ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2020
Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 6 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 6 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Clinical prediction models for postoperative blood transfusion after total knee arthroplasty: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Machine learning in knee arthroplasty: specific data are key-a systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2022Pooled it
- Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.Acta orthopaedica · 2021Pooled it
- Machine learning-based prediction of transfusion.Transfusion · 2020Trial
- 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
- AI and Blood Banking: Predicting Transfusion Demand - A Systematic Review of Forecasting Approaches.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026Review
- Artificial intelligence in orthopedics: current applications, challenges, and future directions.Knee surgery & related research · 2026Review
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- [Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2025Article
- Current Concepts in Artificial Intelligence-Assisted Arthroplasty: A Review of the Perioperative Pathway.Cureus · 2025Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Artificial intelligence in total knee arthroplasty: clinical applications and implications.Knee surgery & related research · 2025Review
- Machine learning for the prediction of blood transfusion risk during or after mitral valve surgery: a multicenter retrospective cohort study.Scientific reports · 2025Article
- How New Technologies Will Transform Total Knee Arthroplasty from a Singular Surgical Procedure to a Holistic Standardized Process.Journal of clinical medicine · 2025Article
- [Artificial intelligence in arthroplasty].Orthopadie (Heidelberg, Germany) · 2025Review
- Artificial Intelligence and the State of the Art of Orthopedic Surgery.The archives of bone and joint surgery · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeA blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purpose of this study was to identify informative preoperative variables to create a machine learning model, and to provide a web-based transfusion risk-assessment system for clinical use.
methodsThis study retrospectively reviewed 1686 patients who underwent TKA at our institution. Data for 43 preoperative variables, including medication history, laboratory values, and demographic characteristics, were collected. Variable selection was conducted using the recursive feature elimination algorithm. The transfusion group was defined as patients with haemoglobin (Hb) < 7 g/dL after TKA. A predictive model was developed using the gradient boosting machine, and the performance of the model was assessed by the area under the receiver operating characteristic curve (AUC). Data sets from an independent institution were tested with the model for external validation.
resultsOf the 1686 patients who underwent TKA, 108 (6.4%) were categorized into the transfusion group. Six preoperative variables were selected, including preoperative Hb, platelet count, type of surgery, tranexamic acid, age, and body weight. The predictive model demonstrated good predictive performance using the six variables [AUC 0.842; 95% confidence interval (CI) 0.820-0.856]. Performance was also good according to the external validation using 400 data from an independent institution (AUC 0.880; 95% CI 0.844-0.910). This web-based blood transfusion risk-assessment system can be accessed at http://safetka.net.
conclusionsA web-based predictive model for transfusion after TKA using a machine learning algorithm was developed using six preoperative variables. The model is simple, has been validated, showed good performance, and can be used before TKA to predict the risk of transfusion and guide appropriate precautions for high-risk patients. LEVEL OF EVIDENCE: Diagnostic level II.
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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.