Predicting Persistent Disabling Low Back Pain in Veterans Affairs Primary Care Using the STarT Back Tool

Strategies for combining interventional and behavioral therapies in management of chronic low back pain: A scoping review

Background

Common non-surgical treatment approaches for chronic low back pain (CLBP) include pharmacologic, interventional and behavioral therapies, but there is no consensus treatment approach. Despite studies that show the effectiveness of interventional and behavioral approaches individually and evidence-based recommendations for multimodal treatment approach, specific stacking and sequencing strategies used to combine both approaches haven't been studied.

Objectives

The objectives of this scoping review were to: 1) explore how interventional and behavioral approaches to CLBP treatment are stacked or sequenced; 2) evaluate the feasibility of utilizing interventional and behavioral treatments in an integrative manner, and 3) assess whether optimal combinations of interventional and behavioral approaches to CLBP treatment exist.

Methods

A literature search of indexed and gray literature was conducted for studies involving the combination of interventional and behavioral therapies for treatment of CLBP. 374 abstracts and 72 records of gray literature were independently screened followed by 60 that underwent full-text review.

Results

A total of three studies were included in this review, all of which found the integration of modalities to be feasible. Two studies that utilized non-conventional interventions found no significant treatment effect by combining modalities. One study demonstrated a non-significant additive effect of combining radiofrequency ablation with cognitive behavioral therapy.

Conclusions

Despite known individual benefits, there are limited studies exploring combined interventional and behavioral approaches to CLBP. Given the feasibility and the additive effects of combining interventions with behavioral therapy seen in the studies included this review, further exploration of this subject is needed to guide clinical practice.

Description and initial validation of a novel measure of pain intensity: the Numeric Rating Scale of Underlying Pain without concurrent Analgesic use

ABSTRACT

Although many individuals with chronic pain use analgesics, the methods used in many randomized controlled trials (RCTs) do not sufficiently account for confounding by differential post-randomization analgesic use. This may lead to underestimation of average treatment effects and diminished power. We introduce (1) a new measure-the Numeric Rating Scale of Underlying Pain without concurrent Analgesic use (NRS-UP (A) )-which can shift the estimand of interest in an RCT to target effects of a treatment on pain intensity in the hypothetical situation where analgesic use was not occurring at the time of outcome assessment; and (2) a new pain construct-an individuals' perceived effect of analgesic use on pain intensity (E A ). The NRS-UP (A) may be used as a secondary outcome in RCTs of point treatments or nonpharmacologic treatments. Among 662 adults with back pain in primary care, participants' mean value of the NRS-UP (A) among those using analgesics was 1.2 NRS points higher than their value on the conventional pain intensity NRS, reflecting a mean E A value of -1.2 NRS points and a perceived beneficial effect of analgesics. More negative values of E A (ie, greater perceived benefit) were associated with a greater number of analgesics used but not with pain intensity, analgesic type, or opioid dose. The NRS-UP (A) and E A were significantly associated with future analgesic use 6 months later, but the conventional pain NRS was not. Future research is needed to determine whether the NRS-UP (A), used as a secondary outcome may allow pain RCTs to target alternative estimands with clinical relevance.

Combining Procedural and Behavioral Treatments for Chronic Low Back Pain: A Pilot Feasibility Randomized Controlled Trial

Individual treatments for chronic low back pain (CLBP) have small magnitude effects. Combining different types of treatments may produce larger effects. This study used a 2×2 factorial randomized controlled trial (RCT) design to combine procedural and behavioral treatments for CLBP. The study aims were to: (1) assess feasibility of conducting a factorial RCT of these treatments; and (2) estimate individual and combined treatment effects of (a) lumbar radiofrequency ablation (LRFA) of the dorsal ramus medial branch nerves (vs. a simulated LRFA control procedure) and (b) Activity Tracker-Informed Video-Enabled Cognitive Behavioral Therapy program for CLBP (AcTIVE-CBT) (vs. an educational control treatment) on back-related disability at 3 months post-randomization. Participants (n=13) were randomized in a 1:1:1:1 ratio. Feasibility goals included an enrollment proportion ≥30%, a randomization proportion ≥80%, and a ≥80% proportion of randomized participants completing the 3-month Roland-Morris Disability Questionnaire (RMDQ) primary outcome endpoint. An intent-to-treat analysis was used. The enrollment proportion was 62%, the randomization proportion was 81%, and all randomized participants completed the primary outcome. Though not statistically significant, there was a beneficial, moderate-magnitude effect of LRFA vs. control on 3-month RMDQ (-3.25 RMDQ points; 95% CI: -10.18, 3.67). There was a significant, beneficial, large-magnitude effect of AcTIVECBT vs. control (-6.29, 95% CI: -10.97, -1.60). Though not statistically significant, there was a beneficial, large effect of LRFA+AcTIVE-CBT vs. control (-8.37; 95% CI: -21.47, 4.74). We conclude that it is feasible to conduct an RCT combining procedural and behavioral treatments for CLBP.

