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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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Abstract

Summary

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.

Introduction

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.

Methods

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.

Results

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.

Conclusions

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.

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Acknowledgements

We gratefully aknowledge Vicky Tessier who has reviewed the search strategy.

Funding

C Beaudoin has received a scholarship from the CHU de Québec and the Fonds de recherche du Québec-Santé (FRQS).

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Authors and Affiliations

  1. Department of Social and Preventive Medicine, Medicine Faculty, Laval University, Ferdinand Vandry Pavillon, 1050 Avenue de la Médecine, Quebec City, QC, G1V 0A6, Canada

    C. Beaudoin & L. Moore

  2. CHU de Québec-Université Laval Research Center, Québec, QC, Canada

    C. Beaudoin, L. Moore, L. Bessette & J. P. Brown

  3. Bureau d’information et d’études en santé des populations, Institut National de Santé Publique du Québec, 945, Avenue Wolfe, Québec, G1V 5B3, Canada

    C. Beaudoin, M. Gagné & S. Jean

  4. Department of Medicine, Medicine Faculty, Laval University, Ferdinand Vandry Pavillon, 1050 Avenue de la Médecine, Quebec City, QC, G1V 0A6, Canada

    L. Bessette, J. P. Brown & S. Jean

  5. Department of Medicine, Medicine Faculty, University of Montréal, Montréal, QC, Canada

    L. G. Ste-Marie

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  1. C. Beaudoin
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Correspondence to C. Beaudoin.

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Competing interests

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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Appendices

Appendix 1. Search strategy for Ovid MEDLINE

Table 9 Search Strategy for Ovid MEDLINE
Full size table

Appendix 2. Quality assessment using a modified version of the QUADAS-2 tool

  1. 1.

    PATIENT SELECTION

    1. a.

      Risk of bias

      • Was a consecutive (ex: administrative database) or random sample of individuals enrolled? Yes, No, Unclear

      • Did the study avoid inappropriate exclusions? Yes, No, Unclear

    2. b.

      Concern regarding applicability

      • Are there concerns that the included individuals and setting do not match the review question (Were participants from the validation cohort representative of individuals from the general population?)? Low, High, Unclear

  2. 2.

    INDEX TEST

    1. a.

      Concern regarding applicability

      • Would the risk factors included in the index test would be available in clinical practice? High, Low, Unclear

  3. 3.

    REFERENCE STANDARD

    1. a.

      Risk of bias

      • Is the reference standard likely to correctly classify the target condition (Were fractures verified or only self-reported?)? Yes, No, Unclear

  4. 4.

    FLOW AND TIMING

    1. a.

      Risk of bias

      • Did all participants receive the same reference standard? Yes, No, Unclear

      • Were at least 90% of eligible participants included in the analysis? Yes, No, Unclear

      • Were more than 10% of missing data set to null or to the average value? Yes, No, Unclear

      • Were all participants followed on the period for which the index test was constructed (Was the study duration (or maximum follow-up time) approximately the same as the length of the period for which the index test was constructed (±1 year)?)? Yes, No, Unclear

      • Were more than 10 fractures by variable observed during the follow-up period? Yes, No, Unclear

Appendix 3. Summary of study quality as assessed using QUADAS-2 tool

Fig. 2
figure 2

Quality of the studies assessing the external validity of tools to identify individuals at high risk of non-traumatic fracture

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Beaudoin, C., Moore, L., Gagné, M. et al. Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression. Osteoporos Int 30, 721–740 (2019). https://doi.org/10.1007/s00198-019-04919-6

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