Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression
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There is no consensus on which tool is the most accurate to assess fracture risk. The results of this systematic review suggest that QFracture, Fracture Risk Assessment Tool (FRAX) with BMD, and Garvan with BMD are the tools with the best discriminative ability. More studies assessing the comparative performance of current tools are needed.
Many tools exist to assess fracture risk. This review aims to determine which tools have the best predictive accuracy to identify individuals at high risk of non-traumatic fracture.
Studies assessing the accuracy of tools for prediction of fracture were searched in MEDLINE, EMBASE, Evidence-Based Medicine Reviews, and Global Health. Studies were eligible if discrimination was assessed in a population independent of the derivation cohort. Meta-analyses and meta-regressions were performed on areas under the ROC curve (AUCs). Gender, mean age, age range, and study quality were used as adjustment variables.
We identified 53 validation studies assessing the discriminative ability of 14 tools. Given the small number of studies on some tools, only FRAX, Garvan, and QFracture were compared using meta-regression models. In the unadjusted analyses, QFracture had the best discriminative ability to predict hip fracture (AUC = 0.88). In the adjusted analysis, FRAX with BMD (AUC = 0.81) and Garvan with BMD (AUC = 0.79) had the highest AUCs. For prediction of major osteoporotic fracture, QFracture had the best discriminative ability (AUC = 0.77). For prediction of osteoporotic or any fracture, FRAX with BMD and Garvan with BMD had higher discriminative ability than their versions without BMD (FRAX: AUC = 0.72 vs 0.69, Garvan: AUC = 0.72 vs 0.65). A significant amount of heterogeneity was present in the analyses.
QFracture, FRAX with BMD, and Garvan with BMD have the highest discriminative performance for predicting fracture. Additional studies in which the performance of current tools is assessed in the same individuals may be performed to confirm this conclusion.
KeywordsDiscrimination Fracture Osteoporosis Risk assessment Systematic review Validation
We gratefully aknowledge Vicky Tessier who has reviewed the search strategy.
C Beaudoin has received a scholarship from the CHU de Québec and the Fonds de recherche du Québec-Santé (FRQS).
Compliance with ethical standards
C Beaudoin, S Jean, L Moore, and M Gagné have no conflict of interest to disclose.
L Bessette has received grant/research support from Amgen Inc., BMS, Janssen, UCB, AbbVie, Pfizer, Sanofi, Eli Lilly, and Novartis; has consulted for Amgen Inc., BMS, Janssen, Roche, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Eli Lilly, and Novartis; and is a member of the Speakers’ Bureau for Amgen Inc., BMS, Janssen, Roche, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Eli Lilly, and Novartis.
LG Ste-Marie has received grant/research support from Amgen Inc., has been a member of the advisory board of Amgen Inc. and Eli Lilly, and received other financial supports from AstraZeneca.
JP Brown has received grant/research support from Amgen Inc. and Eli Lilly; has consulted for Amgen Inc., Eli Lilly, and Merck; and is a member of the Speakers’ Bureau for Amgen Inc. and Eli Lilly.
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