ArticleGlobal spine journal2023
Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion.
Article in Global spine journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.Journal of thrombosis and thrombolysis · 2026Pooled it
- Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning improves prediction of pulmonary thromboembolism and reduces unnecessary computed tomography scans in the emergency department.Scientific reports · 2026Article
- Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.World journal of orthopedics · 2025Review
- Retrospective Single-Center Analysis of 5575 Spinal Surgeries for Complication Associations and Potential Future Use of Generated Data.Journal of clinical medicine · 2025Article
- Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records: A Retrospective Cohort Study.Clinical epidemiology · 2025Article
- Venous Thromboembolism Rates Have Not Decreased in Elective Lumbar Fusion Surgery from 2011 to 2020.Global spine journal · 2024Article
- Machine learning approach to predict venous thromboembolism among patients undergoing multi-level spinal posterior instrumented fusion.Journal of spine surgery (Hong Kong) · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Computational Modeling, Augmented Reality, and Artificial Intelligence in Spine Surgery.Advances in experimental medicine and biology · 2024Review
- 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
- Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning.JOR spine · 2022Article
- Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.Journal of personalized medicine · 2022Review
- Using machine learning and big data for the prediction of venous thromboembolic events after spine surgery: A single-center retrospective analysis of multiple models on a cohort of 6869 patients.Journal of craniovertebral junction & spineArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designRetrospective review.
objectiveTo use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology.
methodsPatients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic.
results13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all,
conclusionPredictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.
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