ArticleBMC medical informatics and decision making2019
Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
60 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Evaluating methodological quality of prognostic prediction models on patient-reported outcome measurements after total hip and total knee arthroplasty : a systematic review.Bone & joint open · 2026Pooled it
- Machine learning in the development and application of patient-reported outcome measures (PROMs) for surgical patients: a systematic review.Journal of patient-reported outcomes · 2026Pooled it
- Optimal inputs for machine learning models in predicting total joint arthroplasty outcomes: a systematic review.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Pooled it
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- Present and future of machine learning in breast surgery: systematic review.The British journal of surgery · 2022Pooled it
- Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review.BMC medical informatics and decision making · 2022Pooled it
- Preoperative predictors of health-related quality of life changes (EQ-5D and EQ VAS) after total hip and knee replacement: a systematic review.BMC musculoskeletal disorders · 2022Pooled it
- Pooled it
- Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes.BMC medical informatics and decision making · 2022Trial
- Trial
- 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
- Development of Machine Learning Algorithms Predicting Psychological Distress After Total Joint Arthroplasty.The Journal of arthroplasty · 2026Article
- Development and validation of machine learning models for predicting rehabilitation non-response after knee arthroplasty.Frontiers in rehabilitation sciences · 2026Article
- Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model.World journal of methodology · 2025Article
- Current Concepts in Artificial Intelligence-Assisted Arthroplasty: A Review of the Perioperative Pathway.Cureus · 2025Review
- Prediction of quality-of-life improvement after total hip arthroplasty : a simplified and internally validated model based on 82,526 total hip arthroplasties from the Swedish Arthroplasty Register.Bone & joint open · 2025Article
- Shared decision making using digital twins in knee osteoarthritis care: a randomized clinical trial of an AI-enabled decision aid versus education alone on decision quality, physical function, and user experience.EClinicalMedicine · 2025Article
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Preoperatively predicting failure to achieve the minimum clinically important difference and substantial clinical benefit for total knee arthroplasty patients using machine learning.Knee surgery & related research · 2025Article
- A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.Journal of orthopaedic surgery and research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient-reported outcomes (PROs) by eight supervised classifiers including a linear model, following hip and knee replacement surgery.
methodsNHS PRO data (130,945 observations) from April 2015 to April 2017 were used to train and test eight classifiers to predict binary postoperative improvement based on minimal important differences. Area under the receiver operating characteristic, J-statistic and several other metrics were calculated. The dependent outcomes were generic and disease-specific improvement based on the EQ-5D-3L visual analogue scale (VAS) as well as the Oxford Hip and Knee Score (Q score).
resultsThe area under the receiver operating characteristic of the best training models was around 0.87 (VAS) and 0.78 (Q score) for hip replacement, while it was around 0.86 (VAS) and 0.70 (Q score) for knee replacement surgery. Extreme gradient boosting, random forests, multistep elastic net and linear model provided the highest overall J-statistics. Based on variable importance, the most important predictors for post-operative outcomes were preoperative VAS, Q score and single Q score dimensions. Sensitivity analysis for hip replacement VAS evaluated the influence of minimal important difference, patient selection criteria as well as additional data years. Together with a small benchmark of the NHS prediction model, robustness of our results was confirmed.
conclusionsSupervised machine-learning implementations, like extreme gradient boosting, can provide better performance than linear models and should be considered, when high predictive performance is needed. Preoperative VAS, Q score and specific dimensions like limping are the most important predictors for postoperative hip and knee PROMs.
Indexed as
Identifiers
What OpenQuestion holds
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.