Six Externally Validated Prognostic Models Have Potential Clinical Value to Predict Patient Health Outcomes in the Rehabilitation of Musculoskeletal Conditions: A Systematic Review

OBJECTIVE

The purpose of this systematic review was to identify and appraise externally validated prognostic models to predict a patient's health outcomes relevant to physical rehabilitation of musculoskeletal (MSK) conditions.

METHODS

We systematically reviewed 8 databases and reported our findings according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020. An information specialist designed a search strategy to identify externally validated prognostic models for MSK conditions. Paired reviewers independently screened the title, abstract, and full text and conducted data extraction. We extracted characteristics of included studies (eg, country and study design), prognostic models (eg, performance measures and type of model) and predicted clinical outcomes (eg, pain and disability). We assessed the risk of bias and concerns of applicability using the prediction model risk of bias assessment tool. We proposed and used a 5-step method to determine which prognostic models were clinically valuable.

RESULTS

We found 4896 citations, read 300 full-text articles, and included 46 papers (37 distinct models). Prognostic models were externally validated for the spine, upper limb, lower limb conditions, and MSK trauma, injuries, and pain. All studies presented a high risk of bias. Half of the models showed low concerns for applicability. Reporting of calibration and discrimination performance measures was often lacking. We found 6 externally validated models with adequate measures, which could be deemed clinically valuable [ie, (1) STart Back Screening Tool, (2) Wallis Occupational Rehabilitation RisK model, (3) Da Silva model, (4) PICKUP model, (5) Schellingerhout rule, and (6) Keene model]. Despite having a high risk of bias, which is mostly explained by the very conservative properties of the PROBAST tool, the 6 models remain clinically relevant.

CONCLUSION

We found 6 externally validated prognostic models developed to predict patients' health outcomes that were clinically relevant to the physical rehabilitation of MSK conditions.

IMPACT

Our results provide clinicians with externally validated prognostic models to help them better predict patients' clinical outcomes and facilitate personalized treatment plans. Incorporating clinically valuable prognostic models could inherently improve the value of care provided by physical therapists.

Noncoding rare variants in PANX3 are associated with chronic back pain

ABSTRACT

Back pain is the leading cause of years lived with disability worldwide, yet surprisingly, little is known regarding the biology underlying this condition. The impact of genetics is known for chronic back pain: its heritability is estimated to be at least 40%. Large genome-wide association studies have shown that common variation may account for up to 35% of chronic back pain heritability; rare variants may explain a portion of the heritability not explained by common variants. In this study, we performed the first gene-based association analysis of chronic back pain using UK Biobank imputed data including rare variants with moderate imputation quality. We discovered 2 genes, SOX5 and PANX3 , influencing chronic back pain. The SOX5 gene is a well-known back pain gene. The PANX3 gene has not previously been described as having a role in chronic back pain. We showed that the association of PANX3 with chronic back pain is driven by rare noncoding intronic polymorphisms. This result was replicated in an independent sample from UK Biobank and validated using a similar phenotype, dorsalgia, from FinnGen Biobank. We also found that the PANX3 gene is associated with intervertebral disk disorders. We can speculate that a possible mechanism of action of PANX3 on back pain is due to its effect on the intervertebral disks.

Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use

The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between "observed" pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs. PERSPECTIVE: This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.

Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data

BACKGROUND

Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient's health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS.

MATERIALS AND METHOD

We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months after a 2 month gap), respectively. We developed a DL model to use patients' demographics, diagnosis and procedure codes, drug names, and diagnostic imaging reports to predict surgery. For each prediction task, we evaluated the model's performance using classical and generalizability evaluation. For classical evaluation, we split the data into training (80%) and testing (20%). For generalizability evaluation, we split the data based on the healthcare system. We used the area under the curve (AUC) to assess performance for each evaluation. We compared results to a benchmark model (i.e. LASSO logistic regression).

RESULTS

For classical performance, the DL model outperformed the benchmark model for early surgery with an AUC of 0.725 compared to 0.597. For late surgery, the DL model outperformed the benchmark model with an AUC of 0.655 compared to 0.635. For generalizability performance, the DL model outperformed the benchmark model for early surgery. For late surgery, the benchmark model outperformed the DL model.

CONCLUSIONS

For early surgery, the DL model was preferred for classical and generalizability evaluation. However, for late surgery, the benchmark and DL model had comparable performance. Depending on the prediction task, the balance of performance may shift between DL and a conventional ML method. As a result, thorough assessment is needed to quantify the value of DL, a relatively computationally expensive, time-consuming and less interpretable method.

