ArticleArthroplasty today2021
A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.
Article in Arthroplasty today, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled 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
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
- Exploring the optimal age for total knee arthroplasty to minimize risk of adverse outcomes: machine learning analysis of a statewide cohort.Arthroplasty (London, England) · 2026Article
- Postoperative serum albumin drop predicts complications in primary total knee arthroplasty: a retrospective cohort study using XGBoost algorithm.BMC musculoskeletal disorders · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort.Orthopaedic journal of sports medicine · 2024Article
- Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery.Heliyon · 2024Article
- Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation.Arthroplasty (London, England) · 2023Article
- Improved performance of machine learning models in predicting length of stay, discharge disposition, and inpatient mortality after total knee arthroplasty using patient-specific variables.Arthroplasty (London, England) · 2023Article
- AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning.PLOS digital health · 2023Article
- Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.The American surgeon · 2023Article
- Machine Learning Algorithm to Predict Worsening of Flexion Range of Motion After Total Knee Arthroplasty.Arthroplasty today · 2022Article
- Construction of Prediction Model of Deep Vein Thrombosis Risk after Total Knee Arthroplasty Based on XGBoost Algorithm.Computational and mathematical methods in medicine · 2022Article
- Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty.Journal of orthopaedicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundThere remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm. We aimed to compare various ML methods to AP in predictive performance of complications after TKA.
methodsThirty-eight preoperative patient demographics and clinical features from all primary TKAs performed at California-licensed hospitals between 2015 and 2017 were evaluated as predictors of major complications after TKA. Traditional logistic regression (LR), various other ML methods (XGBoost, Gradient Boosting, AdaBoost, and Random Forest), and AP were used for model building to determine discriminative power (area under receiver operating curve), calibration (Brier score), and feature importance.
resultsBetween 2015 and 2017, there were a total of 156,750 TKAs with 1109 (0.7%) total major complications. AP had the highest discriminative performance with area under receiver operating curve 0.679 compared with LR, XGBoost, Gradient Boosting, AdaBoost, and Random Forest (0.617, 0.601, 0.662, 0.657, and 0.545, respectively). AP (Brier score 0.007) had similar calibration as the other ML methods (0.006, 0.006, 0.022, 0.007, and 0.008, respectively). The variables that are most important for AP differ from those that are most important for LR.
conclusionCompared to conventional ML algorithms, AP has superior discriminative ability with similar calibration and suggests nonlinear relationships between variables in outcomes of TKA.
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.