Chronic low back pain clinical outcomes present higher associations with the STarT Back Screening Tool than with physiologic measures: a 12-month cohort study

The association of the STarT Back Screening Tool and type of leg pain with low back pain disability trajectories: a prospective cohort study

BACKGROUND

Multiple factors influence the recovery process of low back pain (LBP). The identification and increased knowledge of prognostic factors might contribute to a better understanding of the course of LBP. The purpose of this study is to investigate the association of the STarT Back Screening Tool (SBST) risk score and the type of leg pain (non-radiating LBP, referred non-radicular, and radicular radiating leg pain) with the disability trajectory (at baseline, the slope, and recovery at one year) in adults with low back pain.

METHODS

This is a prospective cohort study in 347 patients with low back pain who sought physiotherapy care at three primary care practices in the Netherlands. Linear mixed models were estimated to describe the association of the SBST risk score and the type of leg pain with disability at baseline, the slope in the disability trajectory, and at twelve months follow-up.

RESULTS

A medium/high risk score on the SBST is associated with higher baseline disability scores on the Oswestry Disability Index (ODI), faster initial recovery, and still a higher disability ODI score at 12 months follow-up. Non-radicular referred and radicular radiating leg pain were associated with worse baseline disability ODI scores in LBP. This association was not present for the initial recovery or at the 12 months follow-up.

CONCLUSION

The SBST is associated with the LBP recovery trajectory. The SBST might be a useful tool to predict the disability trajectory in a heterogeneous group of people with low back pain in primary care and might, therefore, be recommended in future clinical practice guidelines. The type of leg pain was not associated with the recovery trajectory of LBP. Future research might focus on evaluating different types of leg pain.

TRIAL REGISTRATION

Clinicaltrials.gov: 109,643.

How Well Do Seven Self-Report Measures Represent Underlying Back Pain Impact?

BACKGROUND

The extent to which different measures of back pain impact represent an underlying common factor has implications for decisions about which one to use in studies of pain management and estimating one score from others.

AIMS

To determine if different self-report back pain impact measures represent an underlying pain latent variable and estimate associations with it.

METHOD

Seven pain impact measures completed by Amazon Mechanical Turk adults are used to estimate internal consistency reliability and associations: Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), short form of the Örebro Musculoskeletal Pain Questionnaire (OMPQ), Subgroups for Targeted Treatment (STarT) Back Tool, the Graded Chronic Pain Scale (GCPS) disability score, PEG (Pain intensity, interference with Enjoyment of life, interference with General activity), and Impact Stratification Score (ISS).

RESULTS

The sample of 1,874 adults with back pain had an average age of 41 and 52% were female. Sixteen percent were Hispanic, 7% non-Hispanic Black, 5% non-Hispanic Asian, and 71% non-Hispanic White. Internal consistency reliability estimates ranged from 0.710 (OMPQ) to 0.923 (GCPS). Correlations among the measures ranged from 0.609 (RMDQ with OMPQ) to 0.812 (PEG with GCPS). Standardized factor loadings on the pain latent variable ranged from 0.782 (RMDQ) to 0.870 (ISS).

CONCLUSIONS

Scores of each measure can be estimated from the others for use in research.

Assessment of exercise-induced hypoalgesia in chronic low back pain and potential associations with psychological factors and central sensitization symptoms: A case-control study

INTRODUCTION

Exercise is the most recommended treatment for chronic low back pain (CLBP) and is effective in reducing pain, but the mechanisms underlying its effects remain poorly understood. Exercise-induced hypoalgesia (EIH) may play a role and is thought to be driven by central pain modulation mechanisms. However, EIH appears to be disrupted in many chronic pain conditions and its presence in people with CLBP remains unclear. As people suffering from chronic pain often exhibit psychological factors and central sensitization symptoms influencing pain perception, EIH might be associated with these factors.

OBJECTIVE

The aim of this study is to compare the level of EIH between participants with and without CLBP following back and wrist exercises and to assess the associations between EIH, psychological factors, and symptoms of central sensitization (using the central sensitization inventory - CSI) in CLBP.

