Normality Analysis of Numeric Rating Scale Scores in Patients with Chronic Axial Spine Pain Before and After Medial Branch Blocks: a Multicenter Study.

OBJECTIVE: The statistical analysis typically employed to compare pain both before and after interventions assumes scores are normally distributed. The present study evaluates whether Numeric Rating Scale (NRS), specifically the NRS-11, scores are indeed normally distributed in a clinically-relevant cohort of adults with chronic axial spine pain pre- and post-analgesic intervention. METHODS: Retrospective review from four academic medical centers of prospectively collected data from a uniform pain diary administered to consecutive patients after undergoing medial branch blocks. The pain diary assessed NRS-11 scores immediately pre-injection and at 12 different time points post-injection up to 48 hours. D'Agostino-Pearson tests were used to test normality at all time points. RESULTS: One hundred fifty pain diaries were reviewed and despite normally distributed pre-injection NRS-11 scores (K2 = 0.655, p = 0.72), all post-injection NRS-11 data was not normally distributed (K2 = 9.70- 17.62, p = 0.0001-0.008). CONCLUSIONS: Although the results of parametric analyses of NRS-11 scores are commonly reported in pain research, some properties of NRS-11 do not satisfy the assumptions required for these analyses. The data demonstrate non-normal distributions in post-intervention NRS-11 scores, thereby violating a key requisite for parametric analysis. We urge pain researchers to consider appropriate statistical analysis and reporting for non-normally distributed NRS-11 scores to ensure accurate interpretation and communication of these data. Practicing pain physicians should similarly recognize that parametric post-intervention pain score statistics may not accurately describe the data and should expect manuscripts to utilize measures of normality to justify the selected statistical methods.

[Evidence-based interventions to treat chronic low back pain: treatment selection for a personalized medicine approach : German version]. / Evidenzbasierte Interventionen zur Behandlung von chronischem Schmerz im unteren Rücken ­ Therapieauswahl für einen personalisierten Behandlungsansatz : Deutsche Fassung.

INTRODUCTION: Chronic low back pain (cLBP) is highly prevalent in the United States and globally, resulting in functional impairment and lowered quality of life. While many treatments are available for cLBP, clinicians have little information about which specific treatment(s) will work best for individual patients or subgroups of patients. The Back Pain Research Consortium, part of the National Institutes of Health Helping to End Addiction Long-termSM (HEAL) Initiative, will conduct a collaborative clinical trial, which seeks to develop a personalized medicine algorithm to optimize patient and provider treatment selection for patients with cLBP. OBJECTIVE: The primary objective of this article is to provide an update on evidence-based cLBP interventions and describe the process of reviewing and selecting interventions for inclusion in the clinical trial. METHODS: A working group of cLBP experts reviewed and selected interventions for inclusion in the clinical trial. The primary evaluation measures were strength of evidence and magnitude of treatment effect. When available in the literature, duration of effect, onset time, carryover effect, multimodal efficacy, responder subgroups, and evidence for the mechanism of treatment effect or biomarkers were considered. CONCLUSION: The working group selected 4 leading, evidence-based treatments for cLBP to be tested in the clinical trial and for use in routine clinical treatment. These treatments include (1) duloxetine, (2) acceptance and commitment therapy, (3) a classification-based exercise and manual therapy intervention, and (4) a self-management approach. These interventions each had a moderate to high level of evidence to support a therapeutic effect and were from different therapeutic classes.

Predictors of response in PROMIS-global in a chronic low back pain specialty clinic: STarTBack and chronic overlapping pain conditions.

