Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model

Authors

  • Katrin B Johannesdottir Biomedical Signal Processing & AI research group, Digital Health Section, DTU Health Tech, Technical University of Denmark, Lyngby
  • Henrik Kehlet Section of Surgical Pathophysiology 7621, Rigshospitalet, Copenhagen
  • Pelle B Petersen Section of Surgical Pathophysiology 7621, Rigshospitalet, Copenhagen
  • Eske K Aasvang Section of Surgical Pathophysiology 7621, Rigshospitalet, Copenhagen; Department of Anesthesiology, Center for Cancer and Organ Diseases, Copenhagen, Denmark
  • Helge B D Sørensen Biomedical Signal Processing & AI research group, Digital Health Section, DTU Health Tech, Technical University of Denmark, Lyngby
  • Christoffer C Jørgensen Section of Surgical Pathophysiology 7621, Rigshospitalet, Copenhagen
  • On behalf of Centre for Fast-track Hip and Knee Replacement Collaborative Group

DOI:

https://doi.org/10.2340/17453674.2021.843

Keywords:

Arthroplasty, Hip, Knee, Length of hospital stay, Outcome, Prediction

Abstract

Background and purpose — Prediction of postoperative outcomes and length of hospital stay (LOS) of patients is vital for allocation of healthcare resources. We investigated the performance of prediction models based on machinelearning algorithms compared with a previous risk stratification model using traditional multiple logistic regression, for predicting the risk of a LOS of > 2 days after fast-track total hip and knee replacement.

Patients and methods — 3 different machine learning classifiers were trained on data from the Lundbeck Centre for Fast-track Hip and Knee Replacement Database (LCDB) collected from 9,512 patients between 2016 and 2017. The chosen classifiers were a random forest classifier (RF), a support vector machine classifier with a polynomial kernel (SVM), and a multinomial Naïve-Bayes classifier (NB).

Results — Comparing performance measures of the classifiers with the traditional model revealed that all the models had a similar performance in terms of F1 score, accuracy, sensitivity, specificity, area under the receiver operating curve (AUC), and area under the precision-recall curve (AUPRC). A feature importance analysis of the RF classifier found hospital, age, use of walking aid, living alone, and joint operated on to be the most relevant input features. None of the classifiers reached a clinically relevant performance with the input data from the LCDB.

Interpretation — Despite the promising prospects of machine-learning practices for disease and risk prediction, none of the machine learning models tested outperformed the traditional multiple regression model in predicting which patients in this cohort had a LOS > 2 days.

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Published

2021-11-16

How to Cite

Johannesdottir, K. B., Kehlet, H., Petersen, P. B., Aasvang, E. K., Sørensen, H. B. D., Jørgensen, C. C., & Centre for Fast-track Hip and Knee Replacement Collaborative Group, O. behalf of. (2021). Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model. Acta Orthopaedica, 92. https://doi.org/10.2340/17453674.2021.843

Issue

Section

Clinical observational study