METHOD

Twenty-eight participants with CLBP and 23 without pain were recruited. Pressure pain thresholds (PPT) were measured at 4 sites (2 bony sites = capitate, S1|2 muscle sites = wrist flexors, lumbar erector spinae) before and after each of two exercises (wrist flexion and lumbar extension). Exercise-induced hypoalgesia was defined as percent change in PPT from pre- to post-exercise. Participants with CLBP also completed questionnaires to measure psychological factors (e.g., kinesiophobia, catastrophizing, anxiety, and self-efficacy) and symptoms of central sensitization (CSI), and correlations with EIH were calculated.

RESULTS

After wrist exercise, EIH measured at the muscle sites was lower in the CLBP group compared with the pain-free group (p = 0.047) but no differences were found at bony sites (p = 0.49). No significant differences for EIH were observed following back exercise at muscle sites (p = 0.14) or at bony sites (p = 0.65). Exercise-induced hypoalgesia was not correlated with any psychological factors or with the CSI score.

CONCLUSION

The lower EIH following wrist exercises may represent an alteration in pain modulation control in CLBP. However, psychological factors and central sensitization symptoms may not explain the differences observed.

The Relationship Between Pain-Related Psychological Factors and Maximal Physical Performance in Low Back Pain: A Systematic Review and Meta-Analysis

Theoretical frameworks explain how pain-related psychological factors may influence the physical performance. In this systematic review and meta-analysis, we evaluated the evidence regarding the relationship between the pain-related psychological factors and the maximal physical performance in patients with low back pain (LBP). Pubmed, Embase, CINAHL and Web of Science databases were searched from inception to May 2022. Cross-sectional or longitudinal studies reporting cross-sectional measures of association between at least one pain-related psychological factor and a quantitatively measured outcome of maximal physical performance in patients with LBP were eligible for inclusion. Thirty-eight studies (n = 2,490; 27 cross-sectional studies, n = 1,647 (66%); 11 longitudinal studies, n = 843 (34%)) were included, with 92% of participants (n = 2,284) having chronic LBP. Results showed that pain-related fear, pain catastrophizing, and anticipated pain were consistently and negatively associated with the maximal physical performance in chronic LBP, whereas pain-self efficacy showed positive correlations. Overall, magnitudes of absolute pooled r-values were small (r ≤ 0.25), except for anticipated pain, which was moderately associated with maximal physical performance (r = -0.34 to -0.37). Subanalyses and sensitivity analyses yielded similar pooled correlation coefficients. Certainty of evidence using the GRADE recommendations was very low to moderate for pain-related fear, and very low to low for the other pain-related psychological factors. Prospero registration: CRD42021227486. PERSPECTIVE: Overall, small pooled correlation coefficients were shown between pain-related psychological factors and maximal physical performance in chronic LBP. Certainty of evidence was very low to low for all pain-related psychological factors other than pain-related fear. Future studies taking into account limitations of the current literature may therefore change these conclusions.

The relationship between the Keele STarT back screening tool, the short form of central sensitivity inventory and health-related quality of life in patients with low back pain

INTRODUCTION

It has been reported that the scores of the Keele STarT Back Screening Tool (SBST) and the short form of Central Sensitization Inventory (CSI-9) are associated with Health-Related Quality of Life (HRQoL) in patients with low back pain (LBP). However, it is unclear which screening tool is more associated with HRQoL in patients with LBP.

OBJECTIVE

To identify which SBST and CSI-9 are more related to HRQoL and investigate the association between SBST and CSI-9 scores.

STUDY DESIGN

Cross-sectional study.

METHOD

A multiple regression analysis was conducted to examine the factors associated with the HRQoL using age, pain intensity, disability, SBST score, and CSI-9 score as independent variables. Spearman's rank correlation coefficient was also conducted to determine the association between SBST and CSI-9 score.

RESULTS

Multiple regression analysis revealed that the Oswestry Disability Index (ODI) score (p < 0.01, β = -0.62, VIF = 1.70) and SBST score (p < 0.01, β = -0.32, VIF = 1.86) were significant associated variables. A significant association was found between SBST and CSI-9 scores (p < 0.01, ρ = 0.47).

CONCLUSION

The results showed that in patients with LBP the association of HRQoL with SBST was more substantial than with CSI-9.

