ArticleAmerican journal of epidemiology2018
Estimating an Individual's Probability of Revision Surgery After Knee Replacement: A Comparison of Modeling Approaches Using a National Data Set.
Article in American journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Patient relevant outcomes of unicompartmental versus total knee replacement: systematic review and meta-analysis.BMJ (Clinical research ed.) · 2019Pooled it
- Joint-specific measures improve risk adjustment in total knee arthroplasty: A machine learning approach.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Current Concepts in Artificial Intelligence-Assisted Arthroplasty: A Review of the Perioperative Pathway.Cureus · 2025Review
- Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National Joint Registry (SIRIS).Diagnostic and prognostic research · 2025Review
- Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.Archives of orthopaedic and trauma surgery · 2024Article
- Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.Clinical orthopaedics and related research · 2024Article
- CORR Insights®: Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.Clinical orthopaedics and related research · 2024Article
- Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.Medical & biological engineering & computing · 2024Article
- A comparison of survival models for prediction of eight-year revision risk following total knee and hip arthroplasty.BMC medical research methodology · 2022Article
- Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review.Arthroplasty (London, England) · 2022Review
- Using machine learning for the personalised prediction of revision endoscopic sinus surgery.PloS one · 2022Article
- Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review.Arthroplasty today · 2021Article
- Hot spots and trends in knee revision research since the 21st century: a bibliometric analysis.Annals of translational medicine · 2021Article
- Personalized estimation of one-year mortality risk after elective hip or knee arthroplasty for osteoarthritis.Bone & joint research · 2020Article
- JointCalc: A web-based personalised patient decision support tool for joint replacement.International journal of medical informatics · 2020Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Tools that provide personalized risk prediction of outcomes after surgical procedures help patients make preference-based decisions among the available treatment options. However, it is unclear which modeling approach provides the most accurate risk estimation. We constructed and compared several parametric and nonparametric models for predicting prosthesis survivorship after knee replacement surgery for osteoarthritis. We used 430,455 patient-procedure episodes between April 2003 and September 2015 from the National Joint Registry for England, Wales, Northern Ireland, and the Isle of Man. The flexible parametric survival and random survival forest models most accurately captured the observed probability of remaining event-free. The concordance index for the flexible parametric model was the highest (0.705, 95% confidence interval (CI): 0.702, 0.707) for total knee replacement and was 0.639 (95% CI: 0.634, 0.643) for unicondylar knee replacement and 0.589 (95% CI: 0.586, 0.592) for patellofemoral replacement. The observed-to-predicted ratios for both the flexible parametric and the random survival forest approaches indicated that models tended to underestimate the risks for most risk groups. Our results show that the flexible parametric model has a better overall performance compared with other tested parametric methods and has better discrimination compared with the random survival forest approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.