparici, Carina, Behr, Spencer C, Flavell, Robert R, Huang, Shih-Ying, Zalocusky, Kelly A, Nardo, Lorenzo, Seo, Youngho, Hawkins, Randall A, Hernandez Pampaloni, Miguel, Hadley, Dexter, and Franc, Benjamin L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Clinical Research, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Acquired Cognitive Impairment, Aging, Biomedical Imaging, Dementia, Alzheimer's Disease, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Neurological, Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Cognitive Dysfunction, Deep Learning, Female, Fluorodeoxyglucose F18, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Positron-Emission Tomography, Retrospective Studies, Sensitivity and Specificity, Medical and Health Sciences, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

Published
2019

28. The Worldwide Influence of Social Media on Cervical Spine Literature

Author

Lynch, Conor P., primary, Cha, Elliot D.K, additional, Jacob, Kevin C., additional, Patel, Madhav R., additional, Jenkins, Nathaniel W., additional, Parrish, James M., additional, Jadczak, Caroline N., additional, Mohan, Shruthi, additional, Geoghegan, Cara E., additional, and Singh, Kern, additional

Published
2022
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31. Preliminary Evaluation of Standing Full-Length Plain Radiographs Utility in an Adult Degenerative Spine Practice

Author

Parrish, James M., primary, Jenkins, Nathaniel W., additional, Haws, Brittany E., additional, Cha, Elliot D. K., additional, Lynch, Conor P., additional, Patel, Madhav R., additional, Jacob, Kevin C., additional, Jadczak, Caroline N., additional, Mohan, Shruthi, additional, Geoghegan, Cara E., additional, Louie, Philip K., additional, Hirsch, Brandon P., additional, Colman, Matthew W., additional, and Singh, Kern, additional

Published
2021
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36. Multimodal Analgesic Management for Lumbar Decompression Surgery in the Ambulatory Setting: Clinical Case Series and Review of the Literature

Author

Nolte, Michael T., primary, Parrish, James M., additional, Jenkins, Nathaniel W., additional, Cha, Elliot D.K., additional, Lynch, Conor P., additional, Jacob, Kevin C., additional, Patel, Madhav R., additional, Jadczak, Caroline N., additional, Geoghegan, Cara E., additional, Mohan, Shruthi, additional, Podnar, Jeffrey, additional, Buvanendran, Asokumar, additional, and Singh, Kern, additional

Published
2021
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42. Patients With Opioid Use Disorder Have Increased Readmission Rates, Emergency Room Visits, and Costs Following a Hallux Valgus Procedure.

Author

Parrish, James M., Vakharia, Rushabh M., Benson, Dillon C., Hoyt, Aaron K., Jenkins, Nathaniel W., Kaplan, Jonathan R. M., Rush III, Augustus J., Roche, Martin W., and Aiyer, Amiethab A.

Abstract

Background: Patients with a history of opioid use disorder (OUD) tend to have more complications, higher readmission rates, and increased costs following orthopaedic procedures. This study evaluated patients undergoing hallux valgus correction for their odds of increased (1) readmission rates, (2) emergency room (ER) visits, and (3) costs. Methods: Patients undergoing hallux valgus corrections with OUD history were identified using a national Medicare administrative claims database of approximately 24 million orthopaedic surgery patients. OUD patients were matched to non–opioid use disorder (NUD) patients in a 1:4 ratio by age, sex, Elixhauser-Comorbidity Index (ECI), diabetes mellitus, hyperlipidemia, hypertension, and tobacco use. The query yielded 6318 patients (OUD = 1276; NUD = 5042) who underwent a hallux valgus correction. Primary outcomes analyzed included odds of 90-day readmission rates, 30-day ER visits, and 90-day episode-of-care costs. Demographics, odds ratios (ORs), ECI, and cost were assessed as appropriate using a Pearson χ2 test, logistic regression, and a t test. A P value <.05 was considered statistically significant. Results: There were no significant differences in demographics between OUD and NUD patients. OUD patients had higher incidence and odds of 90-day readmission (9.56% vs 6.04%; OR = 1.55; P <.001) and 30-day ER visits (0.86% vs 0.35%; OR = 2.42; P =.021) and incurred greater 90-day episode-of-care costs ($7208.28 vs $6134.75; P <.001) compared with NUD patient controls. Conclusion: The study demonstrates the possible influence of OUD on higher odds of readmission, ER visits, and costs following a hallux valgus correction. Levels of Evidence: Level III: Retrospective cohort study [ABSTRACT FROM AUTHOR]

