ArticleArthroplasty today2022
Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty.
Article in Arthroplasty today, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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
20 citing papers in PubMed.
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Development and Clinical Utility of Machine Learning Models for Prediction of Same-Day Discharge in Outpatient Hip and Knee Replacement: A Prognostic Study.Acta anaesthesiologica Scandinavica · 2026Article
- Comparative analysis of supervised machine learning models for predicting surgeon fixation choice in total knee arthroplasty.BMC medical informatics and decision making · 2026Article
- Mapping the integration of artificial intelligence in knee replacement surgery: a data-driven bibliometric analysis with emphasis on robotic innovation.Journal of robotic surgery · 2026Article
- Optimizing Early Recovery Quality in Same-Day Discharge Total Hip Arthroplasty: A Narrative Review.Clinical interventions in aging · 2026Review
- High patient and surgeon satisfaction with ChatGPT-generated responses to real patient questions regarding total knee arthroplasty.Journal of orthopaedic surgery and research · 2025Article
- Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions.Journal of medical systems · 2025Review
- Blood transfusion in total knee arthroplasty and total hip arthroplasty: A nationwide study of complications, costs and predictive modelling.Journal of experimental orthopaedics · 2025Article
- Assessment of 90-Day Outcomes Following Total Joint Arthroplasty in Ambulatory Surgery Centers, Hospital Outpatient Departments, and Hospitals: A Michigan Arthroplasty Registry Collaborative Quality Initiative Analysis.Arthroplasty today · 2025Article
- Outpatient vs. inpatient designation in total hip arthroplasty: can we predict who will require hospitalization?Archives of orthopaedic and trauma surgery · 2024Article
- Oral Postoperative Antibiotic Prophylaxis for Outpatient Total Hip and Knee Arthroplasty: Describing Current Practices.The Journal of arthroplasty · 2024Article
- Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.World neurosurgery: X · 2024Review
- Enhanced recovery after surgery (ERAS) protocol reduces need for patient selection for day surgery total knee arthroplasty.Journal of orthopaedics · 2024Article
- Adapting Physical Therapy Practice for the "Short-Stay" Total Joint Arthroplasty Patient: A Commentary.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2024Article
- Outpatient Total Joint Arthroplasty: Are We Closing the Racial Disparities Gap?Journal of racial and ethnic health disparities · 2023Article
- The use of machine learning for predicting candidates for outpatient spine surgery: a review.Journal of spine surgery (Hong Kong) · 2023Review
- A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty.Arthroplasty (London, England) · 2023Review
- Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review.Arthroplasty today · 2023Article
- Machine Learning Algorithm to Predict Worsening of Flexion Range of Motion After Total Knee Arthroplasty.Arthroplasty today · 2022Article
- The Use of Artificial Intelligence for Orthopedic Surgical Backlogs Such as the One Following the COVID-19 Pandemic: A Narrative Review.JB & JS open accessReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient selection for outpatient total joint arthroplasty (TJA) is important for optimizing patient outcomes. This study develops machine learning models that may aid in patient selection for outpatient TJA based on medical comorbidities and demographic factors.
methodsThis study queried elective total knee arthroplasty (TKA) and total hip arthroplasty (THA) cases during 2010-2018 in the American College of Surgeons National Surgical Quality Improvement Program. Artificial neural network models predicted same-day discharge and length of stay (LOS) fewer than 2 days (short LOS). Multiple linear and logistic regression analyses were used to identify variables significantly associated with predicted outcomes.
resultsA total of 284,731 TKA cases and 153,053 THA cases met inclusion criteria. For TKA, prediction of short LOS had an area under the receiver operating characteristic curve (AUC) of 0.767 and accuracy of 84.1%; prediction of same-day discharge had an AUC of 0.802 and accuracy of 89.2%. For THA, prediction of short LOS had an AUC of 0.757 and accuracy of 70.6%; prediction of same-day discharge had an AUC of 0.814 and accuracy of 78.8%.
conclusionThis study developed machine learning models for aiding patient selection for outpatient TJA, through accurately predicting short LOS or outpatient vs inpatient cases. As outpatient TJA expands, it will be important to optimize preoperative patient selection and effectively screen surgical candidates from a broader patient population. Incorporating models such as these into electronic medical records could aid in decision-making and resource planning in real time.
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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.