Opportunistic CT screening predicts individuals at risk of major osteoporotic fracture

Abstract

Summary

Millions of CT scans are performed annually and could be also used to opportunistically assess musculoskeletal health; however, it is unknown how well this secondary assessment relates to osteoporotic fracture. This study demonstrates that opportunistic CT screening is a promising tool to predict individuals with previous osteoporotic fracture.

Introduction

Opportunistic computed tomography (oCT) screening for osteoporosis and fracture risk determination complements current dual X-ray absorptiometry (DXA) diagnosis. This study determined major osteoporotic fracture prediction by oCT at the spine and hip from abdominal CT scans.

Methods

Initial 1158 clinical abdominal CT scans were identified from administrative databases and were the basis to generate a cohort of 490 men and women with suitable abdominal CT scans. Participant CT scans met the following criteria: over 50 years of age, the scan had no image artifacts, and the field-of-view included the L4 vertebra and proximal femur. A total of 123 participants were identified as having previously suffered a fracture within 5 years of CT scan date. Fracture cause was identified from clinical data and used to create a low-energy fracture sub-cohort. At each skeletal site, bone mineral density (BMD) and finite element (FE)-estimated bone strength were determined. Logistic regression predicted fracture and receiver-operator characteristic curves analyzed prediction capabilities.

Results

In participants with a fracture, low-energy fractures occurred in 88% of women and 79% of men. Fracture prediction by combining both BMD and FE-estimated bone strength was not statistically different than using either BMD or FE-estimated bone strength alone. Predicting low-energy fractures in women determined the greatest AUC of 0.710 by using both BMD and FE-estimated bone strength.

Conclusions

oCT screening using abdominal CT scans is effective at predicting individuals with previous fracture at major osteoporotic sites and offers a promising screening tool for skeletal health assessment.

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Abbreviations

aBMD:

Areal bone mineral density

AUC:

Area under the receiver-operator characteristic curve

BMD:

Bone mineral density

CT:

Computed tomography

DXA:

Dual X-ray absorptiometry

FE:

Finite element

ICD-10:

International Classification of Diseases 10th Revision

KUB:

Kidney, urinary, and bladder

oCT:

Opportunistic computed tomography

ROC:

Receiver-operator characteristic curve

References

  1. 1.

    Bouxsein ML, Kaufman J, Tosi L, Cummings S, Lane J, Johnell O (2004) Recommendations for optimal care of the fragility fracture patient to reduce the risk of future fracture. J Am Acad Orthop Surg 12:385–395. https://doi.org/10.5435/00124635-200411000-00003

    Article  PubMed  Google Scholar 

  2. 2.

    Budhia S, Mikyas Y, Tang M, Badamgarav E (2012) Osteoporotic fractures: a systematic review of U.S. healthcare costs and resource utilization. PharmacoEconomics 30:147–170. https://doi.org/10.2165/11596880-000000000-00000

    Article  PubMed  Google Scholar 

  3. 3.

    Cauley JA, Thompson DE, Ensrud KC, Scott JC, Black D (2000) Risk of mortality following clinical fractures. Osteoporos Int 11:556–561. https://doi.org/10.1007/s001980070075

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Kanis JA, Oden A, Johnell O, Jonsson B, de Laet C, Dawson A (2001) The burden of osteoporotic fractures: a method for setting intervention thresholds. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 12:417–427. https://doi.org/10.1007/s001980170112

    CAS  Article  Google Scholar 

  5. 5.

    O’Malley CD, Johnston SS, Lenhart G et al (2011) Trends in dual-energy X-ray absorptiometry in the United States, 2000–2009. J Clin Densitom 14:100–107. https://doi.org/10.1016/j.jocd.2011.03.003

    Article  PubMed  Google Scholar 

  6. 6.

    Sinclair A, Morrison A, Young C, Pyke L (2018) The Canadian Medical Imaging Inventory, 2017. CADTH, Ottawa

    Google Scholar 

  7. 7.

    Schileo E, Balistreri L, Grassi L, Cristofolini L, Taddei F (2014) To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations? J Biomech 47:3531–3538. https://doi.org/10.1016/j.jbiomech.2014.08.024

    Article  PubMed  Google Scholar 

  8. 8.

