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Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention

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Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

This study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR).

Methods

Patients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves.

Results

A total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785–0.795; calibration intercept = 0.006, 95% CI: 0.005–0.007, calibration slope = 0.961 95% CI: 0.956–0.965, Brier’s score = 0.10 95% CI: 0.09–0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear.

Conclusion

Machine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes.

Level of evidence

III.

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Acknowledgements

The authors would like to acknowledge the support from the Foderaro-Quattrone Musculoskeletal-Orthopaedic Surgery Research Innovation Fund. This study was partially funded by the following: National Institute of Arthritis and Musculoskeletal and Skin Diseases for the Musculoskeletal Research Training Program (T32AR56950). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. This study used the resources of the Rochester Epidemiology Project (REP) medical records-linkage system, which is supported by the National Institute on Aging (NIA; AG 058738), by the Mayo Clinic Research Committee, and by fees paid annually by REP users. The content of this article is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health (NIH) or the Mayo Clinic.

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Authors and Affiliations

  1. Department of Orthopedic Surgery, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA

    Kevin Jurgensmeier, Sara E. Till, Yining Lu, Alexandra M. Arguello, Michael J. Stuart, Daniel B. F. Saris, Christopher L. Camp & Aaron J. Krych

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  1. Kevin Jurgensmeier
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  2. Sara E. Till
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Correspondence to Aaron J. Krych.

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Conflict of interest

Authors KJ, SET, YL, and AMA have nothing to disclose. Author MJS reports grants, personal fees, and other from Arthrex, Inc. and grants from Stryker, outside the submitted work; and American Journal of Sports Medicine: Editorial or governing board. Author DBF reports grants and other from JRF, personal fees and other from Smith & Nephew, outside the submitted work; and Cartilage: Editorial or governing board. Author CLC reports personal fees and non-financial support from Arthrex, personal fees and non-financial support from Zimmer Biomet, non-financial support from Stryker Corporation, and personal fees from Gemini Inc.. Author AJK reports grants from Aesculap/B. Braun, grants and other from Arthrex, Inc., grants from Arthritis Foundation, grants from Ceterix, grants from Histogenics, other from JRF Ortho and Vericel, grants from DJO LLC, other from Responsive Arthroscopy, outside the submitted work, and American Journal of Sports Medicine: Editorial or governing board, ICRS: Board or committee member, ISAKOS: Board or committee member, Minnesota Orthopedic Society: board or committee member, Musculoskeletal Transplantation Foundation: board or committee member.

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Appendix

See Table 4 and Fig. 5.

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Jurgensmeier, K., Till, S.E., Lu, Y. et al. Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention. Knee Surg Sports Traumatol Arthrosc 31, 518–529 (2023). https://doi.org/10.1007/s00167-022-07117-w

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