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Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis

BACKGROUND: Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is la...

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Autores principales: Zhong, Jingyu, Si, Liping, Zhang, Guangcheng, Huo, Jiayu, Xing, Yue, Hu, Yangfan, Zhang, Huan, Yao, Weiwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131111/
https://www.ncbi.nlm.nih.gov/pubmed/34006309
http://dx.doi.org/10.1186/s13643-021-01683-9
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author Zhong, Jingyu
Si, Liping
Zhang, Guangcheng
Huo, Jiayu
Xing, Yue
Hu, Yangfan
Zhang, Huan
Yao, Weiwu
author_facet Zhong, Jingyu
Si, Liping
Zhang, Guangcheng
Huo, Jiayu
Xing, Yue
Hu, Yangfan
Zhang, Huan
Yao, Weiwu
author_sort Zhong, Jingyu
collection PubMed
description BACKGROUND: Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA. METHODS: The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate. DISCUSSION: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020203543 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-021-01683-9.
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spelling pubmed-81311112021-05-19 Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis Zhong, Jingyu Si, Liping Zhang, Guangcheng Huo, Jiayu Xing, Yue Hu, Yangfan Zhang, Huan Yao, Weiwu Syst Rev Protocol BACKGROUND: Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA. METHODS: The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate. DISCUSSION: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020203543 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-021-01683-9. BioMed Central 2021-05-19 /pmc/articles/PMC8131111/ /pubmed/34006309 http://dx.doi.org/10.1186/s13643-021-01683-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Protocol
Zhong, Jingyu
Si, Liping
Zhang, Guangcheng
Huo, Jiayu
Xing, Yue
Hu, Yangfan
Zhang, Huan
Yao, Weiwu
Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
title Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
title_full Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
title_fullStr Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
title_full_unstemmed Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
title_short Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
title_sort prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131111/
https://www.ncbi.nlm.nih.gov/pubmed/34006309
http://dx.doi.org/10.1186/s13643-021-01683-9
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