    Nishiyama KK, Gilchrist S, Guy P, Cripton P, Boyd SK (2013) Proximal femur bone strength estimated by a computationally fast finite element analysis in a sideways fall configuration. J Biomech 46:1231–1236. https://doi.org/10.1016/j.jbiomech.2013.02.025

    Article  PubMed  Google Scholar 

  9. 9.

    Crawford RP, Cann CE, Keaveny TM (2003) Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography. Bone 33:744–750

    Article  Google Scholar 

  10. 10.

    Keyak JH, Kaneko TS, Tehranzadeh J, Skinner HB (2005) Predicting proximal femoral strength using structural engineering models. Clin Orthop Relat Res:219–228

  11. 11.

    Lee SJ, Graffy PM, Zea RD, Ziemlewicz TJ, Pickhardt PJ (2018) Future osteoporotic fracture risk related to lumbar vertebral trabecular attenuation measured at routine body CT. J Bone Miner Res 33:860–867

    CAS  Article  Google Scholar 

  12. 12.

    Emohare O, Cagan A, Morgan R, Davis R, Asis M, Switzer J, Polly DW Jr (2014) The use of computed tomography attenuation to evaluate osteoporosis following acute fractures of the thoracic and lumbar vertebra. Geriatr Orthop Surg Rehabil 5:50–55. https://doi.org/10.1177/2151458514525042

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Emohare O, Cagan A, Polly DW, Gertner E (2015) Opportunistic computed tomography screening shows a high incidence of osteoporosis in ankylosing spondylitis patients with acute vertebral fractures. J Clin Densitom 18:17–21. https://doi.org/10.1016/j.jocd.2014.07.006

    Article  PubMed  Google Scholar 

  14. 14.

    Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N (2013) Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 158:588–595. https://doi.org/10.7326/0003-4819-158-8-201304160-00003

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Black DM, Bouxsein ML, Marshall LM, Cummings SR, Lang TF, Cauley JA, Ensrud KE, Nielson CM, Orwoll ES, Osteoporotic Fractures in Men (MrOS) Research Group (2008) Proximal femoral structure and the prediction of hip fracture in men: a large prospective study using QCT. J Bone Miner Res 23:1326–1333. https://doi.org/10.1359/jbmr.080316

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Bousson VD, Adams J, Engelke K, Aout M, Cohen-Solal M, Bergot C, Haguenauer D, Goldberg D, Champion K, Aksouh R, Vicaut E, Laredo JD (2011) In vivo discrimination of hip fracture with quantitative computed tomography: results from the prospective European Femur Fracture Study (EFFECT). J Bone Miner Res Off J Am Soc Bone Miner Res 26:881–893. https://doi.org/10.1002/jbmr.270

    Article  Google Scholar 

  17. 17.

    Cheng X, Li J, Lu Y, Keyak J, Lang T (2007) Proximal femoral density and geometry measurements by quantitative computed tomography: association with hip fracture. Bone 40:169–174. https://doi.org/10.1016/j.bone.2006.06.018

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Nishiyama KK, Ito M, Harada A, Boyd SK (2014) Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporos Int 25:619–626. https://doi.org/10.1007/s00198-013-2459-6

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Adams AL, Fischer H, Kopperdahl DL, Lee DC, Black DM, Bouxsein ML, Fatemi S, Khosla S, Orwoll ES, Siris ES, Keaveny TM (2018) Osteoporosis and hip fracture risk from routine computed tomography scans: the Fracture, Osteoporosis, and CT Utilization Study (FOCUS). J Bone Miner Res Off J Am Soc Bone Miner Res 33:1291–1301. https://doi.org/10.1002/jbmr.3423

    Article  Google Scholar 

  20. 20.

