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

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.

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.

References

  1. 1.

    Curtis, J. R. et al. Longitudinal trends in use of bone mass measurement among older Americans, 1999–2005. J. Bone Miner. Res. 23, 1061–1067 (2008).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Medical Advisory Secretariat. Utilization of DXA bone mineral densitometry in Ontario: an evidence-based analysis. Ont. Health Technol. Assess. Ser. 6, 1–180 (2006).

    Google Scholar 

  3. 3.

    Kanis, J. A., Johnell, O., Oden, A., Johansson, H. & McCloskey, E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos. Int. 19, 385–397 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Marques, A. et al. The accuracy of osteoporotic fracture risk prediction tools: a systematic review and meta-analysis. Ann. Rheum. Dis. 74, 1958–1967 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Viswanathan, M. et al. Screening to prevent osteoporotic fractures: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 319, 2532–2551 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Beaudoin, C. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Hess, E. P. et al. Trends in computed tomography utilization rates: a longitudinal practice-based study. J. Patient Saf. 10, 52–58 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Levin, D. C., Rao, V. M. & Parker, L. Financial impact of Medicare code bundling of CT of the abdomen and pelvis. AJR Am. J. Roentgenol. 202, 1069–1071 (2014).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Brauer, C. A., Coca-Perraillon, M., Cutler, D. M. & Rosen, A. B. Incidence and mortality of hip fractures in the United States. JAMA 302, 1573–1579 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Dyer, S. M. et al. A critical review of the long-term disability outcomes following hip fracture. BMC Geriatr. 16, 158 (2016).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    National Institute for Health and Care Excellence. Alendronate, Etidronate, Risedronate, Raloxifene and Strontium Ranelate for the Primary Prevention of Osteoporotic Fragility Fractures in Postmenopausal Women (NICE, 2008); https://www.nice.org.uk/guidance/ta160/resources/raloxifene-for-the-primary-prevention-of-osteoporotic-fragility-fractures-in-postmenopausal-women-pdf-82598368491205

  12. 12.

    Huntjens, K. M. et al. Fracture liaison service: impact on subsequent nonvertebral fracture incidence and mortality. J. Bone Joint Surg. Am. 96, e29 (2014).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Cosman, F. et al. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos. Int. 25, 2359–2381 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Korthoewer, D. & Chandran, M. Osteoporosis management and the utilization of FRAX®: a survey amongst health care professionals of the Asia-Pacific. Arch. Osteoporos. 7, 193–200 (2012).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Silverman, S. L. & Calderon, A. D. The utility and limitations of FRAX: a US perspective. Curr. Osteoporos. Rep. 8, 192–197 (2010).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Lewiecki, E. M. Managing osteoporosis: challenges and strategies. Cleve. Clin. J. Med. 76, 457–466 (2009).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Compston, J. et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch. Osteoporos. 12, 43 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Cebul, R. D., Rebitzer, J. B., Taylor, L. J. & Votruba, M. E. Organizational fragmentation and care quality in the US healthcare system. J. Econ. Perspect. 22, 93–113 (2008).

    Google Scholar 

  19. 19.

    Karr, A. F. et al. Comparing record linkage software programs and algorithms using real-world data. PLoS ONE 14, e0221459 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Herring, B. Suboptimal provision of preventive healthcare due to expected enrollee turnover among private insurers. Health Econ. 19, 438–448 (2010).

    Google Scholar 

  21. 21.

    Lee, S., Chung, C. K., Oh, S. H. & Park, S. B. Correlation between bone mineral density measured by dual-energy X-ray absorptiometry and Hounsfield units measured by diagnostic CT in lumbar spine. J. Korean Neurosurg. Soc. 54, 384–389 (2013).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Pickhardt, P. J. et al. Simultaneous screening for osteoporosis at CT colonography: bone mineral density assessment using MDCT attenuation techniques compared with the DXA reference standard. J. Bone Miner. Res. 26, 2194–2203 (2011).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lee, S. J., Anderson, P. A. & Pickhardt, P. J. Predicting future hip fractures on routine abdominal CT using opportunistic osteoporosis screening measures: a matched case-control study. AJR Am. J. Roentgenol. 209, 395–402 (2017).

    Google Scholar 

  24. 24.

    Melton, L. J. 3rd, Atkinson, E. J., Cooper, C., O’Fallon, W. M. & Riggs, B. L. Vertebral fractures predict subsequent fractures. Osteoporos. Int. 10, 214–221 (1999).

    Google Scholar 

  25. 25.

    Summers, R. M. et al. Feasibility of simultaneous computed tomographic colonography and fully automated bone mineral densitometry in a single examination. J. Comput. Assist. Tomogr. 35, 212–216 (2011).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Lee, S. J. et al. Opportunistic screening for osteoporosis using the sagittal reconstruction from routine abdominal CT for combined assessment of vertebral fractures and density. Osteoporos. Int. 27, 1131–1136 (2016).

