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Health seeking behavior as a predictor of healthcare utilization in a population of patients with spinal pain.健康寻求行为作为脊柱疼痛患者人群中医疗保健利用的预测指标。
PLoS One. 2018 Aug 1;13(8):e0201348. doi: 10.1371/journal.pone.0201348. eCollection 2018.
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Can demographic and anthropometric characteristics predict clinical improvement in patients with chronic non-specific low back pain?人口统计学和人体测量特征能否预测慢性非特异性下腰痛患者的临床改善?
Braz J Phys Ther. 2018 Jul-Aug;22(4):328-335. doi: 10.1016/j.bjpt.2018.06.005. Epub 2018 Jun 28.
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The Global Spine Care Initiative: a narrative review of psychological and social issues in back pain in low- and middle-income communities.全球脊柱护理倡议:对中低收入社区腰痛的心理和社会问题的叙述性综述。
Eur Spine J. 2018 Sep;27(Suppl 6):828-837. doi: 10.1007/s00586-017-5434-7. Epub 2018 Jan 27.
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Core outcome measurement instruments for clinical trials in nonspecific low back pain.非特异性下腰痛临床试验的核心结局测量工具。
Pain. 2018 Mar;159(3):481-495. doi: 10.1097/j.pain.0000000000001117.
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Choosing the right outcome measurement instruments for patients with low back pain.选择适合腰痛患者的正确结局测量工具。
Best Pract Res Clin Rheumatol. 2016 Dec;30(6):1003-1020. doi: 10.1016/j.berh.2017.07.001. Epub 2017 Jul 23.
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McKenzie Method of Mechanical Diagnosis and Therapy was slightly more effective than placebo for pain, but not for disability, in patients with chronic non-specific low back pain: a randomised placebo controlled trial with short and longer term follow-up.麦肯锡机械诊断与治疗法在慢性非特异性下腰痛患者的疼痛方面比安慰剂略有效,但在残疾方面无效:一项短期和长期随访的随机安慰剂对照试验。
Br J Sports Med. 2018 May;52(9):594-600. doi: 10.1136/bjsports-2016-097327. Epub 2017 Jul 12.
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Longitudinal Monitoring of Patients With Chronic Low Back Pain During Physical Therapy Treatment Using the STarT Back Screening Tool.使用 STarT 后背筛查工具对接受物理治疗的慢性下背痛患者进行纵向监测。
J Orthop Sports Phys Ther. 2017 May;47(5):314-323. doi: 10.2519/jospt.2017.7199. Epub 2017 Mar 29.
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Generic prognostic factors for musculoskeletal pain in primary care: a systematic review.基层医疗中肌肉骨骼疼痛的一般预后因素:一项系统评价。
BMJ Open. 2017 Jan 17;7(1):e012901. doi: 10.1136/bmjopen-2016-012901.
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Non-specific low back pain.非特异性下背痛。
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How Effective is Physical Therapy for Common Low Back Pain Diagnoses?: A Multivariate Analysis of 4597 Patients.物理治疗对常见下背痛诊断的效果如何?:对4597例患者的多变量分析
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预后变量能否预测慢性下腰痛患者的一系列结局:一项随机对照试验的长期随访二次分析

Do prognostic variables predict a set of outcomes for patients with chronic low back pain: a long-term follow-up secondary analysis of a randomized control trial.

作者信息

Garcia Alessandra Narciso, Costa Leonardo O P, Costa Luciola Da Cunha Menezes, Hancock Mark, Cook Chad

机构信息

a Doctor of Physical Therapy Division, Department of Orthopaedic Surgery, Duke University , Durham , NC , USA.

b Physical Therapy, University Cidade de Sao Paulo , Sao Paulo , Brazil.

出版信息

J Man Manip Ther. 2019 Sep;27(4):197-207. doi: 10.1080/10669817.2019.1597435. Epub 2019 Apr 4.

DOI:10.1080/10669817.2019.1597435
PMID:30946005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7434226/
Abstract

: The objective was to explore for universal prognostic variables, or predictors, across three different outcome measures in patients with chronic low back pain (LBP). We hypothesized that selected prognostic variables would be 'universal' prognostic variables, regardless of the outcome measures used. : This study was a secondary analysis of data from a previous randomized controlled trial comparing the McKenzie treatment approach with placebo in patients with chronic LBP. Ten baseline prognostic variables were explored in predictive models for three outcomes: pain intensity, disability, and global perceived effect, at 6 and 12 months. Predictive models were created using backward stepwise logistic and linear multivariate regression analyses. : Several predictors were present including age, expectancy of improvement, global perceived effect; however, we only identified baseline disability as a universal predictor of outcomes at 6 months. The second most represented universal predictor was baseline pain intensity for outcomes at 12 months. : Only two predictors demonstrated an association with more than one outcome measure. High baseline disability predicts multidimensional outcome measures at 6 months in patients with chronic LBP while baseline pain intensity can best predict the outcome at 12 months. Nevertheless, other predictors seem to be unique to the outcome used. Level of evidence: 2c.

摘要

目的是探索慢性下腰痛(LBP)患者在三种不同结局指标中的通用预后变量或预测因素。我们假设,无论使用何种结局指标,选定的预后变量都将是“通用”的预后变量。

本研究是对先前一项随机对照试验的数据进行的二次分析,该试验比较了麦肯齐治疗方法与安慰剂对慢性LBP患者的疗效。在预测模型中探讨了10个基线预后变量,用于预测6个月和12个月时的三个结局:疼痛强度、功能障碍和整体感知效果。使用向后逐步逻辑回归和线性多元回归分析创建预测模型。

存在多个预测因素,包括年龄、改善预期、整体感知效果;然而,我们仅确定基线功能障碍是6个月时结局的通用预测因素。第二大常见的通用预测因素是12个月时结局的基线疼痛强度。

只有两个预测因素与不止一个结局指标相关。高基线功能障碍可预测慢性LBP患者6个月时的多维结局指标,而基线疼痛强度最能预测12个月时的结局。然而,其他预测因素似乎因所使用的结局而异。证据水平:2c。