BACKGROUND: Tools, such as the STarTBack Screening Tool (SBT), have been developed to identify risks of progressing to chronic disability in low back pain (LBP) patients in the primary care population. However, less is known about predictors of change in function after treatment in the specialty care population. OBJECTIVE: We pursued a retrospective observational cohort study involving LBP patients seen in a multidisciplinary specialty clinic to assess which features can predict change in function at follow-up. METHODS: The SBT was administered at initial visit, and a variety of patient characteristics were available in the chart including the presence of chronic overlapping pain conditions (COPCs). Patient Reported Outcomes Measurement Information System-10 (PROMIS-10) global physical health (PH) and global mental health (MH) were measured at baseline and at pragmatic time points during follow-up. Linear regression was used to estimate adjusted associations between available features and changes in PROMIS scores. RESULTS: 241 patients were followed for a mean of 17.0 ± 7.5 months. Mean baseline pain was 6.7 (SD 2.1), PROMIS-10 global MH score was 44.8 (SD 9.3), and PH score was 39.4 (SD 8.6). 29.7% were low-risk on the SBT, 41.8% were medium-risk, and 28.5% were high-risk. Mean change in MH and PH scores from baseline to the follow-up questionnaire were 0.86 (SD 8.11) and 2.39 (SD 7.52), respectively. Compared to low-risk patients, high-risk patients had a mean 4.35 points greater improvement in their MH score (p= 0.004) and a mean 3.54 points greater improvement in PH score (p= 0.006). Fewer COPCs also predicted greater improvement in MH and PH. CONCLUSIONS: SBT and the presence of COPC, which can be assessed at initial presentation to a specialty clinic, can predict change in PROMIS following treatment. Effort is needed to identify other factors that can help predict change in function after treatment in the specialty care setting.

Design of the COMEBACK and BACKHOME Studies, Longitudinal Cohorts for Comprehensive Deep Phenotyping of Adults with Chronic Low-Back Pain (cLBP): a part of the BACPAC Research Program.

Objective: The University of California, San Francisco (UCSF) Core Center for Patient-centric, Mechanistic Phenotyping in Chronic Low Back Pain (REACH) is one of the three NIH Back Pain Consortium (BACPAC) Research Programs Mechanistic Research Centers (MRCs). The goal of UCSF REACH is to define cLBP phenotypes and pain mechanisms that can lead to effective, personalized treatments for patients across the population. The primary objective of this research project is to address the critical need for new diagnostic and prognostic markers, and associated patient classification protocols for chronic low back pain (cLBP) treatment. Design: To meet this objective, REACH is conducting two large investigator-initiated translational research cohort studies called: The Longitudinal Clinical Cohort for Comprehensive Deep Phenotyping of Chronic Low-Back Pain (cLBP) Adults Study (comeBACK) and the Chronic Low-Back Pain (cLBP) in Adults Study (BACKHOME). Setting: comeBACK is a longitudinal multicenter in-person observational study of 450 adults with chronic low back pain designed to perform comprehensive deep phenotyping. While, the BACKHOME study is a site-less longitudinal observational e-cohort of approximately 3000 U.S. adults with cLBP. To our knowledge, BACKHOME is the largest prospective remote registry of nationwide adults with cLBP. Methods: Both the comeBACK and BACKHOME studies are collecting a robust and comprehensive set of risk factors, outcomes, and covariates in order to perform deep phenotyping of cLBP patients based on combined biopsychosocial variables to: define cLBP subtypes, establish phenotyping tools for routine clinical evaluation, and lead to improved cLBP outcomes in the future. The data from both studies will be used to establish techniques to develop a patient-centric definition of treatment success and to analyze cLBP patient traits to define clinically useful cLBP phenotypes, using a combination of traditional data analyses and deep learning methods. Conclusions: These 2 pivotal studies, in conjunction with the ancillary studies being performed in both comeBACK and BACKHOME, and the other BACPAC-consortium research projects, we will be able to address a number of diagnostic and therapeutic issues in this complex and diverse patient population with cLBP. These studies will help clarify biopsychosocial mechanisms of cLBP with the aim to provide a foundation to improve the evaluation of treatment effectiveness and to spur new avenues of therapeutic research, including personalized outcome measures that constitute a clinically meaningful treatment effect for individual cLBP patients.