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Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.

Manuel Huber1, Christoph Kurz2, Reiner Leidl2,3

  • 1German Research Center for Environmental Health, Institute for Health Economics and Health Care Management, Helmholtz Zentrum München, Postfach 1129, 85758, Neuherberg, Germany. manuel.huber@helmholtz-muenchen.de.

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|January 10, 2019
PubMed
Summary

Machine learning models accurately predict patient-reported outcomes after hip and knee replacement surgery. Preoperative scores and specific symptoms like limping are key predictors for improved patient outcomes.

Keywords:
Binary classificationBoostingHip replacementKnee replacementMachine learningPatient-reported outcomesPredictive performanceShared decision-makingVariable importance

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Area of Science:

  • Orthopedic Surgery
  • Medical Informatics
  • Machine Learning

Background:

  • Machine-learning classifiers demonstrate strong predictive performance for clinical applications.
  • This study focuses on the practical application of these classifiers in predicting patient-reported outcomes (PROs) post-hip and knee replacement.

Purpose of the Study:

  • To evaluate the predictive performance of eight supervised machine-learning classifiers for patient-reported outcomes (PROs) after hip and knee replacement surgery.
  • To identify key predictors of postoperative improvement in patients undergoing these procedures.

Main Methods:

  • Utilized a large dataset of National Health Service (NHS) PRO data (130,945 observations) from April 2015 to April 2017.
  • Trained and tested eight supervised classifiers, including linear models, to predict binary postoperative improvement.
  • Assessed performance using metrics like Area Under the Receiver Operating Characteristic (ROC) curve and J-statistic, focusing on generic (EQ-5D-3L VAS) and disease-specific (Oxford Hip and Knee Score - Q score) outcomes.

Main Results:

  • The best models achieved an Area Under the ROC curve of approximately 0.87 for VAS and 0.78 for Q score in hip replacement, and 0.86 for VAS and 0.70 for Q score in knee replacement.
  • Extreme gradient boosting, random forests, multistep elastic net, and linear models showed the highest J-statistics.
  • Preoperative VAS, Q score, and specific Q score dimensions (e.g., limping) were identified as the most significant predictors of postoperative outcomes.

Conclusions:

  • Supervised machine-learning algorithms, particularly extreme gradient boosting, offer superior predictive performance compared to linear models for PROs.
  • Preoperative patient-reported measures, including VAS and Q score, are crucial for predicting recovery after hip and knee surgery.
  • These findings support the integration of advanced machine learning for enhanced clinical decision-making in orthopedic surgery.