Best Practices for Chiropractic Management of Adult Patients With Mechanical Low Back Pain: A Clinical Practice Guideline for Chiropractors in the United States

OBJECTIVE

The purpose of this paper was to update the previously published 2016 best-practice recommendations for chiropractic management of adults with mechanical low back pain (LBP) in the United States.

METHODS

Two experienced health librarians conducted the literature searches for clinical practice guidelines and other relevant literature, and the investigators performed quality assessment of included studies. PubMed was searched from March 2015 to September 2021. A steering committee of 10 experts in chiropractic research, education, and practice used the most current relevant guidelines and publications to update care recommendations. A panel of 69 experts used a modified Delphi process to rate the recommendations.

RESULTS

The literature search yielded 14 clinical practice guidelines, 10 systematic reviews, and 5 randomized controlled trials (all high quality). Sixty-nine members of the panel rated 38 recommendations. All but 1 statement achieved consensus in the first round, and the final statement reached consensus in the second round. Recommendations covered the clinical encounter from history, physical examination, and diagnostic considerations through informed consent, co-management, and treatment considerations for patients with mechanical LBP.

CONCLUSION

This paper updates a previously published best-practice document for chiropractic management of adults with mechanical LBP.

The Flares of Low back pain with Activity Research Study (FLAReS): study protocol for a case-crossover study nested within a cohort study

BACKGROUND

Although it is generally accepted that physical activity and flares of low back pain (LBP) are related, evidence for the directionality of this association is mixed. The Flares of Low back pain with Activity Research Study (FLAReS) takes a novel approach to distinguish the short-term effects of specific physical activities on LBP flares from the cumulative effects of such activities, by conducting a longitudinal case-crossover study nested within a cohort study. The first aim is to estimate the short-term effects (≤ 24 h) of specific physical activities on LBP flares among Veterans in primary care in the Veterans Affairs healthcare system. The second aim is to estimate the cumulative effects of specific activities on LBP-related functional limitations at 1-year follow-up.

METHODS

Up to 550 adults of working age (18-65 years) seen for LBP in primary care complete up to 36 "Scheduled" surveys over 1-year follow-up, and also complete unscheduled "Flare Window" surveys after the onset of new flares. Each survey asks about current flares and other factors associated with LBP. Surveys also inquire about activity exposures over the 24 h, and 2 h, prior to the time of survey completion (during non-flare periods) or prior to the time of flare onset (during flares). Other questions evaluate the number, intensity, duration, and/or other characteristics of activity exposures. Other exposures include factors related to mood, lifestyle, exercise, concurrent treatments, and injuries. Some participants wear actigraphy devices for weeks 1-4 of the study. The first aim will examine associations between 10 specific activity categories and participant-reported flares over 1-year follow-up. The second aim will examine associations between the frequency of exposure to 10 activity categories over weeks 1-4 of follow-up and long-term functional limitations at 12 months. All analyses will use a biopsychosocial framework accounting for potential confounders and effect modifiers.

DISCUSSION

FLAReS will provide empirically derived estimates of both the short-term and cumulative effects of specific physical activities for Veterans with LBP, helping to better understand the role of physical activities in those with LBP.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04828330 , registered April 2, 2021.

Genome-wide association studies of low back pain and lumbar spinal disorders using electronic health record data identify a locus associated with lumbar spinal stenosis

ABSTRACT

Identifying genetic risk factors for lumbar spine disorders may lead to knowledge regarding underlying mechanisms and the development of new treatments. We conducted a genome-wide association study involving 100,811 participants with genotypes and longitudinal electronic health record data from the Electronic Medical Records and Genomics Network and Geisinger Health. Cases and controls were defined using validated algorithms and clinical diagnostic codes. Electronic health record-defined phenotypes included low back pain requiring healthcare utilization (LBP-HC), lumbosacral radicular syndrome (LSRS), and lumbar spinal stenosis (LSS). Genome-wide association study used logistic regression with additive genetic effects adjusting for age, sex, site-specific factors, and ancestry (principal components). A fixed-effect inverse-variance weighted meta-analysis was conducted. Genetic variants of genome-wide significance (P < 5 × 10-8) were carried forward for replication in an independent sample from UK Biobank. Phenotype prevalence was 48.8% for LBP-HC, 19.8% for LSRS, and 7.9% for LSS. No variants were significantly associated with LBP-HC. One locus was associated with LSRS (lead variant rs146153280:C>G, odds ratio [OR] = 1.17 for G, P = 2.1 × 10-9), but was not replicated. Another locus on chromosome 2 spanning GFPT1, NFU1, and AAK1 was associated with LSS (lead variant rs13427243:G>A, OR = 1.10 for A, P = 4.3 × 10-8) and replicated in UK Biobank (OR = 1.11, P = 5.4 × 10-5). This was the first genome-wide association study meta-analysis of lumbar spinal disorders using electronic health record data. We identified 2 novel associations with LSRS and LSS; the latter was replicated in an independent sample.