Christian Michelsen, Christoffer C Jørgensen, Mathias Heltberg, Mogens H Jensen, Alessandra Lucchetti, Pelle B Petersen, Troels Petersen, Henrik Kehlet, Frank Madsen, Torben B Hansen, Kirill Gromov, Thomas Jakobsen, Claus Varnum, Soren Overgaard, Mikkel Rathsach, Lars Hansen
BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA). METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols...
November 29, 2023: BMC Anesthesiology