ArticleComputational and mathematical methods in medicine2022
Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Incidental Durotomy Significantly Increases the Risk of Postoperative Infection Following Lumbar Spine Surgery for Degenerative Conditions: A Systematic Review and Meta-analysis.International journal of spine surgery · 2025Article
- Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods.Health information science and systems · 2025Article
- Artificial Intelligence and Its Impact on the Management of Lumbar Degenerative Pathology: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data.Diagnostics (Basel, Switzerland) · 2025Article
- Risk factors and predictive model for early surgical site infection following single-level PLIF in diabetic patients.Frontiers in surgery · 2025Article
- Article
- Prediction models for risk assessment of surgical site infection after spinal surgery: A systematic review.North American Spine Society journal · 2024Review
- Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review.Brain sciences · 2023Review
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Authors and funding
8 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: Surgical site infection is one of the serious complications after lumbar fusion. Early prediction and timely intervention can reduce the harm to patients. The aims of this study were to construct and validate a machine learning model for predicting surgical site infection after posterior lumbar interbody fusion, to screen out the most important risk factors for surgical site infection, and to explore whether synthetic minority oversampling technique could improve the model performance. Method: This study reviewed 584 patients who underwent posterior lumbar interbody fusion for degenerative lumbar disease at our center from January 2019 to August 2021. Clinical information and laboratory test data were collected from the electronic medical records. The original dataset was divided into training set and validation set in a 1 : 1 ratio. Seven machine learning algorithms were used to develop predictive models; the training set of each model was resampled using synthetic minority oversampling technique. Finally, the model performance was assessed in the validation set. Results: Of the 584 patients, 33 (5.65%) occurred surgical site infection. Stepwise logistic regression showed that preoperative albumin level (OR 0.659, 95% CI 0.563-0.756), diabetes (OR 9.129, 95% CI 3.816-23.126), intraoperative dural tear (OR 8.436, 95% CI 2.729-25.334), and rheumatic disease (OR 8.471, 95% CI 1.743-39.567) were significant predictors associated with surgical site infection. The performance of the AdaBoost Classification Trees model was the best among the seven machine learning models, and synthetic minority oversampling technique improved the performance of all models. Conclusion: The prediction model we constructed based on machine learning and synthetic minority oversampling technique can accurately predict surgical site infection, which is conducive to clinical decision-making and optimization of perioperative management.
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