Published
2022
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47. Patients with Opioid Use Disorder Have Increased Readmission Rates, Emergency Room Visits, and Costs Following Hallux Valgus Procedures

Author

Parrish, James M., Vakharia, Rushabh M., Benson, Dillon, Hoyt, Aaron K., Jenkins, Nathaniel W., Kaplan, Jonathan R., Rush, Augustus J., Roche, Martin W., and Aiyer, Amiethab A.

Subjects
lcsh:RD701-811, lcsh:Orthopedic surgery, Cost, Pain, Hallux Valgus, Article
Abstract

Category: Ankle; Bunion; Midfoot/Forefoot; Other Introduction/Purpose: Patients with a history of opioid use disorder (OUD) have been shown to incur more severe medical complications, higher readmission rates, and increased cost following various orthopedic procedures. There is a scarcity in the literature investigating the effects of OUD following a hallux valgus procedure. Therefore, the purpose of this study was to evaluate whether OUD patients undergoing a hallux valgus correction are at greater odds of: 1) readmission rates, 2) emergency room (ER) visits, and 3) costs. Methods: Patients undergoing a hallux valgus correction with a history of OUD were identified using a health insurance claims database. To search for patients undergoing hallux valgus surgical corrections the following current procedural terminology (CPT) medical codes were used: 28290, 28292, 28293, 28294, 28296, 28297, 28298, 28299, 28306, and 28307. To include all eligible patients the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes were also utilized, including: 77.51 and 77.59. OUD patients were matched to non-opioid use disorder (NUD) patients in a 1:4 ratio by age, sex, Elixhauser-Comorbidity Index (ECI), diabetes mellitus, hyperlipidemia, hypertension, and tobacco use. Primary outcomes analyzed included odds of 90-day readmission rates, 30-day emergency room visit, and 90-day episode of care costs. Pearson’s chi-squared test was used to compare demographics. Logistic regression analysis calculated odds-ratios for readmission and ER visits. Welch’s t-test was used for significance in ECI and cost between cohorts. Results: The query yielded 6,318 patients (OUD=1,276; NUD=5,042) who underwent a hallux valgus correction. There were no significant difference between any of the compared age ranges (p=0.859, p=0.952, p=0.909, p=0.961), ECI (p=1.000) and comorbidities between OUD and NUD patients. OUD patients had higher incidence and odds of 90-day readmission rates (9.56 vs. 6.04%; OR: 1.55, pConclusion: Patients with a history of OUD who underwent a hallux valgus correction had a higher odds ratio of 90-day readmission rates, 30-day emergency room visits, and 90-day total global episode of care cost compared to those with NUD. These findings expand on a rapidly growing body of current literature that demonstrate OUD increases numerous costs associated with outcomes of foot and ankle surgery. OUD is a risk factor for poor post operative outcomes for patients undergoing a hallux valgus correction. The findings of this study are likely to improve preoperative counseling and selection when addressing patients with preoperative opioid use. [Table: see text]

Published
2020

50. The influence of cognitive behavioral therapy on lumbar spine surgery outcomes: a systematic review and meta-analysis

Author

Parrish, James M., primary, Jenkins, Nathaniel W., additional, Parrish, Manasi S., additional, Cha, Elliot D. K., additional, Lynch, Conor P., additional, Massel, Dustin H., additional, Hrynewycz, Nadia M., additional, Mohan, Shruthi, additional, Geoghegan, Cara E., additional, Jadczak, Caroline N., additional, Westrick, Jennifer, additional, Van Horn, Rebecca, additional, and Singh, Kern, additional

Published
2021
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