Preoperative Risk Prediction Models for Short-Term Revision and Death After Total Hip Arthroplasty: Data from the Finnish Arthroplasty Register. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Venäläinen, Mikko S.; Panula, Valtteri J.; Klén, Riku; Haapakoski, Jaason J.; Eskelinen, Antti P.; Manninen, Mikko J.; Kettunen, Jukka S.; Puhto, Ari-Pekka; Vasara, Anna I.; Mäkelä, Keijo T.; Elo, Laura L.
• Is Part Of: JB & JS open access. Volume 6:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Orthopedic surgery -- Periodicals; Orthopedic surgery; Periodicals; 617.4705
• DOI: 10.2106/JBJS.OA.20.00091
• Languages: English
• ISSNs: 2472-7245

Abstract

Abstract : Background: Because of the increasing number of total hip arthroplasties (THAs), even a small proportion of complications after the operation can lead to substantial individual difficulties and health-care costs. The aim of this study was to develop simple-to-use risk prediction models to assess the risk of the most common reasons for implant failure to facilitate clinical decision-making and to ensure long-term survival of primary THAs. Methods: We analyzed patient and surgical data reported to the Finnish Arthroplasty Register (FAR) on 25, 919 primary THAs performed in Finland between May 2014 and January 2018. For the most frequent adverse outcomes after primary THA, we developed multivariable Lasso regression models based on the data of the randomly selected training cohort (two-thirds of the data). The performances of all models were validated using the remaining, independent test set consisting of 8, 640 primary THAs (one-third of the data) not used for building the models. Results: The most common outcomes within 6 months after the primary THA were revision operations due to periprosthetic joint infection (1.1%), dislocation (0.7%), or periprosthetic fracture (0.5%), and death (0.7%). For each of these outcomes, Lasso regression identified subsets of variables required for accurate risk predictions. The highest discrimination performance, in terms of area under the receiver operating characteristic curve (AUROC), was observed for death (0.84), whereas the performance was lower for revisions due to periprosthetic joint infection (0.68), dislocation (0.64), or periprosthetic fracture (0.65). Conclusions: Based on the small number of preoperative characteristics of the patient and modifiable surgical parameters, the developed risk prediction models can be easily used to assess the risk of revision or death. All developed models hold the potential to aid clinical decision-making, ultimately leading to improved clinical outcomes. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.