Top

Osteoporosis International

Published in:

01-03-2021 | Osteoporosis | Original Article

Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis

Authors: B. C. S. de Vries, J. H. Hegeman, W. Nijmeijer, J. Geerdink, C. Seifert, C. G. M. Groothuis-Oudshoorn

Published in: Osteoporosis International | Issue 3/2021

Abstract

Summary

Four machine learning models were developed and compared to predict the risk of a future major osteoporotic fracture (MOF), defined as hip, wrist, spine and humerus fractures, in patients with a prior fracture. We developed a user-friendly tool for risk calculation of subsequent MOF in osteopenia patients, using the best performing model.

Introduction

Major osteoporotic fractures (MOFs), defined as hip, wrist, spine and humerus fractures, can have serious consequences regarding morbidity and mortality. Machine learning provides new opportunities for fracture prediction and may aid in targeting preventive interventions to patients at risk of MOF. The primary objective is to develop and compare several models, capable of predicting the risk of MOF as a function of time in patients seen at the fracture and osteoporosis outpatient clinic (FO-clinic) after sustaining a fracture.

Methods

Patients aged > 50 years visiting an FO-clinic were included in this retrospective study. We compared discriminative ability (concordance index) for predicting the risk on MOF with a Cox regression, random survival forests (RSF) and an artificial neural network (ANN)-DeepSurv model. Missing data was imputed using multiple imputations by chained equations (MICE) or RSF’s imputation function. Analyses were performed for the total cohort and a subset of osteopenia patients without vertebral fracture.

Results

A total of 7578 patients were included, 805 (11%) patients sustained a subsequent MOF. The highest concordance-index in the total dataset was 0.697 (0.664–0.730) for Cox regression; no significant difference was determined between the models. In the osteopenia subset, Cox regression outperformed RSF (p = 0.043 and p = 0.023) and ANN-DeepSurv (p = 0.043) with a c-index of 0.625 (0.562–0.689). Cox regression was used to develop a MOF risk calculator on this subset.

Conclusion

We show that predicting the risk of MOF in patients who already sustained a fracture can be done with adequate discriminative performance. We developed a user-friendly tool for risk calculation of subsequent MOF in patients with osteopenia.

Available only for authorised users

Determined with the CKD-EPI formula

Odén A, McCloskey EV, Kanis JA, Harvey NC, Johansson H (2015) Burden of high fracture probability worldwide: secular increases 2010–2040. Osteoporos Int 26:2243–2248. https://doi.org/10.1007/s00198-015-3154-6CrossRefPubMed

Johnell O, Kanis JA (2006) An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int 17:1726–1733. https://doi.org/10.1007/s00198-006-0172-4CrossRefPubMed

Briot K, Paternotte S, Kolta S, Eastell R, Felsenberg D, Reid DM, Glüer CC, Roux C (2013) FRAX®: prediction of major osteoporotic fractures in women from the general population: the OPUS study. PLoS One 8:e83436. https://doi.org/10.1371/journal.pone.0083436CrossRefPubMedPubMedCentral

Jürisson M, Pisarev H, Kanis J, Borgström F, Svedbom A, Kallikorm R, Lember M, Uusküla A (2016) Quality of life, resource use, and costs related to hip fracture in Estonia. Osteoporos Int 27:2555–2566. https://doi.org/10.1007/s00198-016-3544-4CrossRefPubMed

Goldacre MJ, Roberts SE, Yeates D (2002) Mortality after admission to hospital with fractured neck of femur: database study. BMJ 325:868–869. https://doi.org/10.1136/bmj.325.7369.868CrossRefPubMedPubMedCentral

Warriner AH, Patkar NM, Yun H, Delzell E (2011) Minor, major, low-trauma, and high-trauma fractures: what are the subsequent fracture risks and how do they vary? Curr Osteoporos Rep 9:122–128. https://doi.org/10.1007/s11914-011-0064-1CrossRefPubMed

CBO Richtlijn Osteoporose en Fractuurpreventie-2011. https://www.volksgezondheidenzorg.info/bestanden/documenten/cbo-richtlijn-osteoporose-en-fractuurpreventie-2011. Accessed 21 Oct 2019

Weda M, Jansen ME, Vonk RAA (2017) Personalised medicine - Implementatie in de praktijk en data-infrastructuren. RIVM Briefrapport 2017–0006

El-Hajj Fuleihan G, Chakhtoura M, Cauley JA, Chamoun N (2017) Worldwide fracture prediction. J Clin Densitom 20:397–424. https://doi.org/10.1016/j.jocd.2017.06.008CrossRefPubMed

10.

