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Original research
Machine learning approach to predicting persistent opioid use following lower extremity joint arthroplasty
  1. Rodney A Gabriel1,
  2. Bhavya Harjai1,
  3. Rupa S Prasad1,
  4. Sierra Simpson2,
  5. Iris Chu1,
  6. Kathleen M Fisch3 and
  7. Engy T Said1,4
  1. 1Anesthesiology, University of California San Diego, La Jolla, California, USA
  2. 2Psychiatry, University of California San Diego, La Jolla, California, USA
  3. 3Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, California, USA
  4. 4University of California San Diego, San Diego, California, USA
  1. Correspondence to Dr Rodney A Gabriel, Anesthesiology, University of California San Diego, La Jolla, California, USA; ragabriel{at}health.ucsd.edu

Abstract

Background The objective of this study is to develop predictive models for persistent opioid use following lower extremity joint arthroplasty and determine if ensemble learning and an oversampling technique may improve model performance.

Methods We compared various predictive models to identify at-risk patients for persistent postoperative opioid use using various preoperative, intraoperative, and postoperative data, including surgical procedure, patient demographics/characteristics, past surgical history, opioid use history, comorbidities, lifestyle habits, anesthesia details, and postoperative hospital course. Six classification models were evaluated: logistic regression, random forest classifier, simple-feedforward neural network, balanced random forest classifier, balanced bagging classifier, and support vector classifier. Performance with Synthetic Minority Oversampling Technique (SMOTE) was also evaluated. Repeated stratified k-fold cross-validation was implemented to calculate F1-scores and area under the receiver operating characteristics curve (AUC).

Results There were 1042 patients undergoing elective knee or hip arthroplasty in which 242 (23.2%) reported persistent opioid use. Without SMOTE, the logistic regression model has an F1 score of 0.47 and an AUC of 0.79. All ensemble methods performed better, with the balanced bagging classifier having an F1 score of 0.80 and an AUC of 0.94. SMOTE improved performance of all models based on F1 score. Specifically, performance of the balanced bagging classifier improved to an F1 score of 0.84 and an AUC of 0.96. The features with the highest importance in the balanced bagging model were postoperative day 1 opioid use, body mass index, age, preoperative opioid use, prescribed opioids at discharge, and hospital length of stay.

Conclusions Ensemble learning can dramatically improve predictive models for persistent opioid use. Accurate and early identification of high-risk patients can play a role in clinical decision making and early optimization with personalized interventions.

  • pain
  • postoperative
  • chronic pain
  • pain management

Data availability statement

Data are available on reasonable request.

https://doi.org/10.1136/rapm-2021-103299

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    Data availability statement

    Data are available on reasonable request.

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    Footnotes

    • Contributors RG, BH, SS and ETS were involved in study design. RG, RSP and ETS collected the data. RG, BH and SS were involved in the statistical analysis. RG, BH, RSP, SS, IC and KF were involved in the interpretation of results. RG, BH, RSP, SS, IC, KF and ETS were involved in the preparation and finalization of the manuscript. RG serves as the guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.