Evidence map›Paper›PMID 28104610›Full record

ArticleBMJ (Clinical research ed.)2017

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

Noa Dagan, Chandra Cohen-Stavi, Maya Leventer-Roberts, Ran D Balicer

Abstract readComparative StudyValidation Study
In one paragraph

Article in BMJ (Clinical research ed.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

61 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2019
    Pooled it
  7. Review
  8. Article
  9. Article
  10. Screening for Osteoporosis in Nonagenarians: A Scoping Review.Journal of the American Geriatrics Society · 2025
    Article
  11. Review
  12. Predictive capacity of fracture risk assessment tools: overview of systematic reviews.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Review
  13. Review
  14. Bone Health, Fragility Fractures, and the Hand Surgeon.Journal of hand surgery global online · 2025
    Review
  15. Biases in the performance of FRAX without BMD in predicting fracture risk in a multiethnic population with diabetes: the Diabetes and Aging Study.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025
    Article
  16. Article
  17. Article
  18. An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024
    Article
  19. Race-specific FRAX models are evidence-based and support equitable care: a response to the ASBMR Task Force report on Clinical Algorithms for Fracture Risk.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024
    Article
  20. Article

1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Noa DaganClalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel Aviv, Israel noa.dgn@gmail.com.
Chandra Cohen-StaviClalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel Aviv, Israel.
Maya Leventer-RobertsClalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel Aviv, Israel.
Ran D BalicerClalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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.

designRetrospective cohort study.

settingPayer provider healthcare organisation in Israel.

participants1 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 measureFirst 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.

resultsThe 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).

conclusionsBoth 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.

Indexed as

AlgorithmsAgedAged, 80 and overAge FactorsBone DensityCalibrationElectronic Health RecordsFemaleHip FracturesHumansIsraelMaleMiddle AgedOsteoporotic FracturesReproducibility of ResultsRetrospective Studies

Identifiers

PMID28104610
PMCPMC5244817

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.