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颈椎脊髓病患者手术治疗后的分类和预后因素:聚类分析。

Classification and prognostic factors of patients with cervical spondylotic myelopathy after surgical treatment: a cluster analysis.

机构信息

Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China.

Engineering Research Center of Bone and Joint Precision Medicine, 49 North Garden Road, Haidian District, Beijing, 100191, China.

出版信息

Sci Rep. 2024 Jan 2;14(1):99. doi: 10.1038/s41598-023-49477-4.

Abstract

Identifying potential prognostic factors of CSM patients could improve doctors' clinical decision-making ability. The study retrospectively collected the baseline data of population characteristics, clinical symptoms, physical examination, neurological function and quality of life scores of patients with CSM based on the clinical big data research platform. The modified Japanese Orthopedic Association (mJOA) score and SF-36 score from the short-term follow-up data were entered into the cluster analysis to characterize postoperative residual symptoms and quality of life. Four clusters were yielded representing different patterns of residual symptoms and quality of patients' life. Patients in cluster 2 (mJOA RR 55.8%) and cluster 4 (mJOA RR 55.8%) were substantially improved and had better quality of life. The influencing factors for the better prognosis of patients in cluster 2 were young age (50.1 ± 11.8), low incidence of disabling claudication (5.0%) and pathological signs (63.0%), and good preoperative SF36-physiological function score (73.1 ± 24.0) and mJOA socre (13.7 ± 2.8); and in cluster 4 the main influencing factor was low incidence of neck and shoulder pain (11.7%). We preliminarily verified the reliability of the clustering results with the long-term follow-up data and identified the preoperative features that were helpful to predict the prognosis of the patients. This study provided reference and research basis for further study with a larger sample data, extracting more patient features, selecting more follow-up nodes, and improving clustering algorithm.

摘要

识别 CSM 患者的潜在预后因素可以提高医生的临床决策能力。本研究基于临床大数据研究平台,回顾性收集了 CSM 患者的人口特征、临床症状、体格检查、神经功能和生活质量评分的基线数据。将短期随访数据中的改良日本矫形协会(mJOA)评分和 SF-36 评分输入聚类分析,以描述术后残留症状和生活质量。得到了 4 个聚类,代表了不同的残留症状和生活质量模式。聚类 2(mJOA RR 55.8%)和聚类 4(mJOA RR 55.8%)患者的恢复情况明显改善,生活质量较好。聚类 2 患者预后较好的影响因素为年龄较小(50.1±11.8)、致残性跛行(5.0%)和病理体征(63.0%)发生率低、术前 SF36-生理功能评分(73.1±24.0)和 mJOA 评分(13.7±2.8)较好;聚类 4 的主要影响因素是颈肩部疼痛发生率低(11.7%)。我们用长期随访数据初步验证了聚类结果的可靠性,并确定了有助于预测患者预后的术前特征。本研究为进一步研究提供了参考和研究基础,包括增加样本量、提取更多患者特征、选择更多随访节点和改进聚类算法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75a1/10762243/81ae310a23a9/41598_2023_49477_Fig1_HTML.jpg

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