{"title":"Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis.","authors":"Zixiang Pang, Yangqin Ou, Jiawei Liang, Shengbin Huang, Jiayi Chen, Shengsheng Huang, Qian Wei, Yuzhen Liu, Hongyuan Qin, Yuanming Chen","doi":"10.2147/IJGM.S487967","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The objective of this study aimed to investigate the risk factors for poor wound healing (PWH) after posterior lumbar spinal fusion. Currently, there is limited research on the application of machine learning in analyzing PWH after spinal surgery. Thus, our primary aim is to using machine learning identify these risk factors and construct a clinical risk prediction model.</p><p><strong>Methods: </strong>We retrospectively reviewed 2516 patients who underwent posterior lumbar spinal fusion at Guangxi Medical University's Second Affiliated Hospital between August 2021 and August 2023. The data was divided into test and validation groups in a 7:3 ratio. In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. The top six models from the eight machine learning models with the highest area under curve (AUC) values were selected and used to construct a dynamic nomograms model. Model performance was evaluated using receiver operating characteristic (ROC) and calibration curves. The model's internal performance was then verified in the validation group using ROC and calibration curves.</p><p><strong>Results: </strong>Data from 2516 patients were collected, with 411 eligible cases selected. By combining logistic regression analysis with six machine learning algorithms, this study identified six predictors associated with PWH: subcutaneous lumbar spine index(SLSI), albumin, postoperative glucose, cerebrospinal fluid leakage(CSFL), neutrophil (NEU), and C-reactive protein(CRP). These predictors were used to develop a prediction model, visually represented through a nomogram. The AUC value in the test group was 0.981, and the C-index of the model was 0.986 (95% CI 0.966-0.995), indicating excellent predictive capability. Calibration curve analysis showed good consistency between nomogram-predicted values and actual measurements.</p><p><strong>Conclusion: </strong>SLSI, albumin, postoperative glucose, CSFL, NEU and CRP were identified as significant risk factors for PWH after posterior lumbar spinal fusion. The developed prediction model exhibited excellent predictive accuracy and usefulness.</p>","PeriodicalId":14131,"journal":{"name":"International Journal of General Medicine","volume":"17 ","pages":"5479-5491"},"PeriodicalIF":2.1000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606187/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of General Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IJGM.S487967","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: The objective of this study aimed to investigate the risk factors for poor wound healing (PWH) after posterior lumbar spinal fusion. Currently, there is limited research on the application of machine learning in analyzing PWH after spinal surgery. Thus, our primary aim is to using machine learning identify these risk factors and construct a clinical risk prediction model.
Methods: We retrospectively reviewed 2516 patients who underwent posterior lumbar spinal fusion at Guangxi Medical University's Second Affiliated Hospital between August 2021 and August 2023. The data was divided into test and validation groups in a 7:3 ratio. In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. The top six models from the eight machine learning models with the highest area under curve (AUC) values were selected and used to construct a dynamic nomograms model. Model performance was evaluated using receiver operating characteristic (ROC) and calibration curves. The model's internal performance was then verified in the validation group using ROC and calibration curves.
Results: Data from 2516 patients were collected, with 411 eligible cases selected. By combining logistic regression analysis with six machine learning algorithms, this study identified six predictors associated with PWH: subcutaneous lumbar spine index(SLSI), albumin, postoperative glucose, cerebrospinal fluid leakage(CSFL), neutrophil (NEU), and C-reactive protein(CRP). These predictors were used to develop a prediction model, visually represented through a nomogram. The AUC value in the test group was 0.981, and the C-index of the model was 0.986 (95% CI 0.966-0.995), indicating excellent predictive capability. Calibration curve analysis showed good consistency between nomogram-predicted values and actual measurements.
Conclusion: SLSI, albumin, postoperative glucose, CSFL, NEU and CRP were identified as significant risk factors for PWH after posterior lumbar spinal fusion. The developed prediction model exhibited excellent predictive accuracy and usefulness.
期刊介绍:
The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas.
A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal.
As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.