ArticleJB & JS open access
Preoperative Risk Prediction Models for Short-Term Revision and Death After Total Hip Arthroplasty: Data from the Finnish Arthroplasty Register.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Clinical prediction models for patients undergoing total hip arthroplasty: an external validation based on a systematic review and the Dutch Arthroplasty Register.Acta orthopaedica · 2024Pooled it
- Prediction Models for Periprosthetic Joint Infection: A Systematic Review of Traditional and Machine Learning Approaches.Mayo Clinic proceedings. Digital health · 2026Review
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Postoperative urinary retention following hip or knee arthroplasty under spinal anaesthesia with intrathecal morphine: a retrospective cohort study.Singapore medical journal · 2025Article
- Defining Clinically Meaningful Thresholds for 12-Month Patient-Reported Outcomes in Total Hip Arthroplasty; Toward Improving Threshold Accuracy.Arthroplasty today · 2025Article
- Prediction of Early Adverse Events After THA: A Comparison of Different Machine-Learning Strategies Based on 262,356 Observations From the Nordic Arthroplasty Register Association (NARA) Dataset.ACR open rheumatology · 2024Article
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- A Surgeon's Guide to Understanding Artificial Intelligence and Machine Learning Studies in Orthopaedic Surgery.Current reviews in musculoskeletal medicine · 2022Review
- Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data.Cancer medicine · 2022Article
- Prediction model for an early revision for dislocation after primary total hip arthroplasty.PloS one · 2022Article
Corrections and comments
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Authors and funding
11 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
methodsWe 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.
resultsThe 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).
conclusionsBased 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.
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