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Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal

Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Thre...

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Autores principales: Sun, Xuemei, Chen, Yancong, Gao, Yinyan, Zhang, Zixuan, Qin, Lang, Song, Jinlu, Wang, Huan, Wu, Irene XY
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JKL International LLC 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286920/
https://www.ncbi.nlm.nih.gov/pubmed/35855348
http://dx.doi.org/10.14336/AD.2021.1206
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author Sun, Xuemei
Chen, Yancong
Gao, Yinyan
Zhang, Zixuan
Qin, Lang
Song, Jinlu
Wang, Huan
Wu, Irene XY
author_facet Sun, Xuemei
Chen, Yancong
Gao, Yinyan
Zhang, Zixuan
Qin, Lang
Song, Jinlu
Wang, Huan
Wu, Irene XY
author_sort Sun, Xuemei
collection PubMed
description Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.
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spelling pubmed-92869202022-07-18 Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal Sun, Xuemei Chen, Yancong Gao, Yinyan Zhang, Zixuan Qin, Lang Song, Jinlu Wang, Huan Wu, Irene XY Aging Dis Review Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models. JKL International LLC 2022-07-11 /pmc/articles/PMC9286920/ /pubmed/35855348 http://dx.doi.org/10.14336/AD.2021.1206 Text en copyright: © 2022 Sun et al. https://creativecommons.org/licenses/by/2.0/this is an open access article distributed under the terms of the creative commons attribution license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Review
Sun, Xuemei
Chen, Yancong
Gao, Yinyan
Zhang, Zixuan
Qin, Lang
Song, Jinlu
Wang, Huan
Wu, Irene XY
Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal
title Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal
title_full Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal
title_fullStr Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal
title_full_unstemmed Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal
title_short Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal
title_sort prediction models for osteoporotic fractures risk: a systematic review and critical appraisal
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286920/
https://www.ncbi.nlm.nih.gov/pubmed/35855348
http://dx.doi.org/10.14336/AD.2021.1206
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