ArticleJournal of clinical medicine2022
Prediction of Complications and Surgery Duration in Primary Total Hip Arthroplasty Using Machine Learning: The Necessity of Modified Algorithms and Specific Data.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- 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
- Machine learning-based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study.JSES international · 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
- Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.Veterinary surgery : VS · 2025Article
- Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty - a development and validation study.BMC medical informatics and decision making · 2025Article
- [Artificial intelligence in arthroplasty].Orthopadie (Heidelberg, Germany) · 2025Review
- Hemi-Versus Total Hip Arthroplasty in Femoral Neck Fractures? Predicting Failure on a 10-Year Data Analysis of the German Arthroplasty Registry (EPRD).Journal of clinical medicine · 2025Article
- Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.International journal of colorectal disease · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine Learning (ML) in arthroplasty is becoming more popular, as it is perfectly suited for prediction models. However, results have been heterogeneous so far. We hypothesize that an accurate ML model for outcome prediction in THA must be able to compute arthroplasty-specific data. In this study, we evaluate a ML approach applying data from two German arthroplasty-specific registries to predict adverse outcomes after THA, after careful evaluations of ML algorithms, outcome and input variables by an interdisciplinary team of data scientists and surgeons.
methodsData of 1217 cases of primary THA from a single center were derived from two German arthroplasty-specific registries between 2016 to 2019. The XGBoost algorithm was adjusted and applied. Accuracy, sensitivity, specificity and AUC were calculated.
resultsFor the prediction of complications, the ML algorithm achieved an accuracy of 80.3%, a sensitivity of 31.0%, a specificity of 89.4% and an AUC of 64.1%. For the prediction of surgery duration, the ML algorithm yielded an accuracy of 81.7%, a sensitivity of 58.2%, a specificity of 91.6% and an AUC of 89.1%. The feature importance indicated non-linear outcomes for age, height, weight and surgeon. No relevant linear correlations were found.
conclusionThe attunement of input and output data as well as the modifications of the ML algorithm permitted the development of a feasible ML model for the prediction of complications and surgery duration.
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