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

A Multidimensional Regression Model for Predicting Recurrence in Chronic Low Back Pain

BACKGROUND

Recurrence is common in chronic low back pain (CLBP). However, predicting the recurrence risk remains a challenge. The aim is to develop and validate a machine learning tool to predict the recurrence risk in patients with CLBP by using multidimensional medical information.

METHODS

This prospective cohort study consecutively enrolled 341 patients with CLBP from two hospitals between 1 January 2021 and 31 December 2021. Patients from both centres were used for model development and internal validation, employing multivariate logistic regression (MRL) along with three additional machine learning algorithms. The multidimensional model (MDM) was used to predict recurrence in the next 2 years and was compared with the widely used prognostic tool, the STarT BACK Tool (SBT). The models' performance in detecting recurrence was evaluated using several metrics, including the area under the receiver operating characteristic curve (AUC), decision curve analysis, accuracy, sensitivity and specificity.

RESULTS

A total of 131 patients (38.42%) experienced recurrence. In the MRL model, factors linked to recurrence odds included progressive lower limb weakness, anxiety, mechanical pressure test, number of previous episodes, Oswestry disability index and multifidus proton density fat fraction. For recurrence prediction, the MRL-MDM achieved an AUC of 0.813 (95% CI, 0.765-0.862), sensitivity of 85.2% and specificity of 70.2% in internal validation. In comparison, the SBT for recurrence had an AUC of 0.555 (95% CI, 0.518-0.592), sensitivity of 93.3% and specificity of 17.6%.

CONCLUSION

The MDM may predict recurrence in patients with CLBP over a 2-year period, surpassing the performance of SBT.

SIGNIFICANCE STATEMENT

This study found that the STarT BACK tool is suboptimal in predicting the 2-year recurrence of chronic low back pain (CLBP). Our proposed multidimensional machine learning model aids clinicians in identifying patients at high risk for future recurrence of CLBP and in implementing appropriate preventive measures. Given the considerable healthcare resource utilisation associated with the frequent recurrence of CLBP, our novel model provides significant assistance in addressing this issue, demonstrating substantial clinical relevance.

Prediction models for outcomes in people with low back pain receiving conservative treatment: a systematic review

OBJECTIVES

To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment.

STUDY DESIGN AND SETTING

In this systematic review, literature searches were conducted in Embase, Medline, and cumulative index of nursing and allied health literature from their inception until February 2024. Studies containing follow-up assessment (eg, prospective cohort studies, registry-based studies) investigating prediction models of outcomes (eg, pain intensity and disability) for people with LBP receiving conservative treatment were included. Two independent reviewers performed the study selection, the data extraction using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies, and risk of bias assessment using the Prediction model Risk of Bias Assessment. Findings of individual studies were reported narratively taking into account the discrimination and calibration measures of the prediction models.

RESULTS

Seventy-five studies developing or investigating the validity of 216 models were included in this review. Most prediction models investigated people receiving physiotherapy treatment and most models included sociodemographic variables, clinical features, and self-reported measures as predictors. The discriminatory capacity of the internal validity of the 27 prediction models for pain intensity varied greatly showing a c-statistic ranging from 0.48 to 0.94. Similarly, the discriminatory capacity for 31 models for disability had the same pattern showing a c-statistic ranging from 0.48 to 0.86. The calibration measures of the internal validity of the prediction models predicting pain intensity and disability showed to be adequate. Only one of 3 studies testing the external validity of models to predict pain intensity and disability and reported both discrimination and calibration measures, which showed to be inadequate. The prediction models predicting the secondary outcomes (eg, self-reported recovery, quality of life, return to work) showed varied performance measures for internal validity, and only 2 studies tested the external validity of models although they did not provide performance the performance measures.

CONCLUSION

Several prediction models have been developed for people with LBP receiving conservative treatment; however, most show inadequate discriminatory validity. A few studies externally validated the prediction models and future studies should focus on testing this before implementing in clinical practice.

Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool

BACKGROUND

Low back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explored, including those employing artificial intelligence (AI), they still lack scalability and real-time capabilities. Digital care programs (DCPs) facilitate seamless data collection through the Internet of Things and cloud storage, creating an ideal environment for developing and implementing an AI predictive tool to assist clinicians in dynamically optimizing treatment.

OBJECTIVE

This study aims to develop an AI tool that continuously assists physical therapists in predicting an individual's potential for achieving clinically significant pain relief by the end of the program. A secondary aim was to identify predictors of pain nonresponse to guide treatment adjustments.