Tseng WJ, Hung LW, Shieh JS, Abbod MF, Lin J (2013) Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study. BMC Musculoskelet Disord 14:1. https://doi.org/10.1186/1471-2474-14-207CrossRef

11.

Ho-Le TP, Center JR, Eisman JA et al (2017) Prediction of hip fracture in post-menopausal women using artificial neural network approach. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf 2017:4207–4210. https://doi.org/10.1109/EMBC.2017.8037784CrossRef

12.

Ferizi U, Honig S, Chang G (2019) Artificial intelligence, osteoporosis and fragility fractures. Curr Opin Rheumatol 31:368–375. https://doi.org/10.1097/BOR.0000000000000607CrossRefPubMedPubMedCentral

13.

Hegeman JH, Willemsen G, van Nieuwpoort J, Kreeftenberg HG, van der Veer E, Slaets JP, ten Duis H (2004) Effective tracing of osteoporosis at a fracture and osteoporosis clinic in Groningen; an analysis of the first 100 patients. Ned Tijdschr Geneeskd 148:2180–2185PubMed

14.

DIS open data. https://www.opendisdata.nl/. Accessed 7 Feb 2020

15.

Dziak JJ, Henry KL (2017) Two-part predictors in regression models. Multivar Behav Res 52:551–561. https://doi.org/10.1080/00273171.2017.1333404CrossRef

16.

R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna URL http://www.R-project.org/

17.

Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B 34:187–220

18.

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Ann Appl Stat 2:841–860. https://doi.org/10.1214/08-AOAS169CrossRef

19.

Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y (2018) DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol 18:24. https://doi.org/10.1186/s12874-018-0482-1CrossRefPubMedPubMedCentral

20.

Schoenfeld D (1982) Partial residuals for the proportional hazards regression model. Biometrika 69:239. https://doi.org/10.2307/2335876CrossRef

21.

Steyerberg EW (2019) Clinical prediction models. Springer International Publishing, ChamCrossRef

22.

Shepherd BE, Rebeiro PF (2017) Assessing and interpreting the association between continuous covariates and outcomes in observational studies of HIV using splines. J Acquir Immune Defic Syndr 74:e60–e63. https://doi.org/10.1097/QAI.0000000000001221CrossRefPubMedPubMedCentral

23.

Breiman L (2001) Random forest. Mach Learn 45:5–32. https://doi.org/10.1017/CBO9781107415324.004CrossRef

24.

Claesen M, De Moor B (2015) Hyperparameter search in machine learning. https://ftp.esat.kuleuven.be/pub/SISTA/claesenm/reports/14-188.pdf. Accessed 21 Oct 2019

25.

Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc 1–15

26.

Moradi R, Berangi R, Minaei B (2020) A survey of regularization strategies for deep models. Springer, Netherlands

27.

Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830. https://doi.org/10.1145/2786984.2786995CrossRef

28.

Fotso S (2019) PySurvival: open source package for survival analysis modeling. https://square.github.io/pysurvival/#:~:text=PySurvival%20is%20an%20open%20source,compatible%20with%20Python%202.7%2D3.7. Accessed 14 Mar 2020

29.

Kim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ (2019) Deep learning-based survival prediction of oral cancer patients. Sci Rep 9:6994. https://doi.org/10.1038/s41598-019-43372-7CrossRefPubMedPubMedCentral

30.

Forgetta V, Keller-Baruch J, Forest M et al (2018) Machine learning to predict osteoporotic fracture risk from genotypes. bioRxiv:413716. https://doi.org/10.1101/413716

31.

Nissinen T (2019) Convolutional neural networks in osteoporotic fracture risk prediction using spine DXA images - Master’s thesis Computer Sciences, University of Eastern Finland

32.