A Scoping Review of Chronic Low Back Pain Classification Schemes Based on Patient-Reported Outcomes

BACKGROUND

In 2014, the National Institutes of Health Pain Consortium Research Task Force recommended that patients with chronic low back pain (CLBP) be stratified by its impact on their lives. They proposed the Impact Stratification Score (ISS) to help guide therapy and facilitate study comparability. The ISS has been evaluated as a continuous measure, but not for use as a stratification or classification scheme.

OBJECTIVES

Identify the characteristics of successful schemes to inform the use of the ISS for stratification or classification.

STUDY DESIGN

Scoping review of the peer-reviewed literature.

METHODS

Search of PubMed, CINAHL, and APA PsycInfo to identify patient self-report-based classification schemes applicable to CLBP. Data were captured on the methods used for each scheme's development, the domains covered, their scoring criteria and what the classification has successfully measured. The study was reviewed and approved by the RAND Human Subjects Protection Committee (2019-0651-AM02).

RESULTS

The search identified 87 published articles about the development and testing of 5 classification schemes: 1) The Subgroups for Targeted Treatment (STarT) Back Screening Tool, 2) Multiaxial Assessment of Pain, 3) Graded Chronic Pain Scale, 4) Back Pain Classification Scale, and 5) Chronic Pain Risk Score. All have been shown to be predictive of future outcomes and the STarT Back has been found useful in identifying effective classification-specific treatment. Each scheme had a different classification scoring structure, was developed using different methods, and 3 included domains not found in the ISS.

LIMITATIONS

Expanding the search to other databases may have identified more classification schemes. Our minimum number of publications inclusion criterion eliminated dozens of cluster analyses, some of which may have eventually been replicated.

CONCLUSIONS

The methods used to develop these successful classification schemes, especially those that use straightforward scoring schemes, should be considered for use in the development of a scheme based on the ISS.

Improving the Predictive Validity of the Dutch STarT Back Tool

OBJECTIVE

The purpose of this study was to evaluate whether the predictive validity of the Dutch version of the STarT Back Tool (SBT) can be improved by (1) using other cut-off values, (2) changing the items, or (3) adding prognostic factors to the SBT.

DESIGN

This was a secondary analysis of a prospective cohort study (PRINS study: Prevalence of Risk groups in Neck- and back pain patients according to the STarT back screening tool) in patients with low back or neck pain.

METHODS

The predictive validity was calculated with a relative risk ratio and a Spearman correlation. The new cut-off values were calculated with receiver operating characteristic curves. Replacing items of the SBT and adding new items were assessed with logistic regression analyses.

RESULTS

A total of 150 patients were included; 51% were categorized as having low risk, 39% as moderate risk, and 11% as high risk. Changing the cut-off total score to ≤2 and the subscore to ≥5 led to an improvement of the Spearman correlation and RR. Adding the item "duration of the complaints" improved the RR for moderate risk (3.6) (95% CI = 1.6-7.9) and for high risk (9.0) (95% CI = 4.2-19.1) compared with low risk. The new Spearman correlation was improved to rs = 0.37.

CONCLUSION

The predictive validity was improved by adding the item "duration of the complaints" and changing the cut-off values.

Distinguishing chronic low back pain in young adults with mild to moderate pain and disability using trunk compliance

Chronic low back pain (cLBP) rates among younger individuals are rising. Although pain and disability are often less severe, underlying changes in trunk behavior may be responsible for recurrence. We examine the biomarker capacity of a simple Trunk Compliance Index (TCI) to distinguish individuals with and without cLBP. A random subset (n = 49) of the RELIEF RCT were matched to healthy controls for sex, age, height and weight. We measured TCI (as displacement/ weight-normalized perturbation force) using anthropometrically-matched, suddenly-applied pulling perturbations to the trunk segment, randomized across three planes of motion (antero-posterior, medio-lateral, and rotational). Mean differences between cLBP, sex and perturbation direction were assessed with repeated-measures analysis of variance. Discriminatory accuracy of TCI was assessed using Receiver Operator Characteristic (ROC) analysis. Baseline characteristics between groups were equivalent (x̅ [range]): sex (57% female / group), age (23.0 [18-45], 22.8 [18-45]), height, cm (173.0 [156.5-205], 171.3 [121.2-197], weight, kg (71.8 [44.5-116.6], 71.7 [46.8-117.5]) with cLBP associated with significantly lower TCI for 5 of 6 directions (range mean difference, - 5.35: - 1.49, range 95% CI [- 6.46: - 2.18 to - 4.35: - 0.30]. Classification via ROC showed that composite TCI had high discriminatory potential (area under curve [95% CI], 0.90 [0.84-0.96]), driven by TCI from antero-posterior perturbations (area under curve [95% CI], 0.99 [0.97-1.00]). Consistent reductions in TCI suggests global changes in trunk mechanics that may go undetected in classic clinical examination. Evaluation of TCI in younger adults with mild pain and disability may serve as a biomarker for chronicity, leading to improved preventative measures in cLBP.Trial Registration and Funding RELIEF is registered with clinicaltrials.gov (NCT01854892) and funded by the NIH National Center for Complementary & Integrative Health (R01AT006978).

