ArticleThe Journal of the American Academy of Orthopaedic Surgeons2020
A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.
Article in The Journal of the American Academy of Orthopaedic Surgeons, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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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
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- The implementation of artificial intelligence in upper extremity surgery: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Determinants of prolonged hospitalization following shoulder arthroplasty: a systematic review of patient and health system factors.JSES reviews, reports, and techniques · 2026Review
- Modifiable pre-operative physiological predictors of length of stay following shoulder arthroplasty: a systematic review.JSES reviews, reports, and techniques · 2026Review
- Artificial Intelligence-Based Prediction of Prognosis After Nonsurgical Treatment for Medial Meniscus Posterior Root Tear.Orthopaedic journal of sports medicine · 2026Article
- Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.JSES international · 2026Review
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- Review
- Artificial intelligence in shoulder and elbow surgery: a bibliometric analysis of affiliation-based collaboration patterns.JSES international · 2026Review
- Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions.Journal of medical systems · 2025Review
- Machine learning prediction of early reoperation following lower extremity tumor resection and endoprosthetic reconstruction: A PARITY trial secondary analysis.Journal of orthopaedic surgery and research · 2025Article
- Navigating the future: A comprehensive review of technology in shoulder arthroplasty.Journal of hand and microsurgery · 2025Review
- Artificial intelligence in shoulder arthroplasty: how smart is it?JSES international · 2025Article
- Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review.Journal of experimental orthopaedics · 2025Review
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
- Definitional differences in "outpatient" surgery can influence study outcomes related to total shoulder arthroplasty.JSES international · 2025Article
- Perioperative Management, Complications, and Outcomes of Shoulder Arthroplasty in Patients with Diabetes Mellitus.JBJS reviews · 2025Review
- Post-Operative Outcome Predictions in Vestibular Schwannoma Using Machine Learning Algorithms.Journal of personalized medicine · 2024Article
- Machine Learning Prediction Model to Predict Length of Stay of Patients Undergoing Hip or Knee Arthroplasties: Results from a High-Volume Single-Center Multivariate Analysis.Journal of clinical medicine · 2024Article
- Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review.Arthroplasty (London, England) · 2024Article
- Initial clinical experience with a predictive clinical decision support tool for anatomic and reverse total shoulder arthroplasty.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
introductionPatient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplasty based on medical comorbidities and demographic factors.
methodsPatients undergoing elective TSA from 2011 to 2016 in the American College of Surgeons National Surgical Quality Improvement Program were queried. A random forest machine learning model was used to predict which patients had a length of stay of 1 day or less (short stay). A multivariable logistic regression was then used to identify which variables were significantly correlated with a short or long stay.
resultsFrom 2011 to 2016, 4,500 patients were identified as having undergone elective TSA and having the necessary predictive features and outcomes recorded. The machine learning model was able to successfully identify short stay patients, producing an area under the receiver operator curve of 0.77. The multivariate logistic regression identified numerous variables associated with a short stay including age less than 70 years and male sex as well as variables associated with a longer stay including diabetes, chronic obstructive pulmonary disease, and American Society of Anesthesiologists class greater than 2.
conclusionsMachine learning may be used to predict which patients are suitable candidates for short stay or outpatient TSA based on their medical comorbidities and demographic profile.
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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.