Kruse C, Eiken P, Vestergaard P (2017) Machine learning principles can improve hip fracture prediction. Calcif Tissue Int 100:348–360. https://doi.org/10.1007/s00223-017-0238-7CrossRefPubMed

33.

Ensrud KE, Lui L-Y, Taylor BC, Schousboe JT, Donaldson MG, Fink HA, Cauley JA, Hillier TA, Browner WS, Cummings SR, Study of Osteoporotic Fractures Research Group (2009) A comparison of prediction models for fractures in older women: is more better? Arch Intern Med 169:2087–2094. https://doi.org/10.1001/archinternmed.2009.404CrossRefPubMedPubMedCentral

34.

Johansson H, Siggeirsdóttir K, Harvey NC, Odén A, Gudnason V, McCloskey E, Sigurdsson G, Kanis JA (2017) Imminent risk of fracture after fracture Europe PMC funders group. Osteoporos Int 28:775–780. https://doi.org/10.1007/s00198-016-3868-0CrossRefPubMed

35.

Reber KC, König HH, Becker C, Rapp K, Büchele G, Mächler S, Lindlbauer I (2018) Development of a risk assessment tool for osteoporotic fracture prevention: a claims data approach. Bone 110:170–176. https://doi.org/10.1016/j.bone.2018.02.002CrossRefPubMed

36.

Egan M, Jaglal S, Byrne K, Wells J, Stolee P (2008) Factors associated with a second hip fracture: a systematic review. Clin Rehabil 22:272–282. https://doi.org/10.1177/0269215507081573CrossRefPubMed

37.

de Klerk G (2017) Osteoporosis, identification and treatment in fracture patients. de Klerk G. Osteoporosis, identification and treatment in fracture patients: an essential part of fracture management in elderly patients. Rijksuniversiteit Groningen, Groningen, p 144

38.

Haentjens P, Autier P, Collins J et al (2003) Colles fracture, spine fracture, and subsequent risk of hip fracture in men and women. A meta-analysis. J Bone Joint Surg Am 85:1936–1943. https://doi.org/10.2106/00004623-200310000-00011CrossRefPubMed

39.

Zhao D, Cheng P, Zhu B (2016) Epilepsy and fracture risk: a meta-analysis. Int J Clin Exp Med 9:564–569

40.

Perna S, Avanzato I, Nichetti M, D’Antona G, Negro M, Rondanelli M (2017) Association between dietary patterns of meat and fish consumption with bone mineral density or fracture risk: a systematic literature. Nutrients 9. https://doi.org/10.3390/nu9091029

41.

Wells GA, Cranney A, Peterson J, Boucher M, Shea B, Welch V, Coyle D, Tugwell P, Cochrane Musculoskeletal Group (2008) Alendronate for the primary and secondary prevention of osteoporotic fractures in postmenopausal women. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD001155.pub2

Title: Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis
Authors: B. C. S. de Vries
J. H. Hegeman
W. Nijmeijer
J. Geerdink
C. Seifert
C. G. M. Groothuis-Oudshoorn
Publication date: 01-03-2021
Publisher: Springer London
Keywords: Osteoporosis
Osteoporosis
Published in: Osteoporosis International / Issue 3/2021
Print ISSN: 0937-941X
Electronic ISSN: 1433-2965
DOI: https://doi.org/10.1007/s00198-020-05735-z

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis

Abstract

Summary

Introduction

Methods

Results

Conclusion

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Summary

Introduction

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 3/2021

Therapeutic efficacy of zoledronic acid combined with calcitriol in elderly patients receiving total hip arthroplasty or hemiarthroplasty for osteoporotic femoral neck fracture

Cost-effectiveness of romosozumab for the treatment of postmenopausal women with severe osteoporosis at high risk of fracture in Sweden

Five-year follow-up results of aerobic and impact training on bone mineral density in early breast cancer patients

Vertebral fracture: epidemiology, impact and use of DXA vertebral fracture assessment in fracture liaison services

Exploring thoracic kyphosis and incident fracture from vertebral morphology with high-intensity exercise in middle-aged and older men with osteopenia and osteoporosis: a secondary analysis of the LIFTMOR-M trial

C-reactive protein and fracture risk: an updated systematic review and meta-analysis of cohort studies through the use of both frequentist and Bayesian approaches