无症状中老年人矢状脊柱对齐的骨盆发生率依赖聚类:一种机器学习方法。

IF 2.6 2区 医学 Q2 CLINICAL NEUROLOGY
Spine Pub Date : 2025-06-24 DOI:10.1097/BRS.0000000000005441
Qijun Wang, Dongfan Wang, Xiangyu Li, Weiguo Zhu, Peng Cui, Zheng Wang, Wei Wang, Jeffrey C Wang, Xiaolong Chen, Shibao Lu
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引用次数: 0

摘要

研究设计:横断面队列研究。目的:本研究旨在利用无监督机器学习(ML)技术完善无症状中老年人群脊柱矢状形态分类,并利用这些发现,为不同形态亚型的成人脊柱畸形(ASD)患者提出并验证手术矫正参考。背景资料总结:矢状位对齐的恢复是ASD手术中预防机械并发症和获得良好临床效果的关键。然而,在目前的ASD调整策略下,机械并发症的发生率和临床结果的差异很大,严重阻碍了最佳手术计划的决策过程。方法:本研究横断面纳入无症状的中国中老年成年人。矢状脊柱形态簇和基于骨盆发生率的ASD矫正手术的校正标准是使用无监督ML算法从全脊柱x线片中得出的。为了从外部验证在无症状成人中确定的矫正策略,在随访期间对一组接受矫正手术的矢状面畸形ASD患者进行了术后机械并发症、计划外再手术、计划外再入院和临床结果的连续队列检查。结果:共纳入635名无症状成人进行形态学分层,并纳入103名矢状畸形ASD患者进行验证。无监督ML算法成功地将脊柱形态分层为四个簇。通过回归算法计算的基于骨盆发生率的手术矫正标准显示出合理的临床相关性,在随访期间,恢复组(符合矫正标准)的术后机械并发症、计划外再手术、计划外再入院发生率显著降低,患者报告的预后较好。结论:在本研究中,无监督ML算法有效地将无症状的矢状脊柱形态划分为四个不同的簇。使用基于骨盆发生率的比例矫正标准,ASD患者可以预期脊柱矫正手术后机械并发症的发生率降低并改善临床结果。证据级别:Ⅲ。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pelvic Incidence-Dependent Clustering of Sagittal Spinal Alignment in Asymptomatic Middle-Aged and Elderly Adults: A Machine Learning Approach.

Study design: A cross-sectional cohort study.

Objective: This study aimed to refine the sagittal morphological classification of the spine in asymptomatic middle-aged and elderly adult populations using the unsupervised machine learning (ML) techniques and, by leveraging these findings, to propose and validate a surgical correction reference for adult spinal deformity (ASD) patients across different morphological subtypes.

Summary of background data: Restoration of sagittal alignment is the key to preventing mechanical complications and achieving good clinical outcomes in ASD surgery. However, high variations in the reported incidence of mechanical complications and clinical outcomes under current ASD realignment strategies have severely impeded the decision-making process for the optimal surgical plan.

Methods: This study cross-sectionally enrolled asymptomatic middle-aged and elderly Chinese adults. Sagittal spinal morphology clusters and pelvic incidence-based correction criteria for ASD realignment surgery were derived from whole spine radiographs using unsupervised ML algorithms. To externally validate the realignment strategy identified in asymptomatic adults, a consecutive cohort of ASD patients with sagittal deformity who underwent realignment surgery was examined for postoperative mechanical complications, unplanned reoperation, unplanned readmission, and clinical outcomes during follow-up.

Results: A total of 635 asymptomatic adults were enrolled for morphological stratification, and 103 ASD patients with sagittal deformity were included for validation. The unsupervised ML algorithm successfully stratified spinal morphology into four clusters. The pelvic incidence-based surgical correction criteria computed by the regression algorithm demonstrated plausible clinical relevance, evidenced by the significantly lower incidence of postoperative mechanical complications, unplanned reoperation, unplanned readmission, and superior patient-reported outcomes in the restored group (conforming to the correction criteria) during follow-up.

Conclusion: In this study, unsupervised ML algorithm effectively partitioned asymptomatic sagittal spinal morphology into four distinct clusters. Using the pelvic incidence-based proportional correction criteria, ASD patients can anticipate a reduced incidence of mechanical complications and improved clinical outcomes following spinal realignment surgery.

Level of evidence: Ⅲ.

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来源期刊
Spine
Spine 医学-临床神经学
CiteScore
5.90
自引率
6.70%
发文量
361
审稿时长
6.0 months
期刊介绍: Lippincott Williams & Wilkins is a leading international publisher of professional health information for physicians, nurses, specialized clinicians and students. For a complete listing of titles currently published by Lippincott Williams & Wilkins and detailed information about print, online, and other offerings, please visit the LWW Online Store. Recognized internationally as the leading journal in its field, Spine is an international, peer-reviewed, bi-weekly periodical that considers for publication original articles in the field of Spine. It is the leading subspecialty journal for the treatment of spinal disorders. Only original papers are considered for publication with the understanding that they are contributed solely to Spine. The Journal does not publish articles reporting material that has been reported at length elsewhere.
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