PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm
Version 1
: Received: 3 January 2024 / Approved: 3 January 2024 / Online: 4 January 2024 (05:58:14 CET)
How to cite:
Di Matteo, V.; Morandini, P.; Savevski, V.; Grappiolo, G.; Loppini, M. Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm. Preprints2024, 2024010261. https://doi.org/10.20944/preprints202401.0261.v1
Di Matteo, V.; Morandini, P.; Savevski, V.; Grappiolo, G.; Loppini, M. Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm. Preprints 2024, 2024010261. https://doi.org/10.20944/preprints202401.0261.v1
Di Matteo, V.; Morandini, P.; Savevski, V.; Grappiolo, G.; Loppini, M. Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm. Preprints2024, 2024010261. https://doi.org/10.20944/preprints202401.0261.v1
APA Style
Di Matteo, V., Morandini, P., Savevski, V., Grappiolo, G., & Loppini, M. (2024). Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm. Preprints. https://doi.org/10.20944/preprints202401.0261.v1
Chicago/Turabian Style
Di Matteo, V., Guido Grappiolo and Mattia Loppini. 2024 "Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm" Preprints. https://doi.org/10.20944/preprints202401.0261.v1
Abstract
Background: Periprosthetic joint infection (PJI) following total hip and knee arthroplasty remains an extremely challenging and relatively high complication. This study aims to develop, validate and evaluate the use of machine learning (ML) algorithm to predict PJI in patients undergoing revision arthroplasties. Methods: A comprehensive review of patients undergoing hip or knee revision arthroplasty from 1 January 2015 to 31 March 2021 was conducted. Clinical data coming from preoperative patients history, laboratory analysis and demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting PJI preoperatively. Results: 1360 patients were enrolled, 1141 in the aseptic cohort and 219 in the infected cohort were included. ML demonstrated good discriminatory performance in predicting PJI in the selected patients (area under the curve 0.770 ± 0.006 in the training set and 0.720 ± 0.057 in the test set), and identified 3 significant predictors of PJI. Conclusion: ML algorithm trained using preoperative clinical data accurately predicted PJI. The incorporation of ML models into preoperative assessment of patients undergoing prosthetic revision procedures are useful in providing specific risk assessment to aid individualised counselling, shared decision making and presurgical optimization.
Medicine and Pharmacology, Orthopedics and Sports Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
