Molgaard Nielsen Anne, Hestbaek Lise, Vach Werner, Kent Peter, Kongsted Alice
Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark.
Nordic Institute of Chiropractic and Clinical Biomechanics, University of Southern Denmark, 5230, Odense M, Denmark.
BMC Musculoskelet Disord. 2017 Aug 9;18(1):345. doi: 10.1186/s12891-017-1708-9.
Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables').
This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above.
The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up.
Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients.
腰痛患者的异质性已得到充分认识，并且已经提出了不同的亚组划分方法。一种越来越常用的统计技术是潜在类别分析，因为它基于模式识别进行亚组划分，准确性很高。此前，我们基于患者基线特征（病史和体格检查）的潜在类别分析，为腰痛患者的亚组划分提出了两种新方法，单阶段分析时产生了7个亚组，两阶段方法时产生了9个亚组。然而，它们的预后能力尚未得到探索。本研究（i）确定亚组划分方法是否与未来的疼痛强度、疼痛频率和残疾结局相关，（ii）评估这两种方法中的一种是否与这些结局有更强或更一致的关联，以及（iii）与以下变量相比，评估新亚组划分的性能：两种现有的亚组划分工具（STarT Back工具和魁北克工作组分类）、四个基线特征以及一组先前确定的特定领域患者分类（统称为“比较变量”）。
这是一项对928名在初级保健机构咨询腰痛问题的患者进行的纵向队列研究。在线性回归模型中测试了每种亚组划分方法与2周、3个月和12个月时的结局以及每周短信回复之间的关联，并将它们的预后能力（解释方差）与上述比较变量的预后能力进行了比较。
先前确定的两种亚组划分与所有结局的关联相似。两种亚组划分的预后能力均优于比较变量，但参与者的康复信念和特定领域分类除外，不过仍然有限。疼痛强度的解释方差范围为4.3%-6.9%，残疾的解释方差范围为6.8%-20.3%，在2周随访时最高。
与一系列变量相比，潜在类别衍生的亚组提供了额外的预后信息，但改善程度不足以保证进一步开发成一种新的预后工具。进一步的研究可以调查这些新的亚组划分方法是否有助于改进现有的腰痛患者亚组划分工具。
