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Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization

Nature Medicine volume 26, pages 77–82 (2020)Cite this article

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Abstract

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3,4,5,6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50–90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012–2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.

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Fig. 1
Fig. 2: Receiver operating characteristic curves for the major osteoporotic fracture and hip fracture outcomes.
Fig. 3: Calibration plots for the major osteoporotic fracture and hip fracture outcomes.

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Data availability

The study protocol can be shared upon request. Access to the data used for this study can be made available upon request, subject to an internal review by N.D. and R.D.B. to ensure that participant privacy is protected, and subject to completion of a data sharing agreement, approval from the institutional review board of Clalit Health Services and institutional guidelines and in accordance with the current data sharing guidelines of Clalit Health Services and Israeli law. Pending the aforementioned approvals, data sharing will be made in a secure setting, on a per-case-specific manner from the chief information security officer of Clalit Health Services. Please submit such requests to N.D. (noada@clalit.org.il).

Code availability

Requests for the statistical code will be considered by the authors according to the stated need and dependent on specific approval by the information security office of Clalit Health Services.

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Acknowledgements

We thank S. Krispin for her assistance in editing and reviewing the manuscript. This study was supported by a grant given to Clalit Health Services and Zebra Medical Vision by the Israel Innovation Authority (Ministry of Social Equality), to promote digitalized transformation in health care (grant number 64727). The Israel Innovation Authority did not have any active involvement in the study process.

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Author notes

  1. These authors contributed equally: Noa Dagan, Eldad Elnekave.

Authors and Affiliations

  1. Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel

    Noa Dagan, Noam Barda & Ran D. Balicer

  2. Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel

    Noa Dagan, Noam Barda & Eitan Bachmat

  3. School of Public Health, Ben-Gurion University, Beer Sheva, Israel

    Noa Dagan, Noam Barda & Ran D. Balicer

  4. Department of Diagnostic Radiology, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel

    Eldad Elnekave

  5. Zebra Medical Vision, Ltd, Shefayim, Israel

    Eldad Elnekave, Orna Bregman-Amitai, Amir Bar & Mila Orlovsky

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Contributions

N.D., N.B., E.E., E.B. and R.D.B. conceived and designed the study. N.D. extracted the data and performed all the statistical analysis. N.D. and N.B. interpreted the data. N.D. and E.E. drafted the manuscript. O.B.A., A.B. and M.O. contributed the formation of the image processing algorithms. All authors critically revised the manuscript. R.D.B. and E.B. supervised the study and are the guarantors.

Corresponding author

Correspondence to Noa Dagan.

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Competing interests

E.B. has no interests to disclose. N.D., N.B. and R.D.B. report having received grants from the Israel Innovation Authority during the conduct of this study. The parent company of Clalit Research Institute (N.D., N.B. and R.D.B.) owns a minority share in Zebra Medical Vision. E.E., O.B.A., A.B. and M.O. report personal fees from Zebra Medical Vision during the conduct of this study. In addition, E.E. and O.B.A. have a patent for emulating DXA scores based on CT images (WO2016013005A3) and a patent to predict osteoporotic fracture risk (WO2016013004A1).

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Dagan, N., Elnekave, E., Barda, N. et al. Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med 26, 77–82 (2020). https://doi.org/10.1038/s41591-019-0720-z

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