Role of Neck Pain in Defining Clinical Trajectories of Outcomes in Patients With Degenerative Cervical Myelopathy: Results of a Novel Machine Learning Algorithm.

IF 2.6 3区 医学 Q2 CLINICAL NEUROLOGY
Raymond Wong, Mohammed Ali Alvi, Ayesha I Quddusi, Michael G Fehlings
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引用次数: 0

Abstract

Study DesignRetrospective analysis of prospective data.ObjectivesNeck pain represents a crucial factor underscoring a patient's decision to receive surgical intervention for degenerative cervical myelopathy (DCM). However, postoperative pain trajectories are poorly defined. This study aimed to employ machine learning-based trajectory modeling to identify patient subpopulations with distinct pain trajectories after surgery.MethodsWe pooled subjects from three major clinical studies on DCM. Group-based multivariate trajectory (GBMT) modeling was used to classify patients into distinct trajectories based on their neck pain score over one year. Outcome differences were examined with univariate analyses. Predictors of group membership were revealed with multinomial logistic regression.ResultsThree distinct trajectories of neck pain were identified from a total of 968 patients with DCM: "slow pain improvement" (n = 239; 25%), "no pain improvement" (n = 537; 55%), and "fast pain improvement" (n = 192; 20%) groups. Each trajectory exhibited a unique baseline pain profile. The "fast pain improvement" group, comprised of patients experiencing profound neck pain, had the best overall outcomes for pain, NDI, SF-36 PCS, and SF-36 MSC postoperatively. On the other hand, the "no pain improvement" group, consisting of patients with pain and multimodal impairment of moderate severity, had residual pain that remained constant and was least likely to experience functional outcome and quality of life improvement after one year.ConclusionsUnsupervised learning on neck pain identified unique pain recovery trajectories that consist of distinct patient phenotypes. Trajectory grouping offers an important framework to both identify novel DCM subpopulations and predict patterns of pain over time.Clinical Trials Included(1) Assessment of Surgical Techniques for Treating Cervical Spondylotic Myelopathy (CSM); https://clinicaltrials.gov/study/NCT00285337; ClinicalTrials.gov ID NCT00285337. (2) Surgical Treatment of Cervical Spondylotic Myelopathy; https://clinicaltrials.gov/study/NCT00565734; ClinicalTrals.gov ID NCT00565734. (3) Efficacy of Riluzole in Surgical Treatment for Cervical Spondylotic Myelopathy (CSM-Protect) (CSM-Protect); https://clinicaltrials.gov/study/NCT01257828; ClinicalTrials.gov ID NCT01257828.

颈部疼痛在确定退行性颈椎病患者预后的临床轨迹中的作用:一种新的机器学习算法的结果。
研究设计前瞻性资料的回顾性分析。目的颈部疼痛是患者决定接受退行性颈椎病(DCM)手术治疗的关键因素。然而,术后疼痛轨迹定义不清。本研究旨在采用基于机器学习的轨迹建模来识别手术后具有不同疼痛轨迹的患者亚群。方法我们从三个主要的DCM临床研究中收集受试者。使用基于组的多变量轨迹(GBMT)模型根据患者一年内的颈部疼痛评分将患者分为不同的轨迹。结果差异用单变量分析进行检验。用多项逻辑回归分析了群体成员的预测因素。结果968例DCM患者颈部疼痛有三种不同的发展轨迹:“疼痛缓慢改善”(n = 239;25%),“疼痛无改善”(n = 537;55%)和“快速疼痛改善”(n = 192;20%)组。每条轨迹都显示出独特的基线疼痛特征。“快速疼痛改善”组,由经历深度颈部疼痛的患者组成,术后疼痛、NDI、SF-36 PCS和SF-36 MSC的总体结果最好。另一方面,“无疼痛改善”组,由疼痛和中度多模态损伤的患者组成,他们的残余疼痛保持不变,一年后最不可能经历功能结局和生活质量的改善。结论:颈部疼痛的监督学习识别出独特的疼痛恢复轨迹,由不同的患者表型组成。轨迹分组为识别新的DCM亚群和预测疼痛模式提供了一个重要的框架。临床试验包括(1)评估治疗脊髓型颈椎病(CSM)的手术技术;https://clinicaltrials.gov/study/NCT00285337;ClinicalTrials.gov编号NCT00285337。(2)脊髓型颈椎病的外科治疗;https://clinicaltrials.gov/study/NCT00565734;ClinicalTrals.gov ID NCT00565734。(3)利鲁唑在脊髓型颈椎病(CSM-Protect)手术治疗中的疗效;https://clinicaltrials.gov/study/NCT01257828;ClinicalTrials.gov编号NCT01257828。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Global Spine Journal
Global Spine Journal Medicine-Surgery
CiteScore
6.20
自引率
8.30%
发文量
278
审稿时长
8 weeks
期刊介绍: Global Spine Journal (GSJ) is the official scientific publication of AOSpine. A peer-reviewed, open access journal, devoted to the study and treatment of spinal disorders, including diagnosis, operative and non-operative treatment options, surgical techniques, and emerging research and clinical developments.GSJ is indexed in PubMedCentral, SCOPUS, and Emerging Sources Citation Index (ESCI).
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