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

Nicotine dependence and the International Association for the Study of Pain neuropathic pain grade in patients with chronic low back pain and radicular pain: is there an association?

Background

This study investigated whether current smoking and a higher nicotine dependency were associated with chronic low back pain (LBP), lumbar related leg pain (sciatica) and/or radicular neuropathic pain.

Methods

A cross-sectional study was conducted on 150 patients (mean age, 60.1 ± 13.1 yr). Demographic data, the International Association for the Study of Pain (IASP) neuropathic pain grade, STarT Back tool, and the Fagerström test were completed. A control group (n = 50) was recruited.

Results

There was a significant difference between current smokers and non-smokers in the chronic LBP group in the mean pain score (P = 0.025), total STarT Back score (P = 0.015), worst pain location (P = 0.020), most distal pain radiation (P = 0.042), and in the IASP neuropathic pain grade (P = 0.026). There was a significant difference in the mean Fagerström score between the four IASP neuropathic pain grades (P = 0.005). Current smoking yielded an odds ratio (OR) of 3.071 (P = 0.011) for developing chronic LBP and sciatica, and an OR of 4.028 (P = 0.002) for obtaining an IASP “definite/probable” neuropathic pain grade, for both cohorts. The likelihood for chronic LBP and sciatica increased by 40.9% (P = 0.007), while the likelihood for an IASP neuropathic grade of “definite/probable” increased by 50.8% (P = 0.002), for both cohorts, for every one unit increase in the Fagerström score.

Conclusions

A current smoking status and higher nicotine dependence increase the odds for chronic LBP, sciatica and radicular neuropathic pain.

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test-retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions

Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.

The StarT back screening tool and a pain mannequin improve triage in individuals with low back pain at risk of a worse prognosis - a population based cohort study

Background

The STarT Back Screening Tool (SBT) identifies patients with low back pain (LBP) at risk of a worse prognosis of persistent disabling back pain, and thereby facilitates triage to appropriate treatment level. However, the SBT does not consider the pain distribution, which is a known predictor of chronic widespread pain (CWP). The aim of this study was to determine if screening by the SBT and screening of multisite chronic widespread pain (MS-CWP) could identity individuals with a worse prognosis. A secondary aim was to analyze self-reported health in individuals with and without LBP, in relation to the combination of these two screening tools.

Methods

One hundred and nineteen individuals (aged 40–71 years, mean (SD) 59 (8) years), 52 with LBP and 67 references, answered two screening tools; the SBT and a pain mannequin – as well as a questionnaire addressing self-reported health. The SBT stratifies into low, medium or high risk of a worse prognosis. The pain mannequin stratifies into either presence or absence of CWP in combination with ≥7 painful areas of pain (0–18), here defined as MS-CWP (high risk of worse prognosis). The two screening tools were studied one-by-one, and as a combined screening. For statistical analyses, independent t-tests and Chi-square tests were used.

Results

Both the SBT and the pain mannequin identified risk of a worse prognosis in individuals with (p = 0.007) or without (p = 0.001) LBP. We found that the screening tools identified partly different individuals at risk. The SBT identified one individual, while the pain mannequin identified 21 (19%). When combining the two screening methods, 21 individuals (17%) were at high risk of a worse prognosis. When analyzing differences between individuals at high risk (combined SBT and MS-CWP) with those at low risk, individuals at high risk reported worse health (p = 0.013 - < 0.001).

