ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2019
Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.
Article in 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, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 40 citations in OpenAlex.
- Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning.Global spine journal · 2024Article
- Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review.Brain sciences · 2023Review
- Machine Learning Applications in Spine Surgery.Cureus · 2023Review
- The use of machine learning for predicting candidates for outpatient spine surgery: a review.Journal of spine surgery (Hong Kong) · 2023Review
- Artificial Intelligence and Machine Learning Applications in Spine Surgery.International journal of spine surgery · 2023Article
- Early prediction of patient discharge disposition in acute neurological care using machine learning.BMC health services research · 2022Article
- Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review.Global spine journal · 2022Article
- Artificial intelligence in spine care: current applications and future utility.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 · 2022Review
- Could Machine Learning Better Predict Postoperative C5 Palsy of Cervical Ossification of the Posterior Longitudinal Ligament?Clinical spine surgery · 2022Article
- Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.Journal of personalized medicine · 2022Review
- Validation of the ACS-NSQIP Risk Calculator: A Machine-Learning Risk Tool for Predicting Complications and Mortality Following Adult Spinal Deformity Corrective Surgery.International journal of spine surgery · 2021Article
- Using machine learning methods to predict nonhome discharge after elective total shoulder arthroplasty.JSES international · 2021Article
- Outcomes of Same-Day Orthopedic Surgery: Are Spine Patients More Likely to Have Optimal Immediate Recovery From Outpatient Procedures?International journal of spine surgery · 2021Article
- Prediction Models in Degenerative Spine Surgery: A Systematic Review.Global spine journal · 2021Article
- Machine learning models to predict length of stay and discharge destination in complex head and neck surgery.Head & neck · 2021Article
- Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms.Geriatric orthopaedic surgery & rehabilitation · 2021Article
- Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle.EFORT open reviews · 2020Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis.
methodsThe National Surgical Quality Improvement Program (NSQIP) database was used to select patients that underwent surgical treatment for degenerative spondylolisthesis between 2009 and 2016. Our primary outcome measure was non-home discharge which was defined as any discharge not to home for which we grouped together all non-home discharge destinations including rehabilitation facility, skilled nursing facility, and unskilled nursing facility. We used Akaike information criterion to select the most appropriate model based on the outcomes of the stepwise backward logistic regression. Four machine learning algorithms were developed to predict discharge placement and were assessed by discrimination, calibration, and overall performance.
resultsNine thousand three hundred and thirty-eight patients were included. Median age was 63 (interquartile range [IQR] 54-71), and 63% (n = 5,887) were female. The non-home discharge rate was 18.6%. Our models included age, sex, diabetes, elective surgery, BMI, procedure, number of levels, ASA class, preoperative white blood cell count, and preoperative creatinine. The Bayes point machine was considered the best model based on discrimination (AUC = 0.753), calibration (slope = 1.111; intercept = - 0.002), and overall model performance (Brier score = 0.132).
conclusionThis study has shown that it is possible to create a predictive machine learning algorithm with both good accuracy and calibration to predict discharge placement. Using our methodology, this type of model can be developed for many other conditions and (elective) treatments. These slides can be retrieved under Electronic Supplementary Material.
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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.