Identifying Treatment Effect Modifiers in the STarT Back Trial: A Secondary Analysis

Identification of patient characteristics influencing treatment outcomes is a top low back pain (LBP) research priority. Results from the STarT Back Trial support the effectiveness of prognostic stratified care for LBP compared to current best care, however patient characteristics associated with treatment response have not yet been explored. The purpose of this secondary analysis was to identify treatment-effect modifiers within the STarT Back Trial at 4 months follow-up (n=688). Treatment response was dichotomized using back-specific physical disability measured by the Roland-Morris Disability Questionnaire (≥7). Candidate modifiers were identified using previous literature and evaluated using logistic regression with statistical interaction terms to provide preliminary evidence of treatment-effect modification. Socioeconomic status (SES) was identified as an effect modifier for disability outcomes (OR = 1.71, P=.028). High SES patients receiving prognostic stratified care were 2.5 times less likely to have a poor outcome compared to low SES patients receiving best current care (OR = 0.40, P=.006). Education level (OR = 1.33, P=.109) and number of pain medications (OR = 0.64, P=.140) met our criteria for effect modification with weaker evidence (0.20>P≥0.05). These findings provide preliminary evidence for SES, education, and number of pain medications as treatment-effect modifiers of prognostic stratified care delivered in the STarT Back Trial

Could the clinical interpretability of subgroups detected using clustering methods be improved by using a novel two-stage approach?

Background: Recognition of homogeneous subgroups of patients can usefully improve prediction of their outcomes and the targeting of treatment. There are a number of research approaches that have been used to recognise homogeneity in such subgroups and to test their implications. One approach is to use statistical clustering techniques, such as Cluster Analysis or Latent Class Analysis, to detect latent relationships between patient characteristics. Influential patient characteristics can come from diverse domains of health, such as pain, activity limitation, physical impairment, social role participation, psychological factors, biomarkers and imaging. However, such 'whole person' research may result in data-driven subgroups that are complex, difficult to interpret and challenging to recognise clinically. This paper describes a novel approach to applying statistical clustering techniques that may improve the clinical interpretability of derived subgroups and reduce sample size requirements. Methods: This approach involves clustering in two sequential stages. The first stage involves clustering within health domains and therefore requires creating as many clustering models as there are health domains in the available data. This first stage produces scoring patterns within each domain. The second stage involves clustering using the scoring patterns from each health domain (from the first stage) to identify subgroups across all domains. We illustrate this using chest pain data from the baseline presentation of 580 patients. Results: The new two-stage clustering resulted in two subgroups that approximated the classic textbook descriptions of musculoskeletal chest pain and atypical angina chest pain. The traditional single-stage clustering resulted in five clusters that were also clinically recognisable but displayed less distinct differences. Conclusions: In this paper, a new approach to using clustering techniques to identify clinically useful subgroups of patients is suggested. Research designs, statistical methods and outcome metrics suitable for performing that testing are also described. This approach has potential benefits but requires broad testing, in multiple patient samples, to determine its clinical value. The usefulness of the approach is likely to be context-specific, depending on the characteristics of the available data and the research question being asked of it

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions

Identification and management of chronic pain in primary care:a review

Chronic pain is a common, complex, and challenging condition, where understanding the biological, social, physical and psychological contexts is vital to successful outcomes in primary care. In managing chronic pain the focus is often on promoting rehabilitation and maximizing quality of life rather than achieving cure. Recent screening tools and brief intervention techniques can be effective in helping clinicians identify, stratify and manage both patients already living with chronic pain and those who are at risk of developing chronic pain from acute pain. Frequent assessment and reassessment are key to ensuring treatment is appropriate and safe, as well as minimizing and addressing side effects. Primary care management should be holistic and evidence-based (where possible) and incorporates both pharmacological and non-pharmacological approaches, including psychology, self-management, physiotherapy, peripheral nervous system stimulation, complementary therapies and comprehensive pain-management programmes. These may either be based wholly in primary care or supported by appropriate specialist referral

The influence of clinical equipoise and patient preferences on outcomes of conservative manual interventions for spinal pain: an experimental study

Mark D Bishop,1,2 Joel E Bialosky,1–3 Charles W Penza,1,2 Jason M Beneciuk,1,3 Meryl J Alappattu1,2 1Department of Physical Therapy, University of Florida, 2Center for Pain Research and Behavioral Health, 3Brooks-PHHP Research Collaboration, Gainesville, FL, USA Background: Expected pain relief from treatment is associated with positive clinical outcomes in patients with musculoskeletal pain. Less studied is the influence on outcomes related to the preference of patients and providers for a specific treatment. Objectives: We sought to determine how provider and patient preferences for a manual therapy intervention influenced outcomes in individuals with acutely induced low back pain (LBP). Participants and methods: Pain-free participants were randomly assigned to one of two manual therapies (joint biased [JB] or constant touch [CT]) 48 hours after completing an exercise protocol to induce LBP. Expectations for pain relief and preferences for treatment were collected at baseline, prior to randomization. Pain relief was assessed using a 100 mm visual analog scale. All study procedures were conducted in a private testing laboratory at the University of Florida campus. Results: Sixty participants were included in this study. After controlling for preintervention pain intensity, the multivariate model included only preintervention pain (B=0.12, p=0.07) and provider preference (B=3.05, p<0.0001) and explained 35.8% of the variance in postintervention pain. When determining whether a participant met his or her expected pain relief, receiving an intervention from a provider with a strong preference for that intervention increased the odds of meeting a participant’s expected pain relief 68.3 times (p=0.013) compared to receiving any intervention from a provider with no preference. Receiving JB intervention from any provider increased the odds of meeting expected relief 29.7 times (p=0.023). The effect of a participant receiving an intervention they preferred was retained in the model but did not meet the criteria for a significant contribution. Conclusion: Our primary findings were that participant and provider preferences for treatment positively influence pain outcomes in individuals with acutely induced LBP, and joint-biased interventions resulted in a greater chance of meeting participants’ expected outcomes. This is contrary to our hypothesis that the interaction of receiving an intervention for which a participant had a preference would result in the best outcome. Keywords: equipoise, expectations, manual therapy, acute pai