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Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty

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Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression.

Methods

From the department’s clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics.

Results

At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73–0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI [− 0.0025, 0.002]).

Conclusions

When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression—in particular, ordinal logistic regression that does not assume linearity in its predictors.

Level of evidence

Prognostic level II

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Acknowledgements

We thank Brandon Greenwell for his generous help with the vip R package and Michael W. Wade at Vanderbilt University Medical Center for his editorial work on this article. We acknowledge the support from Jennifer Liaw, the head of the Department of Physiotherapy, Singapore General Hospital. We thank William Yeo from the Orthopaedic Diagnostic Centre, Singapore General Hospital, for his assistance. Finally, we thank Ee-Lin Woon, Felicia Jie-Ting Seah, Nai-Hong Chan, and the therapy assistants (Penny Teh and Hamidah Binti Hanib) for their kind assistance.

Funding

No funding was provided for the completion of this study.

Author information

Author notes

  1. Yong-Hao Pua and Hakmook Kang contributed equally to this work.

Authors and Affiliations

  1. Department of Physiotherapy, Singapore General Hospital, Singapore, Singapore

    Yong-Hao Pua, Eleanor Shu-Xian Chew & Cheryl Lian-Li Poon

  2. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

    Hakmook Kang

  3. Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore

    Julian Thumboo

  4. Research Health Institute, University of the Sunshine Coast, Sunshine Coast, Australia

    Ross Allan Clark

  5. Department of Physiotherapy, Changi General Hospital, Singapore, Singapore

    Hwei-Chi Chong

  6. Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore

    Seng-Jin Yeo

Authors
  1. Yong-Hao Pua
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  2. Hakmook Kang
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  3. Julian Thumboo
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  4. Ross Allan Clark
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  5. Eleanor Shu-Xian Chew
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Correspondence to Yong-Hao Pua.

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Ethical approval was provided by the SingHealth Centralized IRB (SingHealth CIRB 2014/2027, Singapore).

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Pua, YH., Kang, H., Thumboo, J. et al. Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 28, 3207–3216 (2020). https://doi.org/10.1007/s00167-019-05822-7

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