Conclusions

Both screening tools identified individuals at risk, but they captured different aspects, and also different number of individuals at high risk of a worse prognosis. Thus, using a combination may improve early detection and facilitate triage to appropriate treatment level with multimodal approach also in those otherwise missed by the SBT.

Feasibility and long-term efficacy of a proactive health program in the treatment of chronic back pain: a randomized controlled trial

BACKGROUND

To facilitate access to evidence-based care for back pain, a German private medical insurance offered a health program proactively to their members. Feasibility and long-term efficacy of this approach were evaluated.

METHODS

Using Zelen's design, adult members of the health insurance with chronic back pain according to billing data were randomized to the intervention (IG) or the control group (CG). Participants allocated to the IG were invited to participate in the comprehensive health program comprising medical exercise therapy and life style coaching, and those allocated to the CG to a longitudinal back pain survey. Primary outcomes were back pain severity (Korff's Chronic Pain Grade Questionnaire) as well as health-related quality of life (SF-12) assessed by identical online questionnaires at baseline and 2-year follow-up in both study arms. In addition to analyses of covariance, a subgroup analysis explored the heterogeneity of treatment effects among different risks of back pain chronification (STarT Back Tool).

RESULTS

Out of 3462 persons selected, randomized and thereafter contacted, 552 agreed to participate. At the 24-month follow-up, data on 189 of 258 (73.3%) of the IG were available, in the CG on 255 of 294 (86.7%). Significant, small beneficial effects were seen in primary outcomes: Compared to the CG, the IG reported less disability (1.6 vs 2.0; p = 0.025; d = 0.24) and scored better at the SF-12 physical health scale (43.3 vs 41.0; p < 0.007; d = 0.26). No effect was seen in back pain intensity and in the SF-12 mental health scale. Persons with medium or high risk of back pain chronification at baseline responded better to the health program in all primary outcomes than the subgroup with low risk at baseline.

CONCLUSIONS

After 2 years, the proactive health program resulted in small positive long-term improvements. Using risk screening prior to inclusion in the health program might increase the percentage of participants deriving benefits from it.

TRIAL REGISTRATION

The trial was registered at the German Clinical Trials Register under DRKS00015463 retrospectively (dated 4 Sept 2018).

Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy

INTRODUCTION

Artificial intelligence (AI) is a field of mathematical engineering which has potential to enhance healthcare through new care delivery strategies, informed decision making and facilitation of patient engagement. Machine learning (ML) is a form of narrow artificial intelligence which can be used to automate decision making and make predictions based upon patient data.

PURPOSE

This review outlines key applications of supervised and unsupervised machine learning in musculoskeletal medicine; such as diagnostic imaging, patient measurement data, and clinical decision support. The current literature base is examined to identify areas where ML performs equal to or more accurately than human levels.

IMPLICATIONS

Potential is apparent for intelligent machines to enhance various areas of physiotherapy practice through automization of tasks which involve data analysis, classification and prediction. Changes to service provision through applications of ML, should encourage physiotherapists to increase their awareness of and experiences with emerging technologies. Data literacy should be a component of professional development plans to assist physiotherapists in the application of ML and the preparation of information technology systems to use these techniques.

STarT Back Tool risk stratification is associated with changes in movement profile and sensory discrimination in low back pain: A study of 290 patients

BACKGROUND

Investigation of movement and sensory profiles across STarT Back risk subgroups.

METHODS

A chronic low back pain cohort (n = 290) were classified as low, medium or high risk using the STarT Back Tool, and completed a repeated spinal bending task and quantitative sensory testing. Pain summation, time taken and the number of protective behaviours with repeated bending were measured. Sensory tests included two-point discrimination, temporal summation, pressure/thermal pain thresholds and conditioned pain modulation. Subgroups were profiled against movement and sensory variables.

