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Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty
The ability to accurately predict postoperative outcomes is of considerable interest in the field of orthopaedic surgery. Machine learning has been used as a form of predictive modeling in multiple health-care settings. The purpose of the current study was to determine whether machine learning algor...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Journal of Bone and Joint Surgery, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352606/ https://www.ncbi.nlm.nih.gov/pubmed/34386682 http://dx.doi.org/10.2106/JBJS.OA.20.00128 |
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author | McLendon, Paul B. Christmas, Kaitlyn N. Simon, Peter Plummer, Otho R. Hunt, Audrey Ahmed, Adil S. Mighell, Mark A. Frankle, Mark A. |
author_facet | McLendon, Paul B. Christmas, Kaitlyn N. Simon, Peter Plummer, Otho R. Hunt, Audrey Ahmed, Adil S. Mighell, Mark A. Frankle, Mark A. |
author_sort | McLendon, Paul B. |
collection | PubMed |
description | The ability to accurately predict postoperative outcomes is of considerable interest in the field of orthopaedic surgery. Machine learning has been used as a form of predictive modeling in multiple health-care settings. The purpose of the current study was to determine whether machine learning algorithms using preoperative data can predict improvement in American Shoulder and Elbow Surgeons (ASES) scores for patients with glenohumeral osteoarthritis (OA) at a minimum of 2 years after shoulder arthroplasty. METHODS: This was a retrospective cohort study that included 472 patients (472 shoulders) diagnosed with primary glenohumeral OA (mean age, 68 years; 56% male) treated with shoulder arthroplasty (431 anatomic total shoulder arthroplasty and 41 reverse total shoulder arthroplasty). Preoperative computed tomography (CT) scans were used to classify patients on the basis of glenoid and rotator cuff morphology. Preoperative and final postoperative ASES scores were used to assess the level of improvement. Patients were separated into 3 improvement ranges of approximately equal size. Machine learning methods that related patterns of these variables to outcome ranges were employed. Three modeling approaches were compared: a model with the use of all baseline variables (Model 1), a model omitting morphological variables (Model 2), and a model omitting ASES variables (Model 3). RESULTS: Improvement ranges of ≤28 points (class A), 29 to 55 points (class B), and >55 points (class C) were established. Using all follow-up time intervals, Model 1 gave the most accurate predictions, with probability values of 0.94, 0.95, and 0.94 for classes A, B, and C, respectively. This was followed by Model 2 (0.93, 0.80, and 0.73) and Model 3 (0.77, 0.72, and 0.71). CONCLUSIONS: Machine learning can accurately predict the level of improvement after shoulder arthroplasty for glenohumeral OA. This may allow physicians to improve patient satisfaction by better managing expectations. These predictions were most accurate when latent variables were combined with morphological variables, suggesting that both patients’ perceptions and structural pathology are critical to optimizing outcomes in shoulder arthroplasty. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence. |
format | Online Article Text |
id | pubmed-8352606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Journal of Bone and Joint Surgery, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83526062021-08-11 Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty McLendon, Paul B. Christmas, Kaitlyn N. Simon, Peter Plummer, Otho R. Hunt, Audrey Ahmed, Adil S. Mighell, Mark A. Frankle, Mark A. JB JS Open Access Scientific Articles The ability to accurately predict postoperative outcomes is of considerable interest in the field of orthopaedic surgery. Machine learning has been used as a form of predictive modeling in multiple health-care settings. The purpose of the current study was to determine whether machine learning algorithms using preoperative data can predict improvement in American Shoulder and Elbow Surgeons (ASES) scores for patients with glenohumeral osteoarthritis (OA) at a minimum of 2 years after shoulder arthroplasty. METHODS: This was a retrospective cohort study that included 472 patients (472 shoulders) diagnosed with primary glenohumeral OA (mean age, 68 years; 56% male) treated with shoulder arthroplasty (431 anatomic total shoulder arthroplasty and 41 reverse total shoulder arthroplasty). Preoperative computed tomography (CT) scans were used to classify patients on the basis of glenoid and rotator cuff morphology. Preoperative and final postoperative ASES scores were used to assess the level of improvement. Patients were separated into 3 improvement ranges of approximately equal size. Machine learning methods that related patterns of these variables to outcome ranges were employed. Three modeling approaches were compared: a model with the use of all baseline variables (Model 1), a model omitting morphological variables (Model 2), and a model omitting ASES variables (Model 3). RESULTS: Improvement ranges of ≤28 points (class A), 29 to 55 points (class B), and >55 points (class C) were established. Using all follow-up time intervals, Model 1 gave the most accurate predictions, with probability values of 0.94, 0.95, and 0.94 for classes A, B, and C, respectively. This was followed by Model 2 (0.93, 0.80, and 0.73) and Model 3 (0.77, 0.72, and 0.71). CONCLUSIONS: Machine learning can accurately predict the level of improvement after shoulder arthroplasty for glenohumeral OA. This may allow physicians to improve patient satisfaction by better managing expectations. These predictions were most accurate when latent variables were combined with morphological variables, suggesting that both patients’ perceptions and structural pathology are critical to optimizing outcomes in shoulder arthroplasty. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence. Journal of Bone and Joint Surgery, Inc. 2021-03-26 /pmc/articles/PMC8352606/ /pubmed/34386682 http://dx.doi.org/10.2106/JBJS.OA.20.00128 Text en Copyright © 2021 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Scientific Articles McLendon, Paul B. Christmas, Kaitlyn N. Simon, Peter Plummer, Otho R. Hunt, Audrey Ahmed, Adil S. Mighell, Mark A. Frankle, Mark A. Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty |
title | Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty |
title_full | Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty |
title_fullStr | Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty |
title_full_unstemmed | Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty |
title_short | Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty |
title_sort | machine learning can predict level of improvement in shoulder arthroplasty |
topic | Scientific Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352606/ https://www.ncbi.nlm.nih.gov/pubmed/34386682 http://dx.doi.org/10.2106/JBJS.OA.20.00128 |
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