    Wang X, Sanyal A, Cawthon PM, Palermo L, Jekir M, Christensen J, Ensrud KE, Cummings SR, Orwoll E, Black DM, for the Osteoporotic Fractures in Men (MrOS) Research Group, Keaveny TM (2012) Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J Bone Miner Res 27:808–816. https://doi.org/10.1002/jbmr.1539

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lee DC, Hoffmann PF, Kopperdahl DL, Keaveny TM (2017) Phantomless calibration of CT scans for measurement of BMD and bone strength-Inter-operator reanalysis precision. Bone 103:325–333. https://doi.org/10.1016/j.bone.2017.07.029

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Lee SJ, Anderson PA, Pickhardt PJ (2017) Predicting future hip fractures on routine abdominal CT using opportunistic osteoporosis screening measures: a matched case-control study. Am J Roentgenol 209:395–402. https://doi.org/10.2214/AJR.17.17820

    Article  Google Scholar 

  23. 23.

    Michalski AS, Besler BA, Michalak GJ, Boyd SK (2020) CT-based internal density calibration for opportunistic skeletal assessment using abdominal CT scans. Med Eng Phys 78:55–63. https://doi.org/10.1016/j.medengphy.2020.01.009

    Article  PubMed  Google Scholar 

  24. 24.

    Mueller DK, Kutscherenko A, Bartel H, Vlassenbroek A, Ourednicek P, Erckenbrecht J (2011) Phantom-less QCT BMD system as screening tool for osteoporosis without additional radiation. Eur J Radiol 79:375–381. https://doi.org/10.1016/j.ejrad.2010.02.008

    Article  PubMed  Google Scholar 

  25. 25.

    Therkildsen J, Thygesen J, Winther S, Svensson M, Hauge EM, Böttcher M, Ivarsen P, Jørgensen HS (2018) Vertebral bone mineral density measured by quantitative computed tomography with and without a calibration phantom: a comparison between 2 different software solutions. J Clin Densitom Off J Int Soc Clin Densitom 21:367–374. https://doi.org/10.1016/j.jocd.2017.12.003

    Article  Google Scholar 

  26. 26.

    Weaver AA, Beavers KM, Hightower RC, Lynch SK, Miller AN, Stitzel JD (2015) Lumbar bone mineral density phantomless computed tomography measurements and correlation with age and fracture incidence. Traffic Inj Prev 16(Suppl 2):S153–S160. https://doi.org/10.1080/15389588.2015.1054029

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31:1116–1128. https://doi.org/10.1016/j.neuroimage.2006.01.015

    Article  PubMed  Google Scholar 

  28. 28.

    Keaveny TM (2010) Biomechanical computed tomography-noninvasive bone strength analysis using clinical computed tomography scans. Ann N Y Acad Sci 1192:57–65. https://doi.org/10.1111/j.1749-6632.2009.05348.x

    Article  PubMed  Google Scholar 

  29. 29.

    Keyak JH, Koyama AK, LeBlanc A, Lu Y, Lang TF (2009) Reduction in proximal femoral strength due to long-duration spaceflight. Bone 44:449–453. https://doi.org/10.1016/j.bone.2008.11.014

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Michalski AS, Edwards WB, Boyd SK (2019) The influence of reconstruction kernel on bone mineral and strength estimates using quantitative computed tomography and finite element analysis. J Clin Densitom Off J Int Soc Clin Densitom 22:219–228. https://doi.org/10.1016/j.jocd.2017.09.001

    Article  Google Scholar 

  31. 31.

    Keller TS (1994) Predicting the compressive mechanical behavior of bone. J Biomech 27:1159–1168

    CAS  Article  Google Scholar 

  32. 32.

    Lewis G (1997) Properties of acrylic bone cement: state of the art review. J Biomed Mater Res 38:155–182

    CAS  Article  Google Scholar 

  33. 33.

    Pistoia W, van Rietbergen B, Lochmuller EM et al (2002) Estimation of distal radius failure load with micro-finite element analysis models based on three-dimensional peripheral quantitative computed tomography images. Bone 30:842–848

    CAS  Article  Google Scholar 

  34. 34.

    Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35. https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Core Team R (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  36. 36.

    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12:77

    Article  Google Scholar 

  37. 37.

    Melton LJ 3rd, Khosla S, Atkinson EJ et al (2000) Cross-sectional versus longitudinal evaluation of bone loss in men and women. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 11:592–599. https://doi.org/10.1007/s001980070080

    Article  Google Scholar 

  38. 38.

