Skip to main content
SpringerLink
Log in

Analysis of Factors Associated with Large Kidney Stones: Stone Composition, Comorbid Conditions, and 24-H Urine Parameters—a Machine Learning-Aided Approach

  • Medicine
  • Published:
SN Comprehensive Clinical Medicine Aims and scope Submit manuscript

Abstract

We aim to describe factors that are associated with kidney stones 20 mm or larger. This information would potentially guide research regarding factors of kidney stone growth. We retrospectively reviewed a patient cohort who underwent surgical treatment for kidney stones. Patients with detailed demographics, 24-h urine testing, and kidney stone profiling were included. Large stone was defined as measuring 20 mm or more. Univariate analysis was conducted to assess variables associated with kidney stones larger than 20 mm. Multivariate logistic regression and statistical machine learning methods were used to infer prediction models. The specific composition of kidney stones, laboratory testing results, and detailed demographics of 277 patients were included in our analysis. Multiple variables were significantly associated with large stones in univariate analysis. The final model that predicts large stone size includes several variables from different domains: hypertension (OR = 1.91; 95% CI 1.06, 3.43), older age (60+ vs 20–40) (OR = 2.46; 95% CI 1.07, 5.63), decreased calcium oxalate supersaturation (OR = 0.92; 95% CI 0.85, 0.99), and higher percentage of protein in stone composition (OR = 5.64; 95% CI 2.04, 15.58). This model yields a sensitivity 83% and specificity of 56%. Models using machine learning algorithms identified similar predictors, but the performance varies. Our model yielded good performance, and it could potentially be used as a clinical tool for predicting large stones and identifying factors affecting kidney stone growth. Similar analysis in other cohorts should be pursued to externally validate findings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen Z, Prosperi M, Bird VY. Prevalence of kidney stones in the USA: the National Health and Nutrition Evaluation Survey. J Clin Urol. 2018:205141581881382. https://doi.org/10.1177/2051415818813820.

  2. Scales CD, Smith AC, Hanley JM, et al. Prevalence of kidney stones in the United States. Eur Urol. 2012;62:160–5. https://doi.org/10.1016/j.eururo.2012.03.052.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Pearle MS, Goldfarb DS, Assimos DG, et al (2014) AUA guidelines medical management of kidney stones: AUA guideline. https://doi.org/10.1016/j.juro.2014.05.006

  4. Kyriazis I, Panagopoulos V, Kallidonis P, Özsoy M, Vasilas M, Liatsikos E. Complications in percutaneous nephrolithotomy. World J Urol. 2014;33:1069–77. https://doi.org/10.1007/s00345-014-1400-8.

    Article  PubMed  Google Scholar 

  5. Ahmadi F, Etemadi SM, Lessan-Pezeshki M, Mahdavi-Mazdeh M, Ayati M, Mir A, et al. Contribution of stone size to chronic kidney disease in kidney stone formers. Int J Urol. 2015;22:104–8. https://doi.org/10.1111/iju.12606.

    Article  CAS  PubMed  Google Scholar 

  6. Gettman MT, Segura JW. Management of ureteric stones: issues and controversies. BJU Int. 2005;95(Suppl 2):85–93. https://doi.org/10.1111/j.1464-410X.2005.05206.x.

    Article  PubMed  Google Scholar 

  7. Burgher A, Beman M, Holtzman JL, Monga M. Progression of nephrolithiasis: long-term outcomes with observation of asymptomatic calculi. J Endourol. 2004;18:534–9. https://doi.org/10.1089/end.2004.18.534.

    Article  PubMed  Google Scholar 

  8. Goertz JK, Lotterman S. Can the degree of hydronephrosis on ultrasound predict kidney stone size? Am J Emerg Med. 2010;28:813–6. https://doi.org/10.1016/j.ajem.2009.06.028.

    Article  PubMed  Google Scholar 

  9. Assimos D, Krambeck A, Miller NL, Monga M, Murad MH, Nelson CP, et al. Surgical management of stones: American Urological Association/Endourological Society Guideline, PART I. J Urol. 2016;196:1153–60. https://doi.org/10.1016/j.juro.2016.05.090.

    Article  PubMed  Google Scholar 

  10. Cohen J, Cohen S, Grasso M. Ureteropyeloscopic treatment of large, complex intrarenal and proximal ureteral calculi. BJU Int. 2013;111:E127–31. https://doi.org/10.1111/j.1464-410X.2012.11352.x.

    Article  PubMed  Google Scholar 

  11. Quinlan JR (1992) C4.5: programs for machine learning.

    Google Scholar 

  12. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat. 2000;28:337–407.

    Article  Google Scholar 

  13. Breiman L. Random forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.

    Article  Google Scholar 

  14. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6. https://doi.org/10.2202/1544-6115.1309.

  15. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27:861–74. https://doi.org/10.1016/j.patrec.2005.10.010.

    Article  Google Scholar 

  16. Nadeau C, Bengio Y. Inference for the generalization error. Mach Learn. 2003;52:239–81. https://doi.org/10.1023/A:1024068626366.

    Article  Google Scholar 

  17. Schisterman EF, Perkins NJ, Liu A, Bondell H. Optimal cut-point and its corresponding Youden index to discriminate individuals using pooled blood samples. Epidemiology. 2005;16:73–81. https://doi.org/10.1097/01.ede.0000147512.81966.ba.

    Article  PubMed  Google Scholar 

  18. Frank E, Hall MA, Witten IH (2016) The WEKA workbench.

  19. Bird VY, Khan SR. How do stones form? Is unification of theories on stone formation possible? Arch Esp Urol. 2017;70:12–27.

    PubMed  PubMed Central  Google Scholar 

  20. Pramanik R, Asplin JR, Jackson ME, Williams JC. Protein content of human apatite and brushite kidney stones: significant correlation with morphologic measures. Urol Res. 2008;36:251–8. https://doi.org/10.1007/s00240-008-0151-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Finkielstein VA, Goldfarb DS. Strategies for preventing calcium oxalate stones. CMAJ. 2006;174:1407–9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Keller EX, De Coninck V, Audouin M, et al. Stone composition independently predicts stone size in 18,029 spontaneously passed stones. World J Urol. 2019. https://doi.org/10.1007/s00345-018-02627-0.

Download references

Author information

Authors and Affiliations

  1. Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, PO Box 100231, 2004 Mowry Road, Gainesville, FL, 32610-0231, USA

    Zhaoyi Chen & Mattia Prosperi

  2. Department of Urology, College of Medicine, University of Florida, Gainesville, FL, USA

    Vincent G. Bird & Victoria Y. Bird

Authors
  1. Zhaoyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mattia Prosperi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent G. Bird
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Victoria Y. Bird
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Chen: Protocol/project development, data analysis, manuscript writing/editing

Prosperi: Data analysis, manuscript writing/editing

Bird (Vincent): Data collection or management, manuscript writing/editing

Bird (Victoria): Protocol/project development, data collection or management, manuscript writing/editing

Corresponding author

Correspondence to Zhaoyi Chen.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors. The analysis was a retrospective analysis, and the study was approved by the University of Florida Institutional Review Board as exempt.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Medicine

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Prosperi, M., Bird, V.G. et al. Analysis of Factors Associated with Large Kidney Stones: Stone Composition, Comorbid Conditions, and 24-H Urine Parameters—a Machine Learning-Aided Approach. SN Compr. Clin. Med. 1, 597–602 (2019). https://doi.org/10.1007/s42399-019-00087-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42399-019-00087-0

Keywords

Search

Navigation