RESULTS

The high-risk subgroup demonstrated greater pain summation following repeated forward bending (p < 0.001). The medium-risk subgroup demonstrated greater pain summation following repeated backward bending (p = 0.032). Medium- and high-risk subgroups demonstrated greater forward/backward bend time compared to the low-risk subgroup (p = 0.001, p = 0.005, respectively). Medium- and high-risk subgroups demonstrated a higher number of protective behaviours per forward bend compared to the low-risk subgroup (p = 0.008). For sensory variables, only two-point discrimination differed between subgroups, with medium- and high-risk subgroups demonstrating higher thresholds (p = 0.016).

CONCLUSIONS

This study showed altered movement characteristics and sensory discrimination across SBT risk subgroups in people with CLBP. Membership of the high SBT risk subgroup was associated with greater pain and disability levels, greater pain summation following repeated bending, slower bending times, a greater number of protective behaviours during forward bending, and a higher TPD threshold. Treatment outcomes for higher risk SBT subgroups may be enhanced by interventions specifically targeting movement and sensory alterations.

SIGNIFICANCE

In 290 people with chronic low back pain movement profile and two-point discrimination threshold differed across risk subgroups defined by the STarT Back Tool. Conversely, pain sensitivity did not differ across these subgroups. These findings may add further guidance for targeted care in these subgroups.

Acupoint herbal plaster for patients with primary dysmenorrhea: study protocol for a randomized controlled trial

BACKGROUND

Primary dysmenorrhea (PD), is one of main gynecological complaints in women of child-bearing age. Common medications for PD do not always achieve satisfactory outcome of pain relief. Hence, both health professionals and patients are seeking help from complementary and alternative medicine. The acupoint herbal plaster (AHP), which appears to be a safe and effective way to alleviate menstrual pain, as well as to improve other PD-related symptoms. Despite similar clinical studies for this condition in the past, no high-quality methodology-based clinical trial has been reported to date. The current study aims to assess the efficacy of the AHP compared with the acupoint placebo plaster (APP) and being placed on a waiting-list control group in patients with primary dysmenorrhea.

METHODS/DESIGN

This study is a randomized, single-center, placebo-controlled clinical trial. A total of 180 women with PD will be included and randomly allocated to the AHP, APP and waiting-list (WL) groups in a 1:1:1 ratio. Patients in the AHP group will be provided with herbal plasters (Shaofuzhuyu decoction) on various acupoints: Shenque (CV8), Guanyuan (CV4), Qihai (CV5), Ciliao (BL32) and Zigong (EX-CA1). Women in the APP group will receive placebo plasters on the same acupoints, and no intervention will be given to the WL group until completion of the study. The primary outcome will be pain intensity reduction measured by a Visual Analog Scale (VAS), with other outcome measurements including the Cox Menstrual Symptom Scale (CMSS), the 12-Item Short Form Health Survey (SF-12) and the Participant Global Impression of Change (PGIC). All assessments will be performed at baseline, each menstrual cycle during the treatment course and the follow-up course. Any adverse events will be recorded throughout the study.

DISCUSSION

This is the first study to compare the changes in menstrual pain after three different interventions: the active intervention (AHP), the placebo intervention (APP), and a period of no intervention (WL). This three-arm randomized controlled trial (RCT) aims to investigate the relative contributions of the specific (AHP vs. APP) and non-specific (APP vs. WL) effects to the overall clinical effects of the active AHP on women with PDM. The scientific and rigorous methodology design of this trial should gather good evidence to assess the curative effects and safety of the AHP on PD. Moreover, the results of this study may provide evidence-based references for the treatment of menstrual pain in future.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ID: ChiCTR-TRC-16008701. Registered on 22 July 2016.

[Psychosocial risk factors for chronic back pain in the general population and in competitive sports : From theory to clinical screening-a review from the MiSpEx network]

BACKGROUND

Lumbar back pain and the high risk of chronic complaints is not only an important health concern in the general population but also in high performance athletes. In contrast to non-athletes, there is a lack of research into psychosocial risk factors in athletes. Moreover, the development of psychosocial screening questionnaires that would be qualified to detect athletes with a high risk of chronicity is in the early stages. The purpose of this review is to give an overview of research into psychosocial risk factors in both populations and to evaluate the performance of screening instruments in non-athletes.

METHODS

The databases MEDLINE, PubMed, and PsycINFO were searched from March to June 2016 using the keywords "psychosocial screening", "low back pain", "sciatica" and "prognosis", "athletes". We included prospective studies conducted in patients with low back pain with and without radiation to the legs, aged ≥18 years and a follow-up of at least 3 months.

