PreprintArticleVersion 2Preserved in Portico This version is not peer-reviewed
Machine Learning Prediction Model to Predict Length of Stay in Patients undergoing Hip or Knee Arthroplasties: Results from a High Volume Single Center Multivariate Analysis
Version 1
: Received: 13 November 2023 / Approved: 14 November 2023 / Online: 14 November 2023 (11:28:29 CET)
Version 2
: Received: 15 January 2024 / Approved: 15 January 2024 / Online: 16 January 2024 (09:58:02 CET)
How to cite:
Di Matteo, V.; Tommasini, T.; Morandini, P.; Savevski, V.; Grappiolo, G.; Loppini, M. Machine Learning Prediction Model to Predict Length of Stay in Patients undergoing Hip or Knee Arthroplasties: Results from a High Volume Single Center Multivariate Analysis. Preprints2023, 2023110915. https://doi.org/10.20944/preprints202311.0915.v2
Di Matteo, V.; Tommasini, T.; Morandini, P.; Savevski, V.; Grappiolo, G.; Loppini, M. Machine Learning Prediction Model to Predict Length of Stay in Patients undergoing Hip or Knee Arthroplasties: Results from a High Volume Single Center Multivariate Analysis. Preprints 2023, 2023110915. https://doi.org/10.20944/preprints202311.0915.v2
Di Matteo, V.; Tommasini, T.; Morandini, P.; Savevski, V.; Grappiolo, G.; Loppini, M. Machine Learning Prediction Model to Predict Length of Stay in Patients undergoing Hip or Knee Arthroplasties: Results from a High Volume Single Center Multivariate Analysis. Preprints2023, 2023110915. https://doi.org/10.20944/preprints202311.0915.v2
APA Style
Di Matteo, V., Tommasini, T., Morandini, P., Savevski, V., Grappiolo, G., & Loppini, M. (2024). Machine Learning Prediction Model to Predict Length of Stay in Patients undergoing Hip or Knee Arthroplasties: Results from a High Volume Single Center Multivariate Analysis. Preprints. https://doi.org/10.20944/preprints202311.0915.v2
Chicago/Turabian Style
Di Matteo, V., Guido Grappiolo and Mattia Loppini. 2024 "Machine Learning Prediction Model to Predict Length of Stay in Patients undergoing Hip or Knee Arthroplasties: Results from a High Volume Single Center Multivariate Analysis" Preprints. https://doi.org/10.20944/preprints202311.0915.v2
Abstract
Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients Length of Stay (LOS). This study aims to develop a machine learning prediction model that may aid to predict LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients undergoing elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization has been classified as “Short LOS” if it was less than or equal to 6 days and “Long LOS” if it was greater than 7 days. Clinical data coming from pre-operative laboratory analysis, vital parameters, demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting short or long LOS. Results: Final dataset was composed of 1517 patients (795 “LONG”, 722 “SHORT”, p = 0.3196) with a total of 1541 admissions (729 “LONG”, 812 “SHORT”, p < 0.000). Complete model had a prediction efficacy of 78,99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice predicting which patients are suitable for a shorter LOS, from those with a longer LOS in which a cautious approach could be recommended.
Keywords
Artificial intelligence; Machine learning; Arthroplasty; Hip; Knee; Length of stay.
Subject
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.
