Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics

Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to c...

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Vydáno v:	Frontiers in surgery Ročník 9; s. 935656
Hlavní autoři:	Zhou, Chenxing, Huang, ShengSheng, Liang, Tuo, Jiang, Jie, Chen, Jiarui, Chen, Tianyou, Chen, Liyi, Sun, Xuhua, Zhu, Jichong, Wu, Shaofeng, Ye, Zhen, Guo, Hao, Chen, Wenkang, Liu, Chong, Zhan, Xinli
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Frontiers Media S.A 25.07.2022
Témata:	anterior cervical corpectomy and fusion anterior cervical discectomy and fusion cervical spondylotic myelopathy cluster analysis machine learning Surgery
ISSN:	2296-875X, 2296-875X
On-line přístup:	Získat plný text
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Shrnutí:	Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification.BackgroundAnterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification.A total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering.MethodsA total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering.We identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different.ResultsWe identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different.Our results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.ConclusionsOur results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Ziya Levent Gokaslan, Brown University, United States Νikolaos Ch. Syrmos, Aristotle University of Thessaloniki, Greece Edited by: Mario Ganau, Oxford University Hospitals NHS Trust, United Kingdom These authors have contributed equally to this work Specialty Section: This article was submitted to Neurosurgery, a section of the journal Frontiers in Surgery
ISSN:	2296-875X 2296-875X
DOI:	10.3389/fsurg.2022.935656