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BackgroundHeterogeneity in patients with low back pain (LBP) is well recognised and different approaches to subgrouping have been proposed. Latent Class Analysis (LCA) is a statistical technique that is increasingly being used to identify subgroups based on patient characteristics. However, as LBP is a complex multi-domain condition, the optimal approach when using LCA is unknown. Therefore, this paper describes the exploration of two approaches to LCA that may help improve the identification of clinically relevant and interpretable LBP subgroups.MethodsFrom 928 LBP patients consulting a chiropractor, baseline data were used as input to the statistical subgrouping. In a single-stage LCA, all variables were modelled simultaneously to identify patient subgroups. In a two-stage LCA, we used the latent class membership from our previously published LCA within each of six domains of health (activity, contextual factors, pain, participation, physical impairment and psychology) (first stage) as the variables entered into the second stage of the two-stage LCA to identify patient subgroups. The description of the results of the single-stage and two-stage LCA was based on a combination of statistical performance measures, qualitative evaluation of clinical interpretability (face validity) and a subgroup membership comparison.ResultsFor the single-stage LCA, a model solution with seven patient subgroups was preferred, and for the two-stage LCA, a nine patient subgroup model. Both approaches identified similar, but not identical, patient subgroups characterised by (i) mild intermittent LBP, (ii) recent severe LBP and activity limitations, (iii) very recent severe LBP with both activity and participation limitations, (iv) work-related LBP, (v) LBP and several negative consequences and (vi) LBP with nerve root involvement.ConclusionsBoth approaches identified clinically interpretable patient subgroups. The potential importance of these subgroups needs to be investigated by exploring whether they can be identified in other cohorts and by examining their possible association with patient outcomes. This may inform the selection of a preferred LCA approach.Electronic supplementary materialThe online version of this article (doi:10.1186/s12891-017-1411-x) contains supplementary material, which is available to authorized users.

BackgroundLatent class analysis (LCA) is increasingly being used in health research, but optimal approaches to handling complex clinical data are unclear. One issue is that commonly used questionnaires are multidimensional, but expressed as summary scores. Using the example of low back pain (LBP), the aim of this study was to explore and descriptively compare the application of LCA when using questionnaire summary scores and when using single items to subgrouping of patients based on multidimensional data.Materials and methodsBaseline data from 928 LBP patients in an observational study were classified into four health domains (psychology, pain, activity, and participation) using the World Health Organization’s International Classification of Functioning, Disability, and Health framework. LCA was performed within each health domain using the strategies of summary-score and single-item analyses. The resulting subgroups were descriptively compared using statistical measures and clinical interpretability.ResultsFor each health domain, the preferred model solution ranged from five to seven subgroups for the summary-score strategy and seven to eight subgroups for the single-item strategy. There was considerable overlap between the results of the two strategies, indicating that they were reflecting the same underlying data structure. However, in three of the four health domains, the single-item strategy resulted in a more nuanced description, in terms of more subgroups and more distinct clinical characteristics.ConclusionIn these data, application of both the summary-score strategy and the single-item strategy in the LCA subgrouping resulted in clinically interpretable subgroups, but the single-item strategy generally revealed more distinguishing characteristics. These results 1) warrant further analyses in other data sets to determine the consistency of this finding, and 2) warrant investigation in longitudinal data to test whether the finer detail provided by the single-item strategy results in improved prediction of outcomes and treatment response.

BackgroundHeterogeneity 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’).MethodsThis 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.ResultsThe 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.ConclusionsLatent 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12891-017-1708-9) contains supplementary material, which is available to authorized users.

Background Manual therapy is a cornerstone of chiropractic education, whereby students work towards a level of skill and expertise that is regarded as competent to work within the field of chiropractic. Due to the COVID-19 pandemic, chiropractic programs in every region around the world had to make rapid changes to the delivery of manual therapy technique education, however what those changes looked like was unknown. Aims The aims of this study were to describe the immediate actions made by chiropractic programs to deliver education for manual therapy techniques and to summarise the experience of academics who teach manual therapy techniques during the initial outbreak of COVID-19 pandemic. Methods A qualitative descriptive approach was used to describe the immediate actions made by chiropractic programs to deliver manual therapy technique education during the COVID-19 pandemic. Chiropractic programs were identified from the webpages of the Councils on Chiropractic Education International and the Council on Chiropractic Education – USA. Between May and June 2020, a convenience sample of academics who lead or teach in manual therapy technique in those programs were invited via email to participate in an online survey with open-ended questions. Responses were entered into the NVivo software program and analysed using a reflexive thematic analysis by a qualitative researcher independent to the data collection. Results Data from 16 academics in 13 separate chiropractic programs revealed five, interconnected themes: Immediate response; Move to online delivery; Impact on learning and teaching; Additional challenges faced by educators; and Ongoing challenges post lockdown. Conclusion This study used a qualitative descriptive approach to describe how some chiropractic programs immediately responded to the initial outbreak of the COVID-19 pandemic in their teaching of manual therapy techniques. Chiropractic programs around the world provided their students with rapid, innovative learning strategies, in an attempt to maintain high standards of chiropractic education; however, challenges included maintaining student engagement in an online teaching environment, psychomotor skills acquisition and staff workload.