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Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women

BACKGROUND: The FRAX® tool estimates the risk of a fragility fracture among the population and many countries have been evaluating its performance among their populations since its creation in 2007. The purpose of this study is to update the first FRIDEX cohort analysis comparing FRAX with the bone...

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Autores principales: Azagra, Rafael, Zwart, Marta, Encabo, Gloria, Aguyé, Amada, Martin-Sánchez, Juan Carlos, Puchol-Ruiz, Nuria, Gabriel-Escoda, Paula, Ortiz-Alinque, Sergio, Gené, Emilio, Iglesias, Milagros, Moriña, David, Diaz-Herrera, Miguel Angel, Utzet, Mireia, Manresa, Josep Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912785/
https://www.ncbi.nlm.nih.gov/pubmed/27317560
http://dx.doi.org/10.1186/s12891-016-1096-6
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author Azagra, Rafael
Zwart, Marta
Encabo, Gloria
Aguyé, Amada
Martin-Sánchez, Juan Carlos
Puchol-Ruiz, Nuria
Gabriel-Escoda, Paula
Ortiz-Alinque, Sergio
Gené, Emilio
Iglesias, Milagros
Moriña, David
Diaz-Herrera, Miguel Angel
Utzet, Mireia
Manresa, Josep Maria
author_facet Azagra, Rafael
Zwart, Marta
Encabo, Gloria
Aguyé, Amada
Martin-Sánchez, Juan Carlos
Puchol-Ruiz, Nuria
Gabriel-Escoda, Paula
Ortiz-Alinque, Sergio
Gené, Emilio
Iglesias, Milagros
Moriña, David
Diaz-Herrera, Miguel Angel
Utzet, Mireia
Manresa, Josep Maria
author_sort Azagra, Rafael
collection PubMed
description BACKGROUND: The FRAX® tool estimates the risk of a fragility fracture among the population and many countries have been evaluating its performance among their populations since its creation in 2007. The purpose of this study is to update the first FRIDEX cohort analysis comparing FRAX with the bone mineral density (BMD) model, and its predictive abilities. METHODS: The discriminatory ability of the FRAX was assessed using the ‘area under curve’ of the receiver operating characteristic (AUC-ROC). Predictive ability was assessed by comparing estimated risk fractures with incidence fractures after a 10-year follow up period. RESULTS: One thousand three hundred eight women ≥ 40 and ≤ 90 years followed up during a 10-year period. The AUC for major osteoporotic fractures using FRAX without DXA was 0.686 (95 % CI 0.630–0.742) and using FN T-score of DXA 0.714 (95 % CI 0.661–0.767). Using only the traditional parameters of DXA (FN T-score), the AUC was 0.706 (95 % CI 0.652–0.760). The AUC for hip osteoporotic fracture was 0.883 (95 % CI 0.827–0.938), 0.857 (95 % CI 0.773–0.941), and 0.814 (95 % CI 0.712–0.916) respectively. For major osteoporotic fractures, the overall predictive value using the ratio Observed fractures/Expected fractures calculated with FRAX without T-score of DXA was 2.29 and for hip fractures 2.28 and with the inclusion of the T-score 2.01 and 1.83 respectively. However, for hip fracture in women < 65 years was 1.53 and 1.24 respectively. CONCLUSIONS: The FRAX tool has been found to show a good discriminatory capacity for detecting women at high risk of fragility fracture, and is better for hip fracture than major fracture. The test of sensibility shows that it is, at least, not inferior than when using BMD model alone. The predictive capacity of FRAX tool needs some adjustment. This capacity is better for hip fracture prediction and better for women < 65 years. Further studies in Catalonia and other regions of Spain are needed to fine tune the FRAX tool’s predictive capability.
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spelling pubmed-49127852016-06-19 Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women Azagra, Rafael Zwart, Marta Encabo, Gloria Aguyé, Amada Martin-Sánchez, Juan Carlos Puchol-Ruiz, Nuria Gabriel-Escoda, Paula Ortiz-Alinque, Sergio Gené, Emilio Iglesias, Milagros Moriña, David Diaz-Herrera, Miguel Angel Utzet, Mireia Manresa, Josep Maria BMC Musculoskelet Disord Research Article BACKGROUND: The FRAX® tool estimates the risk of a fragility fracture among the population and many countries have been evaluating its performance among their populations since its creation in 2007. The purpose of this study is to update the first FRIDEX cohort analysis comparing FRAX with the bone mineral density (BMD) model, and its predictive abilities. METHODS: The discriminatory ability of the FRAX was assessed using the ‘area under curve’ of the receiver operating characteristic (AUC-ROC). Predictive ability was assessed by comparing estimated risk fractures with incidence fractures after a 10-year follow up period. RESULTS: One thousand three hundred eight women ≥ 40 and ≤ 90 years followed up during a 10-year period. The AUC for major osteoporotic fractures using FRAX without DXA was 0.686 (95 % CI 0.630–0.742) and using FN T-score of DXA 0.714 (95 % CI 0.661–0.767). Using only the traditional parameters of DXA (FN T-score), the AUC was 0.706 (95 % CI 0.652–0.760). The AUC for hip osteoporotic fracture was 0.883 (95 % CI 0.827–0.938), 0.857 (95 % CI 0.773–0.941), and 0.814 (95 % CI 0.712–0.916) respectively. For major osteoporotic fractures, the overall predictive value using the ratio Observed fractures/Expected fractures calculated with FRAX without T-score of DXA was 2.29 and for hip fractures 2.28 and with the inclusion of the T-score 2.01 and 1.83 respectively. However, for hip fracture in women < 65 years was 1.53 and 1.24 respectively. CONCLUSIONS: The FRAX tool has been found to show a good discriminatory capacity for detecting women at high risk of fragility fracture, and is better for hip fracture than major fracture. The test of sensibility shows that it is, at least, not inferior than when using BMD model alone. The predictive capacity of FRAX tool needs some adjustment. This capacity is better for hip fracture prediction and better for women < 65 years. Further studies in Catalonia and other regions of Spain are needed to fine tune the FRAX tool’s predictive capability. BioMed Central 2016-06-17 /pmc/articles/PMC4912785/ /pubmed/27317560 http://dx.doi.org/10.1186/s12891-016-1096-6 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Azagra, Rafael
Zwart, Marta
Encabo, Gloria
Aguyé, Amada
Martin-Sánchez, Juan Carlos
Puchol-Ruiz, Nuria
Gabriel-Escoda, Paula
Ortiz-Alinque, Sergio
Gené, Emilio
Iglesias, Milagros
Moriña, David
Diaz-Herrera, Miguel Angel
Utzet, Mireia
Manresa, Josep Maria
Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women
title Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women
title_full Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women
title_fullStr Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women
title_full_unstemmed Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women
title_short Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women
title_sort rationale of the spanish frax model in decision-making for predicting osteoporotic fractures: an update of fridex cohort of spanish women
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912785/
https://www.ncbi.nlm.nih.gov/pubmed/27317560
http://dx.doi.org/10.1186/s12891-016-1096-6
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