ArticleInfection and drug resistance2023
Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.
Article in Infection and drug resistance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Development of Machine Learning Models for Predicting Surgical Site Infection After Spinal Surgery.Journal of clinical medicine · 2026Article
- Participant-Aware Model Validation for Repeated-Measures Data: Comparative Cross-Validation Study.JMIR AI · 2026Article
- Multimodal imaging fusion and machine learning model development: differential diagnosis of spinal inflammatory lesions using combined CT hounsfield units and MRI features.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 · 2026Article
- Development and validation of a machine learning model utilizing the ACS-NSQIP database for predicting early postoperative Re-operation in lumbar microdiscectomy.Brain & spine · 2026Article
- Review
- Predicting Surgical Site Infection after Lumbar Laminectomy and Discectomy: A Cutting-edge Algorithmic Approach by Incorporating Ensembled Stacking into the Current State-of-the-art for Automated Machine Learning.Neurosurgical review · 2025Article
- Can artificial intelligence in spine imaging affect current practice? Practical developments and their clinical status.North American Spine Society journal · 2025Article
- Development and validation of machine learning models to predict vertebral artery injury by C2 pedicle screws.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
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Breaking Boundaries in Spinal Surgery: GPT-4's Quest to Revolutionize Surgical Site Infection Management.The Journal of infectious diseases · 2025Article
- Development and validation of a preoperative systemic inflammation-based nomogram for predicting surgical site infection in patients with colorectal cancer.International journal of colorectal disease · 2024Review
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- Machine learning application for prediction of surgical site infection after posterior cervical surgery.International wound journal · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis.International journal of general medicine · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.
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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.