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Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction
PURPOSE: Osteoarthritis (OA) is a degenerative disease of joints. Currently, there is still a lack of effective tools to predict the long-term efficacy of surgical treatment of OA. The purpose of this study was to explore the prognostic factors of endoscopic surgery for OA and to predict the long-te...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467768/ https://www.ncbi.nlm.nih.gov/pubmed/36105246 http://dx.doi.org/10.1155/2022/1799177 |
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author | Su, Qi Xu, Guokang |
author_facet | Su, Qi Xu, Guokang |
author_sort | Su, Qi |
collection | PubMed |
description | PURPOSE: Osteoarthritis (OA) is a degenerative disease of joints. Currently, there is still a lack of effective tools to predict the long-term efficacy of surgical treatment of OA. The purpose of this study was to explore the prognostic factors of endoscopic surgery for OA and to predict the long-term efficacy of this type of surgery for OA by establishing a prognostic model. METHODS: Baseline and follow-up data on 236 OA patients who underwent surgery in our hospital from January 2017 to December 2021 were selected and patients were randomly assigned to a training set (n = 165) and a test set (n = 71). The Pearson correlation coefficient was used to analyze the correlation between features. Feature selection was performed by recursive feature elimination (RFE) and linear regression. K-means clustering analysis was performed on the selected features to obtain the number of output layers. Finally, a single hidden layer error backpropagation (BP)-artificial neural network (ANN) model was established on the training set, and receiver operating characteristic (ROC) curve was drawn on the test set for verification. RESULTS: Correlation analysis revealed no redundancy among features. RFE and linear regression screened out the features associated with postoperative prognosis under endoscopic surgery: sex, age, BMI, region, morning stiffness time, step count, and osteophyte area. K-means clustering yielded that the optimal number of categories was three, the same as the number of categories for the outcome variable. Therefore, a 7-1-3 BP neural network model was established based on these 7 features, and this model could predict the postoperative situation within one year to a relatively accurate extent: area under curve values (AUC) were 0.814, 0.700, and 0.761 in patients with worse, unchanged, and improved conditions one year after surgery, respectively, higher than the multiclass AUC value (0.646). CONCLUSION: The prognostic model of endoscopic surgery for OA constructed in this study can well predict the disease progression of patients within one year after surgery. |
format | Online Article Text |
id | pubmed-9467768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94677682022-09-13 Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction Su, Qi Xu, Guokang Comput Math Methods Med Research Article PURPOSE: Osteoarthritis (OA) is a degenerative disease of joints. Currently, there is still a lack of effective tools to predict the long-term efficacy of surgical treatment of OA. The purpose of this study was to explore the prognostic factors of endoscopic surgery for OA and to predict the long-term efficacy of this type of surgery for OA by establishing a prognostic model. METHODS: Baseline and follow-up data on 236 OA patients who underwent surgery in our hospital from January 2017 to December 2021 were selected and patients were randomly assigned to a training set (n = 165) and a test set (n = 71). The Pearson correlation coefficient was used to analyze the correlation between features. Feature selection was performed by recursive feature elimination (RFE) and linear regression. K-means clustering analysis was performed on the selected features to obtain the number of output layers. Finally, a single hidden layer error backpropagation (BP)-artificial neural network (ANN) model was established on the training set, and receiver operating characteristic (ROC) curve was drawn on the test set for verification. RESULTS: Correlation analysis revealed no redundancy among features. RFE and linear regression screened out the features associated with postoperative prognosis under endoscopic surgery: sex, age, BMI, region, morning stiffness time, step count, and osteophyte area. K-means clustering yielded that the optimal number of categories was three, the same as the number of categories for the outcome variable. Therefore, a 7-1-3 BP neural network model was established based on these 7 features, and this model could predict the postoperative situation within one year to a relatively accurate extent: area under curve values (AUC) were 0.814, 0.700, and 0.761 in patients with worse, unchanged, and improved conditions one year after surgery, respectively, higher than the multiclass AUC value (0.646). CONCLUSION: The prognostic model of endoscopic surgery for OA constructed in this study can well predict the disease progression of patients within one year after surgery. Hindawi 2022-09-05 /pmc/articles/PMC9467768/ /pubmed/36105246 http://dx.doi.org/10.1155/2022/1799177 Text en Copyright © 2022 Qi Su and Guokang Xu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Su, Qi Xu, Guokang Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction |
title | Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction |
title_full | Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction |
title_fullStr | Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction |
title_full_unstemmed | Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction |
title_short | Endoscopic Surgical Treatment of Osteoarthritis and Prognostic Model Construction |
title_sort | endoscopic surgical treatment of osteoarthritis and prognostic model construction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467768/ https://www.ncbi.nlm.nih.gov/pubmed/36105246 http://dx.doi.org/10.1155/2022/1799177 |
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