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Predictive Classification System for Low Back Pain Based on Unsupervised Clustering
STUDY DESIGN: Retrospective study. OBJECTIVE: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. METHOD: Normal subjects with repeated lumbar MRI scans were re...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240599/ https://www.ncbi.nlm.nih.gov/pubmed/33896208 http://dx.doi.org/10.1177/21925682211001813 |
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author | Jin, Lixia Jiang, Chang Gu, Lishu Jiang, Mengying Shi, Yuanlu Qu, Qixun Shen, Na Shi, Weibin Cao, Yuanwu Chen, Zixian Jiang, Chun Feng, Zhenzhou Shen, Linghao Jiang, Xiaoxing |
author_facet | Jin, Lixia Jiang, Chang Gu, Lishu Jiang, Mengying Shi, Yuanlu Qu, Qixun Shen, Na Shi, Weibin Cao, Yuanwu Chen, Zixian Jiang, Chun Feng, Zhenzhou Shen, Linghao Jiang, Xiaoxing |
author_sort | Jin, Lixia |
collection | PubMed |
description | STUDY DESIGN: Retrospective study. OBJECTIVE: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. METHOD: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method. RESULTS: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP (P = .017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1. CONCLUSION: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment. |
format | Online Article Text |
id | pubmed-10240599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-102405992023-06-06 Predictive Classification System for Low Back Pain Based on Unsupervised Clustering Jin, Lixia Jiang, Chang Gu, Lishu Jiang, Mengying Shi, Yuanlu Qu, Qixun Shen, Na Shi, Weibin Cao, Yuanwu Chen, Zixian Jiang, Chun Feng, Zhenzhou Shen, Linghao Jiang, Xiaoxing Global Spine J Original Articles STUDY DESIGN: Retrospective study. OBJECTIVE: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. METHOD: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method. RESULTS: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP (P = .017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1. CONCLUSION: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment. SAGE Publications 2021-04-26 2023-04 /pmc/articles/PMC10240599/ /pubmed/33896208 http://dx.doi.org/10.1177/21925682211001813 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Articles Jin, Lixia Jiang, Chang Gu, Lishu Jiang, Mengying Shi, Yuanlu Qu, Qixun Shen, Na Shi, Weibin Cao, Yuanwu Chen, Zixian Jiang, Chun Feng, Zhenzhou Shen, Linghao Jiang, Xiaoxing Predictive Classification System for Low Back Pain Based on Unsupervised Clustering |
title | Predictive Classification System for Low Back Pain Based on Unsupervised Clustering |
title_full | Predictive Classification System for Low Back Pain Based on Unsupervised Clustering |
title_fullStr | Predictive Classification System for Low Back Pain Based on Unsupervised Clustering |
title_full_unstemmed | Predictive Classification System for Low Back Pain Based on Unsupervised Clustering |
title_short | Predictive Classification System for Low Back Pain Based on Unsupervised Clustering |
title_sort | predictive classification system for low back pain based on unsupervised clustering |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240599/ https://www.ncbi.nlm.nih.gov/pubmed/33896208 http://dx.doi.org/10.1177/21925682211001813 |
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