    Riggs BL, Melton Iii LJ, Robb RA et al (2004) Population-based study of age and sex differences in bone volumetric density, size, geometry, and structure at different skeletal sites. J Bone Miner Res 19:1945–1954. https://doi.org/10.1359/JBMR.040916

    Article  PubMed  Google Scholar 

  39. 39.

    Riggs BL, Melton LJ, Robb RA et al (2008) A population-based assessment of rates of bone loss at multiple skeletal sites: evidence for substantial trabecular bone loss in young adult women and men. J Bone Miner Res Off J Am Soc Bone Miner Res 23:205–214. https://doi.org/10.1359/jbmr.071020

    Article  Google Scholar 

  40. 40.

    Keaveny TM, Kopperdahl DL, Melton LJ et al (2010) Age-dependence of femoral strength in white women and men. J Bone Miner Res 25:994–1001. https://doi.org/10.1359/jbmr.091033

    Article  PubMed  Google Scholar 

  41. 41.

    Amin S, Kopperdhal DL, Melton LJ et al (2011) Association of hip strength estimates by finite-element analysis with fractures in women and men. J Bone Miner Res 26:1593–1600. https://doi.org/10.1002/jbmr.347

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Cumming RG, Nevitt MC, Cummings SR (1997) Epidemiology of hip fractures. Epidemiol Rev 19:244–257. https://doi.org/10.1093/oxfordjournals.epirev.a017956

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Kanis JA, Johnell O, Oden A, Sernbo I, Redlund-Johnell I, Dawson A, de Laet C, Jonsson B (2000) Long-term risk of osteoporotic fracture in Malmo. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 11:669–674. https://doi.org/10.1007/s001980070064

    CAS  Article  Google Scholar 

  44. 44.

    Melton LJ, Chrischilles EA, Cooper C et al (1992) Perspective. How many women have osteoporosis? J Bone Miner Res 7:1005–1010. https://doi.org/10.1002/jbmr.5650070902

    Article  PubMed  Google Scholar 

  45. 45.

    Melton LJ 3rd, Atkinson EJ, O’Connor MK et al (1998) Bone density and fracture risk in men. J Bone Miner Res Off J Am Soc Bone Miner Res 13:1915–1923. https://doi.org/10.1359/jbmr.1998.13.12.1915

    Article  Google Scholar 

  46. 46.

    CIHI (2008) In: Canadian Institutes for Health Information (ed) Medical Imaging in Canada, 2007, Ottawa

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

This study was funded in part by the Bob and Nola Rintoul Chair in Bone and Joint Research and a Discovery Grant from the Natural Sciences and Engineering Council (NSERC) of Canada.

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Corresponding author

Correspondence to S.K. Boyd.

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Ethics approval

Data collection and analyses for this study were approved by the University of Calgary Conjoint Health Research Ethics Board (REB 17-1033) with a waiver of informed consent.

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Supplementary information

ESM 1

Flow diagram of participants to define the study cohort from the CT scan identification to the final defined cohorts, using specific inclusion criteria of each CT scan. The number of participants removed from the total 1158 participant cohort was identified using each criterion, leading to the final defined cohort. Some participants are excluded due to multiple criteria. (PDF 37 kb)

ESM 2

Fracture numbers specified by sex and anatomical region (DOCX 13 kb)

ESM 3

Outcomes from the ROC curves using combined parameters and the complete fracture cohort (DOCX 14 kb)

ESM 4

Outcomes from the ROC curves using individual parameters and the complete fracture cohort (DOCX 15 kb)

ESM 5

Outcomes from the ROC curves using individual parameters and the low energy fracture sub-cohort (DOCX 15 kb)

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Michalski, A., Besler, B., Burt, L. et al. Opportunistic CT screening predicts individuals at risk of major osteoporotic fracture. Osteoporos Int (2021). https://doi.org/10.1007/s00198-021-05863-0

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Keywords

  • Bone fracture
  • Osteoporosis
  • Patient-specific modeling
  • X-ray computed tomography