    CAS  Google Scholar 

  27. 27.

    Steyerberg, E. W. Clinical Prediction Models: a Practical Approach to Development, Validation, and Updating (Springer, 2009).

  28. 28.

    Fraser, L. A. et al. Fracture prediction and calibration of a Canadian FRAX® tool: a population-based report from CaMos. Osteoporos. Int. 22, 829–837 (2011).

    Google Scholar 

  29. 29.

    Pressman, A. R., Lo, J. C., Chandra, M. & Ettinger, B. Methods for assessing fracture risk prediction models: experience with FRAX in a large integrated health care delivery system. J. Clin. Densitom. 14, 407–415 (2011).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Dagan, N., Cohen-Stavi, C., Leventer-Roberts, M. & Balicer, R. D. 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 356, i6755 (2017).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Pickhardt, P. J., Bodeen, G., Brett, A., Brown, J. K. & Binkley, N. Comparison of femoral neck BMD evaluation obtained using Lunar DXA and QCT with asynchronous calibration from CT colonography. J. Clin. Densitom. 18, 5–12 (2015).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Ziemlewicz, T. J. et al. Opportunistic quantitative CT bone mineral density measurement at the proximal femur using routine contrast-enhanced scans: direct comparison with DXA in 355 adults. J. Bone Miner. Res. 31, 1835–1840 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Pickhardt, P. J. et al. Population-based opportunistic osteoporosis screening: validation of a fully automated CT tool for assessing longitudinal BMD changes. Br. J. Radiol. 92, 20180726 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Jang, S. et al. Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20,000 adults. Radiology 291, 360–367 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Pickhardt, P. J. et al. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann. Intern. Med. 158, 588–595 (2013).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Adams, A. L. et al. Osteoporosis and hip fracture risk from routine computed tomography scans: the Fracture, Osteoporosis, and CT Utilization Study (FOCUS). J. Bone Miner. Res. 33, 1291–1301 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Sirota-Cohen, C., Rosipko, B., Forsberg, D. & Sunshine, J. L. Implementation and benefits of a vendor-neutral archive and enterprise-imaging management system in an integrated delivery network. J. Digit. Imaging 32, 211–220 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Nagels, J., Macdonald, D. & Coz, C. Measuring the benefits of a regional imaging environment. J. Digit. Imaging 30, 609–614 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Burge, R. et al. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J. Bone Miner. Res. 22, 465–475 (2007).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Hernlund, E. et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch. Osteoporos. 8, 136 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Gross, R., Rosen, B. & Chinitz, D. Evaluating the Israeli health care reform: strategy, challenges and lessons. Health Policy 45, 99–117 (1998).

    CAS  Google Scholar 

  42. 42.

    Bar, A., Wolf, L., Bergman Amitai, O., Toledano, E. & Elnekave, E. Compression fractures detection on CT. In Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis (eds Armato, S.G. 3rd & Petrick, N. A.) 1013440 (SPIE, 2017).

  43. 43.

    Krishnaraj, A. et al. Simulating dual-energy X-ray absorptiometry in CT using deep-learning segmentation cascade. J. Am. Coll. Radiol. 16, 1473–1479 (2019).

    Google Scholar 

  44. 44.

    Bregman-Armitai, O. & Elnekave, E. Systems and methods for emulating DEXA scores based on CT images. Patent no. WO2016013005A2 (2019); https://patentimages.storage.googleapis.com/aa/dd/d7/a9ac0a3b551f72/WO2016013005A2.pdf

  45. 45.

    van Buuren, S. & Groothuis-Oudshoorn, K. MICE: Multivariate imputation by chained equations. R package version 2.22 https://cloud.r-project.org/web/packages/mice/index.html (2014).

  46. 46.

    Genant, H. K., Wu, C. Y., van Kuijk, C. & Nevitt, M. C. Vertebral fracture assessment using a semiquantitative technique. J. Bone Miner. Res. 8, 1137–1148 (1993).

    CAS  Google Scholar 

  47. 47.

    Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-assisted Intervention (Springer, Cham.) 234–241 (2015).

  48. 48.

    Pickhardt, P. J. et al. Effect of IV contrast on lumbar trabecular attenuation at routine abdominal CT: correlation with DXA and implications for opportunistic osteoporosis screening. Osteoporos. Int. 27, 147–152 (2016).

    CAS  Google Scholar 

  49. 49.

    Marshall, A., Altman, D. G., Holder, R. L. & Royston, P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med. Res. Methodol. 9, 57 (2009).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Harrell, F. E. Jr. rms: Regression modeling strategies. R package version 4.4-0 https://rdrr.io/cran/rms/ (2015).

  51. 51.

    Walker, E. & Nowacki, A. S. Understanding equivalence and noninferiority testing. J. Gen. Intern. Med. 26, 192–196 (2011).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: Visualizing the performance of scoring classifiers. R package version 1.0-7 https://rdrr.io/cran/ROCR/ (2015).

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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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Authors

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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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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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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