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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

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...

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Autores principales: Noh, Sung Hyun, Lee, Hye Sun, Park, Go Eun, Ha, Yoon, Park, Jeong Yoon, Kuh, Sung Uk, Chin, Dong Kyu, Kim, Keun Su, Cho, Yong Eun, Kim, Sang Hyun, Kim, Kyung Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Spinal Neurosurgery Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080453/
https://www.ncbi.nlm.nih.gov/pubmed/37016873
http://dx.doi.org/10.14245/ns.2244854.427
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author Noh, Sung Hyun
Lee, Hye Sun
Park, Go Eun
Ha, Yoon
Park, Jeong Yoon
Kuh, Sung Uk
Chin, Dong Kyu
Kim, Keun Su
Cho, Yong Eun
Kim, Sang Hyun
Kim, Kyung Hyun
author_facet Noh, Sung Hyun
Lee, Hye Sun
Park, Go Eun
Ha, Yoon
Park, Jeong Yoon
Kuh, Sung Uk
Chin, Dong Kyu
Kim, Keun Su
Cho, Yong Eun
Kim, Sang Hyun
Kim, Kyung Hyun
author_sort Noh, Sung Hyun
collection PubMed
description 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. This information can be used to prevent mechanical complications during ASD surgery.
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spelling pubmed-100804532023-04-08 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 Noh, Sung Hyun Lee, Hye Sun Park, Go Eun Ha, Yoon Park, Jeong Yoon Kuh, Sung Uk Chin, Dong Kyu Kim, Keun Su Cho, Yong Eun Kim, Sang Hyun Kim, Kyung Hyun Neurospine Original Article 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. This information can be used to prevent mechanical complications during ASD surgery. Korean Spinal Neurosurgery Society 2023-03 2023-03-31 /pmc/articles/PMC10080453/ /pubmed/37016873 http://dx.doi.org/10.14245/ns.2244854.427 Text en Copyright © 2023 by the Korean Spinal Neurosurgery Society https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Noh, Sung Hyun
Lee, Hye Sun
Park, Go Eun
Ha, Yoon
Park, Jeong Yoon
Kuh, Sung Uk
Chin, Dong Kyu
Kim, Keun Su
Cho, Yong Eun
Kim, Sang Hyun
Kim, Kyung Hyun
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080453/
https://www.ncbi.nlm.nih.gov/pubmed/37016873
http://dx.doi.org/10.14245/ns.2244854.427
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