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Predicting Mechanical Complications After Adult Spinal Deformity Operation Using a Machine Learning Based on Modified Global Alignment and Proportion Scoring With Body Mass Index and Bone Mineral Density

Authors
 Sung Hyun Noh  ;  Hye Sun Lee  ;  Go Eun Park  ;  Yoon Ha  ;  Jeong Yoon Park  ;  Sung Uk Kuh  ;  Dong Kyu Chin  ;  Keun Su Kim  ;  Yong Eun Cho  ;  Sang Hyun Kim  ;  Kyung Hyun Kim 
Citation
 NEUROSPINE, Vol.20(1) : 265-274, 2023-03 
Journal Title
NEUROSPINE
ISSN
 2586-6583 
Issue Date
2023-03
Keywords
Adult spinal deformity ; Body mass index ; Bone mineral density ; Machine learning ; Mechanical complication ; Random forest
Abstract
Objective: This study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors.

Methods: Between January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥ 2 years, were included in the study. The data were stratified into training (n = 167, 70%) and test (n = 71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network.

Results: Body mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p < 0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817-0.925), 0.942 (0.911-0.974), 1.000 (1.000-1.000), and 0.947 (0.915-0.980), respectively, and the accuracies were 0.784 (0.722-0.847), 0.868 (0.817-0.920), 1.000 (1.000-1.000), and 0.856 (0.803-0.909), respectively. In the test set, the AUROCs were 0.785 (0.678-0.893), 0.808 (0.702-0.914), 0.810 (0.710-0.910), and 0.730 (0.610-0.850), respectively, and the accuracies were 0.732 (0.629-0.835), 0.718 (0.614-0.823), 0.732 (0.629-0.835), and 0.620 (0.507-0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset.

Conclusion: This study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery.
Files in This Item:
T202307332.pdf Download
DOI
10.14245/ns.2244854.427
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kuh, Sung Uk(구성욱) ORCID logo https://orcid.org/0000-0003-2566-3209
Kim, Kyung Hyun(김경현)
Kim, Keun Su(김근수) ORCID logo https://orcid.org/0000-0002-3384-5638
Noh, Sung Hyun(노성현)
Park, Goeun(박고은)
Park, Jeong Yoon(박정윤) ORCID logo https://orcid.org/0000-0002-3728-7784
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
Cho, Yong Eun(조용은) ORCID logo https://orcid.org/0000-0001-9815-2720
Chin, Dong Kyu(진동규) ORCID logo https://orcid.org/0000-0002-9835-9294
Ha, Yoon(하윤)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/197558
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