RESULTS

We identified 16 eligible studies, all of them conducted in samples of non-athletes. Among the most frequently published screening questionnaires, the Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ) demonstrated a sufficient early prediction of return to work and the STarT Back Screening Tool (SBT) revealed acceptable performance predicting pain-related impairment. The prediction of future pain was sufficient with the Risk Analysis of Back Pain Chronification (RISC-BP) and the Heidelberg Short Questionnaire (HKF).

CONCLUSION

Psychosocial risk factors of chronic back pain, such as chronic stress, depressive mood, and maladaptive pain processing are becoming increasingly more recognized in competitive sports. Screening instruments that have been shown to be predictive in the general population are currently being tested for suitability in the German MiSpEx research consortium.

The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study

QUESTIONS

In people with chronic non-specific low back pain (LBP), what is the predictive and discriminative validity of the STarT Back Tool (SBT) for pain intensity, self-reported LBP-related disability, and global self-perceived change at 1-year follow-up? What is the profile of the SBT risk subgroups with respect to demographic variables, pain intensity, self-reported LBP-related disability, and psychological measures?

DESIGN

Prospective cohort study.

PARTICIPANTS

A total of 290 adults with dominant axial LBP of≥3months' duration recruited from the general community, and private physiotherapy, psychology, and pain-management clinics in Western Australia.

OUTCOME MEASURES

The 1-year follow-up measures were pain intensity, LBP-related disability, and global self-perceived change.

RESULTS

Outcomes were collected on 264 participants. The SBT categorised 82 participants (28%) as low risk, 116 (40%) as medium risk, and 92 (32%) as high risk. The risk subgroups differed significantly (p<0.05) on baseline pain, disability, and psychological scores. The SBT's predictive ability was strongest for disability: RR was 2.30 (95% CI 1.28 to 4.10) in the medium-risk group and 2.86 (95% CI 1.60 to 5.11) in the high-risk group. The SBT's predictive ability was weaker for pain: RR was 1.25 (95% CI 1.04 to 1.51) in the medium-risk group and 1.26 (95% CI 1.03 to 1.52) in the high-risk group. For the SBT total score, the AUC was 0.71 (95% CI 0.64 to 0.77) for disability and 0.63 (95% CI 0.55 to 0.71) for pain.

CONCLUSION

This was the first large study to investigate the SBT in a population exclusively with chronic LBP. The SBT provided an acceptable indication of 1-year disability, had poor predictive and discriminative ability for future pain, and was unable to predict or discriminate global perceived change. In this cohort with chronic non-specific LBP, the SBT's predictive and discriminative abilities were restricted to disability at 1year. [Kendell M, Beales D, O'Sullivan P, Rabey M, Hill J, Smith A (2018) The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study. Journal of Physiotherapy 64: 107-113].

Predictive ability of the start back tool: an ancillary analysis of a low back pain trial from Danish general practice

Background

Low back pain (LBP) is a common cause of contact with the primary healthcare sector. In some patients, symptoms quickly resolve, but others develop long-lasting pain and disability. To improve the care pathway for patients with LBP, the STarT Back Tool (STarT) questionnaire has been developed. It helps initial decision-making by subgrouping patients on the basis of their prognosis and helps to target treatment according to prognosis. An assumption behind the use of STarT is the ability to predict functional improvement. This assumption has never been tested in a population that consists exclusively of patients enrolled when consulting a Danish general practitioner for LBP. The aim of this study was to investigate STarT’s ability to predict a 30% improvement in the Roland Morris Disability Questionnaire (RMDQ) score.

Methods

This was an ancillary analysis using data from a Danish guideline implementation study (registered at ClinicalTrials.gov NCT01699256). An inclusion criterion was age 18 to 65 years of age. Exclusion criteria were pregnancy, fractures, and signs of underlying pathology. Patient-reported STarT score and the Roland Morris Disability Questionnaire were administered at baseline and again after 4, 8, and 52 weeks.

