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Use of machine learning to model surgical decision-making in lumbar spine surgery

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Abstract

Purpose

The majority of lumbar spine surgery referrals do not proceed to surgery. Early identification of surgical candidates in the referral process could expedite their care, whilst allowing timelier implementation of non-operative strategies for those who are unlikely to require surgery. By identifying clinical and imaging features associated with progression to surgery in the literature, we aimed to develop a machine learning model able to mirror surgical decision-making and calculate the chance of surgery based on the identified features.

Material and methods

In total, 55 factors were identified to predict surgical progression. All patients presenting with a lumbar spine complaint between 2013 and 2019 at a single Australian Tertiary Hospital (n = 483) had their medical records reviewed and relevant data collected. An Artificial Neural Network (ANN) was constructed to predict surgical candidacy. The model was evaluated on its accuracy, discrimination, and calibration.

Results

Eight clinical and imaging predictive variables were included in the final model. The ANN was able to predict surgical progression with 92.1% accuracy. It also exhibited excellent discriminative ability (AUC = 0.90), with good fit of data (Calibration slope 0.938, Calibration intercept – 0.379, HLT > 0.05).

Conclusion

Through use of machine learning techniques, we were able to model surgical decision-making with a high degree of accuracy. By demonstrating that the operating patterns of single centres can be modelled successfully, the potential for more targeted and tailored referrals becomes possible, reducing outpatient wait-list duration and increasing surgical conversion rates.

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Data availability

Available at request.

Code availability

SPSS is a publicly available software. The final model is not currently available for public use given current ongoing validation.

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia

    Nathan Xie, Peter J. Wilson & Rajesh Reddy

  2. Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia

    Nathan Xie, Peter J. Wilson & Rajesh Reddy

Authors
  1. Nathan Xie
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  2. Peter J. Wilson
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  3. Rajesh Reddy
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Contributions

All authors made substantial contributions to the work, including conception, design, acquisition, and analysis of data. NX drafted the work, PW and RR were involved in critical revision. All authors approved the final version for submission.

Corresponding author

Correspondence to Nathan Xie.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

Obtained from SESLHD Ethics Committee—approval number 18/035.

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Xie, N., Wilson, P.J. & Reddy, R. Use of machine learning to model surgical decision-making in lumbar spine surgery. Eur Spine J 31, 2000–2006 (2022). https://doi.org/10.1007/s00586-021-07104-8

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  • DOI: https://doi.org/10.1007/s00586-021-07104-8

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