ArticleJournal of clinical medicine2021
Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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
12 citing papers in PubMed, 26 citations in OpenAlex.
- Algorithmic fairness reporting in artificial intelligence for spinal deformity: a systematic review.Spine deformity · 2026Review
- Improving postoperative length of stay forecasting with retrieval-augmented prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.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 · 2025Article
- Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive geriatric assessment.BMC medical informatics and decision making · 2025Article
- Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.Asian spine journal · 2024Article
- Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.Journal of orthopaedic surgery and research · 2024Observational
- Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review.Brain sciences · 2023Review
- The use of machine learning for predicting candidates for outpatient spine surgery: a review.Journal of spine surgery (Hong Kong) · 2023Review
- Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care.International journal of spine surgery · 2023Article
- Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data.European journal of medical research · 2022Article
- Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.Journal of personalized medicine · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon's NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566-0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.
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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.