Results

Between January 2013 and July 2014, 475 patients from the original trial participated with questionnaires. From this subpopulation, 441 (92.8%) patients provided information regarding STarT. Baseline and eight-week RMDQ data were available for 304 (64.0%) patients. After 8 weeks, 61 (65.6%) in the low-risk group, 67 (54.9%) in the medium-risk group, and 33 (37.1%) in the high-risk group had achieved a 30% improvement in the RMDQ score. After 8 weeks, high-risk patients were at 61% (95% CI: 20–125%, P < 0.001) higher risk of not achieving a 30% improvement in the RMDQ score compared with patients in either the low-risk group or the medium-risk group.

Conclusion

STarT was predictive for functional improvement in patients from general practice with LBP.

Trial registration

ClinicalTrials.gov NCT01699256, Nov 29, 2016 (registered retrospectively).

The STarT back tool in chiropractic practice: a narrative review

Background

The Keele STarT Back Tool was designed for primary care medical physicians in the UK to determine the risk for persistent disabling pain in patients with musculoskeletal pain and to tailor treatments accordingly. In medical and physical therapy settings, STarT Back Tool’s tailored care plans improved patients’ low back pain outcomes and lowered costs.

Objective

Review studies using the STarT Back Tool in chiropractic patient populations.

Methods

PubMed, The Cochrane Library, Index to Chiropractic Literature, and Science Direct databases were searched. Articles written in English, published in peer-reviewed journals, that studied the STarT Back Tool in patients seeking chiropractic care were included.

Results

Seven articles were selected based on inclusion and exclusion criteria. The STarT Back Tool was feasibly incorporated into 19 chiropractic clinics in Denmark. Total STarT Back 5-item score correlated moderately with total Bournemouth Questionnaire score. Two studies reported that the STarT Back Tool’s predictive ability was poor, while another reported that the tool predicted outcomes in patients scoring in the medium and high risk categories who completed the STarT Back 2 days after their initial visit. A study examining Danish chiropractic, medical and physical therapy settings revealed that only baseline episode duration affected STarT Back’s prognostic ability across all care settings. The tool predicted pain and disability in chiropractic patients whose episode duration was at least 2 weeks, but not in patients with an episode duration <2 weeks.

Conclusion

While the STarT Back Tool can be incorporated into chiropractic settings and correlates with some elements of the Bournemouth Questionnaire, its prognostic ability is sometimes limited by the shorter low back pain episodes with which chiropractic patients often present. It may be a better predictor in patients whose episode duration is at least 2 weeks. Studies examining outcomes of stratified care in chiropractic patients are needed.

The Japanese version of the STarT Back Tool predicts 6-month clinical outcomes of low back pain

BACKGROUND

The STarT Back Tool classifies patients into low-, medium-, or high-risk groups according to risk for chronic low back pain. The Japanese version of the STarT Back Tool (STarT-J) has been translated and psychometrically validated. The present analysis investigated the predictive ability of the STarT-J.

METHODS

Baseline data were collected through an online survey conducted with Japanese patients with low back pain. Long-term outcomes were assessed in a 6-month follow-up survey. Clinical outcomes at 6 months were evaluated with a pain numerical rating scale, the Roland-Morris Disability Questionnaire, and the EuroQol 5 Dimension. Differences in these scores among the three STarT-J risk groups were analyzed. Participants' perceived changes in low back pain and overall health status were examined to determine associations between the chronicity of low back pain at 6 months and STarT-J risk groups.

RESULTS

Data of 1228 volunteers who responded to the baseline and follow-up surveys were included in this analysis. Mean ± standard deviation (SD) scores for the pain numerical rating scale and the Roland-Morris Disability Questionnaire were highest in the high-risk group (5.6 ± 1.9 and 9.6 ± 7.5) and lowest in the low-risk group (3.9 ± 1.6 and 2.1 ± 3.5). Mean ± SD EuroQol 5 Dimension index scores were lowest in the high-risk group (0.66 ± 0.20) and highest in the low-risk group (0.86 ± 0.14). A small percentage of high-risk patients (5.3%) perceived improvement in low back pain at the 6-month follow-up.

CONCLUSIONS

The STarT-J predicted 6-month pain and disability outcomes. The STarT-J is an easy-to-use tool to screen for patients who are more likely to have chronic low back pain, and may be useful to initiate stratified care in primary care settings.