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External validation and comparison of three prediction tools for risk of osteoporotic fractures using data from population based electronic health records: retrospective cohort study

BMJ 2017; 356 doi: (Published 19 January 2017) Cite this as: BMJ 2017;356:i6755
  1. Noa Dagan, chief data officer, and PhD student1 2,
  2. Chandra Cohen-Stavi, chief scientific writing officer1,
  3. Maya Leventer-Roberts, deputy director, and adjunct assistant professor1 3,
  4. Ran D Balicer, director, and associate professor1 4
  1. 1Clalit Research Institute, Chief Physician’s Office, Clalit Health Services, Tel Aviv, Israel
  2. 2Computer Science Department, Ben Gurion University of the Negev, Be’er Sheba, Israel
  3. 3Department of Preventive Medicine and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  4. 4Epidemiology Department, Ben Gurion University of the Negev, Be’er Sheba, Israel
  1. Correspondence to: N Dagan noa.dgn{at}
  • Accepted 8 December 2016


Objective To directly compare the performance and externally validate the three most studied prediction tools for osteoporotic fractures—QFracture, FRAX, and Garvan—using data from electronic health records.

Design Retrospective cohort study.

Setting Payer provider healthcare organisation in Israel.

Participants 1 054 815 members aged 50 to 90 years for comparison between tools and cohorts of different age ranges, corresponding to those in each tools’ development study, for tool specific external validation.

Main outcome measure First diagnosis of a major osteoporotic fracture (for QFracture and FRAX tools) and hip fractures (for all three tools) recorded in electronic health records from 2010 to 2014. Observed fracture rates were compared to probabilities predicted retrospectively as of 2010.

Results The observed five year hip fracture rate was 2.7% and the rate for major osteoporotic fractures was 7.7%. The areas under the receiver operating curve (AUC) for hip fracture prediction were 82.7% for QFracture, 81.5% for FRAX, and 77.8% for Garvan. For major osteoporotic fractures, AUCs were 71.2% for QFracture and 71.4% for FRAX. All the tools underestimated the fracture risk, but the average observed to predicted ratios and the calibration slopes of FRAX were closest to 1. Tool specific validation analyses yielded hip fracture prediction AUCs of 88.0% for QFracture (among those aged 30-100 years), 81.5% for FRAX (50-90 years), and 71.2% for Garvan (60-95 years).

Conclusions Both QFracture and FRAX had high discriminatory power for hip fracture prediction, with QFracture performing slightly better. This performance gap was more pronounced in previous studies, likely because of broader age inclusion criteria for QFracture validations. The simpler FRAX performed almost as well as QFracture for hip fracture prediction, and may have advantages if some of the input data required for QFracture are not available. However, both tools require calibration before implementation.


  • We thank our colleagues at the Clalit Research Institute: Sydney Krispin and Carly Davis for their assistance in editing and reviewing the manuscript and Amichay Akriv and Moshe Hoshen for their guidance on the statistical analyses.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

  • Funding: This study was supported internally by the Clalit Research Institute for the design and conduct of the study, data collection, analysis and interpretation of the data, and preparation and review of the manuscript.

  • Contributors: ND, RDB, and ML-R conceived and designed the study. ND, CC-S, and ML-R analysed and interpreted the data. ND and CC-S drafted the manuscript. All authors critically revised the manuscript for important intellectual content. ND carried out the statistical analysis. CC-S provided administrative, technical, and material support. RDB supervised the study and is the guarantor.

  • Data sharing: No additional data available.

  • Ethical approval: This study was approved by the Clalit Health Services research ethics committee.

  • Transparency: The lead author (ND) confirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

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