ArticleJournal of orthopaedics
Development of a multivariable prediction model for early revision of total knee arthroplasty - The effect of including patient-reported outcome measures.
Article in Journal of orthopaedics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Low confidence in the cumulative evidence for the existence of a volume-outcome relationship after revision total knee replacement: A systematic review and meta-analysis.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Pooled it
- Evaluating two implant designs in patients undergoing primary total knee arthroplasty using a novel measure of early optimal recovery: a retrospective observational study.Musculoskeletal surgery · 2025Observational
- Leveraging transfer learning for predicting total knee arthroplasty failure from post-operative radiographs.Journal of experimental orthopaedics · 2024Article
- Artificial intelligence in knee arthroplasty: current concept of the available clinical applications.Arthroplasty (London, England) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRevision TKA is a serious adverse event with substantial consequences for the patient. As revision is becoming increasingly common in patients under 65 years, the need for improved preoperative patient selection is imminently needed. Therefore, this study aimed to identify the most important factors of early revision and to develop a prediction model of early revision including assessment of the effect of incorporating data on patient-reported outcome measures (PROMs). MATERIAL AND
methodsA cohort of 538 patients undergoing primary TKA was included. Multiple logistic regression using forward selection of variables was applied to identify the best predictors of early revision and to develop a prediction model. The model was internally validated with stratified 5-fold cross-validation. This procedure was repeated without including data on PROMs to develop a model for comparison. The models were evaluated on their discriminative capacity using area under the receiver operating characteristic curve (AUC).
resultsThe most important factors of early revision were age (OR 0.63 [0.42, 0.95];
conclusionsAlthough more work is needed in order to reach a clinically meaningful quality of the predictions, our results show that the inclusion of PROMs seems to improve the quality of the prediction model.
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Registered trials
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