ArticleThe Journal of arthroplasty2021
Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.
Article in The Journal of arthroplasty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.European journal of haematology · 2023Pooled it
- 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
- [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
- Data-Driven Approach to Development of a Risk Score for Periprosthetic Joint Infections in Total Joint Arthroplasty Using Electronic Health Records.The Journal of arthroplasty · 2025Article
- The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty.Journal of personalized medicine · 2025Review
- Perioperative laboratory profiles predict complications after extensive head and neck reconstruction: a proof-of-concept study.Frontiers in oncology · 2025Article
- A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort.Orthopaedic journal of sports medicine · 2024Article
- Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study.BMC anesthesiology · 2023Article
- Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Article
- AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning.PLOS digital health · 2023Article
- Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.Medical & biological engineering & computing · 2023Review
- Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III).Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Review
- Article
- Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty.Scientific reports · 2022Article
- Prediction of Complications and Surgery Duration in Primary Total Hip Arthroplasty Using Machine Learning: The Necessity of Modified Algorithms and Specific Data.Journal of clinical medicine · 2022Article
- Artificial intelligence in orthopedic surgery: evolution, current state and future directions.Arthroplasty (London, England) · 2022Review
- Prediction model for an early revision for dislocation after primary total hip arthroplasty.PloS one · 2022Article
- Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.Journal of shoulder and elbow arthroplasty · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundAs the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods.
methodsThis is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration.
resultsThere were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease).
conclusionWe report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.
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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.