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Nasiri N, Maazi M, Mehta S, McMullen EP, Pourghadiri A, Croitoru D, Piguet V. Seasonal Trends in Hidradenitis Suppurativa: Data Analysis of the United States and Canada Google Search Patterns. J Cutan Med Surg 2024:12034754241252436. [PMID: 38708559 DOI: 10.1177/12034754241252436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Affiliation(s)
- Nima Nasiri
- Department of Electrical and Computer Engineering (ECE), University of British Columbia, Vancouver, BC, Canada
| | - Mahan Maazi
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shanti Mehta
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Eric P McMullen
- Division of Dermatology, University of Toronto, Toronto, ON, Canada
| | - Amir Pourghadiri
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David Croitoru
- Division of Dermatology, University of Toronto, Toronto, ON, Canada
- Division of Dermatology, Department of Medicine, University Health Network, Toronto ON, Canada
- Division of Dermatology, Department of Medicine, Women's College Hospital, Toronto, ON, Canada
| | - Vincent Piguet
- Division of Dermatology, University of Toronto, Toronto, ON, Canada
- Division of Dermatology, Department of Medicine, Women's College Hospital, Toronto, ON, Canada
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Yao PF, Diao YD, McMullen EP, Manka M, Murphy J, Lin C. Predicting amputation using machine learning: A systematic review. PLoS One 2023; 18:e0293684. [PMID: 37934767 PMCID: PMC10629636 DOI: 10.1371/journal.pone.0293684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/17/2023] [Indexed: 11/09/2023] Open
Abstract
Amputation is an irreversible, last-line treatment indicated for a multitude of medical problems. Delaying amputation in favor of limb-sparing treatment may lead to increased risk of morbidity and mortality. This systematic review aims to synthesize the literature on how ML is being applied to predict amputation as an outcome. OVID Embase, OVID Medline, ACM Digital Library, Scopus, Web of Science, and IEEE Xplore were searched from inception to March 5, 2023. 1376 studies were screened; 15 articles were included. In the diabetic population, models ranged from sub-optimal to excellent performance (AUC: 0.6-0.94). In trauma patients, models had strong to excellent performance (AUC: 0.88-0.95). In patients who received amputation secondary to other etiologies (e.g.: burns and peripheral vascular disease), models had similar performance (AUC: 0.81-1.0). Many studies were found to have a high PROBAST risk of bias, most often due to small sample sizes. In conclusion, multiple machine learning models have been successfully developed that have the potential to be superior to traditional modeling techniques and prospective clinical judgment in predicting amputation. Further research is needed to overcome the limitations of current studies and to bring applicability to a clinical setting.
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Affiliation(s)
- Patrick Fangping Yao
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Yi David Diao
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Eric P. McMullen
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Marlin Manka
- Department of Computer Science, University of Western Ontario, London, ON, Canada
| | - Jessica Murphy
- Division of Physical Medicine and Rehabilitation, McMaster University, Hamilton, ON, Canada
| | - Celina Lin
- Division of Physical Medicine and Rehabilitation, McMaster University, Hamilton, ON, Canada
- Division of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON, Canada
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McMullen EP, Syed SA, Espiritu KD, Grewal RS, Elder GA, Morita PP, Drucker AM. The Therapeutic Applications of Machine Learning in Atopic Dermatitis: A Scoping Review. J Cutan Med Surg 2023:12034754231168846. [PMID: 37073787 DOI: 10.1177/12034754231168846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Affiliation(s)
- Eric P McMullen
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Saad A Syed
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | | | - Rajan S Grewal
- School of Health, University of Waterloo, Waterloo, Ontario, Canada
| | - Geoffrey A Elder
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Plinio P Morita
- School of Health, University of Waterloo, Waterloo, Ontario, Canada
- Research Institute for Aging, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Aaron M Drucker
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Division of Dermatology, Department of Medicine and Women's College Research Institute, Women's College Hospital, Toronto, ON, Canada
- Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada
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McMullen EP, Lee Y, Lipsitz O, Lui LMW, Vinberg M, Ho R, Rodrigues NB, Rosenblat JD, Cao B, Gill H, Teopiz KM, Cha DS, McIntyre RS. Strategies to Prolong Ketamine's Efficacy in Adults with Treatment-Resistant Depression. Adv Ther 2021; 38:2795-2820. [PMID: 33929660 PMCID: PMC8189962 DOI: 10.1007/s12325-021-01732-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/25/2021] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Ketamine treatment is capable of significant and rapid symptom improvement in adults with treatment-resistant depression (TRD). A limitation of ketamine treatment in TRD is the relatively short duration of time to relapse (e.g., median 2-4 weeks). The objective of the systematic review herein is to identify strategies capable of prolonging the acute efficacy of ketamine in adults with TRD. METHODS PubMed/MEDLINE databases were searched from inception to December 2020 for clinical studies written in English using the following key terms: ketamine, prolong, and depression. A total of 454 articles were identified from the literature search which included all clinical studies regarding prolonging the antidepressant effects of ketamine. Twenty-two articles were included: ten randomized controlled trials (RCTs), eight prospective open-label trials, one retrospective chart review, and three case reports. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for data extraction. The primary outcome was prolonged effect, defined as statistically significant antidepressant effects following acute ketamine treatment. RESULTS A total of 454 articles were identified, and 22 articles were included. Different treatment modalites including pharmacological interventions, manualized-based psychotherapies, electroconvulsive therapy, transcranial magnetic stimulation, and intravenous monotherapy were examined to determine their impact on the prolongation of antidepressant effects following acute ketamine treatment. No treatment modality, other than repeat-dose IV ketamine, has demonstrated ability to significantly prolong the acute efficacy of IV ketamine in TRD. CONCLUSION Hitherto, available open-label data and controlled trial data support repeat administration of IV ketamine as an effective strategy to prolong the efficacy of ketamine's antidepressant effects (although not the focus of the study herein, maintenance repeat-dose esketamine treatment is proven effective in esketamine responders). There is a need to identify multimodality strategies that are safe and capable of prolonging the efficacy of ketamine in adults with TRD.
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Affiliation(s)
- Eric P McMullen
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Yena Lee
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Orly Lipsitz
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Leanna M W Lui
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Maj Vinberg
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, Psychiatric Research Unit, University of Copenhagen, Psychiatric Centre North Zealand, Hilleroed, Denmark
| | - Roger Ho
- Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore, Singapore
- Department of Psychological Medicine, National University Hospital, Singapore, Singapore
| | - Nelson B Rodrigues
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Joshua D Rosenblat
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Bing Cao
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Ministry of Education, Southwest University, Chongqing, 400715, People's Republic of China
| | - Hartej Gill
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Kayla M Teopiz
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
| | - Danielle S Cha
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
| | - Roger S McIntyre
- Mood Disorder Psychopharmacology Unit, University Health Network, University of Toronto, 399 Bathurst Street, MP 9-325, Toronto, ON, M5T 2S8, Canada.
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada.
- Brain and Cognition Discovery Foundation, Canada, University of Toronto, Toronto, ON, Canada.
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