METHODS

Data collected actively (eg, demographic and clinical information) and passively in real-time (eg, range of motion, exercise performance, and socioeconomic data from public data sources) from 6125 patients enrolled in a remote digital musculoskeletal intervention program were stored in the cloud. Two machine learning techniques, recurrent neural networks (RNNs) and light gradient boosting machine (LightGBM), continuously analyzed session updates up to session 7 to predict the likelihood of achieving significant pain relief at the program end. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curves, specificity, and sensitivity. Model explainability was assessed using SHapley Additive exPlanations values.

RESULTS

At each session, the model provided a prediction about the potential of being a pain responder, with performance improving over time (P<.001). By session 7, the RNN achieved an ROC-AUC of 0.70 (95% CI 0.65-0.71), and the LightGBM achieved an ROC-AUC of 0.71 (95% CI 0.67-0.72). Both models demonstrated high specificity in scenarios prioritizing high precision. The key predictive features were pain-associated domains, exercise performance, motivation, and compliance, informing continuous treatment adjustments to maximize response rates.

CONCLUSIONS

This study underscores the potential of an AI predictive tool within a DCP to enhance the management of LBP, supporting physical therapists in redirecting care pathways early and throughout the treatment course. This approach is particularly important for addressing the heterogeneous phenotypes observed in LBP.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946 and NCT05417685; https://clinicaltrials.gov/ct2/show/NCT05417685.

Psychological Risk Factors for Delayed Recovery Among Active Duty Service Members Seeking Treatment for Musculoskeletal Complaints at a Navy Shore-Based Military Medical Treatment Facility

INTRODUCTION

Musculoskeletal injuries (MSIs) are a leading cause of separation from the U.S. Navy. Data have shown that several psychological responses to MSI are associated with treatment outcomes. Yellow flags are maladaptive psychological responses to injury and predict delayed recovery, whereas pink flags indicate resilience after MSI and are associated with good treatment outcomes. Identifying these factors in patients with MSI would permit early targeted care to address factors that may delay their readiness for deployment and enhance factors that support recovery.

MATERIALS AND METHODS

Active duty service members with MSI who reported to physical therapy outpatient services at a naval hospital were recruited for the study. Yellow flags were assessed at baseline as part of a larger study. Participants completed the Fear Avoidance Beliefs Questionnaire (with two subscales, physical activity and work), the Pain Catastrophizing Scale, and the Hospital Anxiety and Depression Scale. Clinically relevant cut-off scores were used to indicate risk factors of delayed recovery. Pink flags were assessed with the Pain Self-Efficacy Questionnaire and a measure of positive outcome expectations for recovery.

RESULTS

Two hundred and ninety participants responded to some or all of the questionnaires. Of these, 82% exceeded the cut-off scores on the physical activity subscale of the Fear Avoidance Beliefs Questionnaire, and 39% did so on the work subscale. Pain catastrophizing exceeded the cut-off in only 4.9% of the sample. Forty-three percent of these exceeded the cut-off for the anxiety subscale of the Hospital Anxiety and Depression Scale; 27% exceeded the cut-off on the depression subscale of the Hospital Anxiety and Depression Scale. Additionally, 54% endorsed scores greater than 40 on the Pain Self-Efficacy Questionnaire, and 53% endorsed a high score on the positive outcome expectations.

CONCLUSIONS

A substantial portion of the sample endorsed elevated scores on one or more indicators of delayed recovery from MSI. Most participants showed a fear of physical activity, and approximately half reported pain-related distress (anxiety and depression). In addition, feelings of self-efficacy and positive outcome expectations of treatment were endorsed by only about half of the participants, indicating that the remaining half did not report adaptive responses to MSI. Early identification of these risk factors will allow for targeted treatment approaches that incorporate these yellow flags into treatment and support a psychologically informed approach to physical therapy. This approach is likely to reduce delayed recovery and improve deployment readiness.

Prognostic Models for Chronic Low Back Pain Outcomes in Primary Care Are at High Risk of Bias and Lack Validation-High-Quality Studies Are Needed: A Systematic Review

OBJECTIVE: To provide an updated overview of available prognostic models for people with chronic low back pain (LBP) in primary care. DESIGN: Prognosis systematic review LITERATURE SEARCH: We searched for relevant studies on MEDLINE, Embase, Web of Science, and CINAHL databases (up to July 13, 2022), and performed citation tracking in Web of Science. STUDY SELECTION CRITERIA: We included observational (cohort or nested case-control) studies and randomized controlled trials that developed or validated prognostic models for adults with chronic LBP in primary care. The outcomes of interest were physical functioning, pain intensity, and health-related quality of life at any follow-up time-point. DATA SYNTHESIS: Data were extracted using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and the Prediction model Risk of Bias Assessment Tool (PROBAST) tool was used to evaluate the risk of bias of the models. Due to the number of studies retrieved and the heterogeneity, we reported the results descriptively. RESULTS: Ten studies (out of 5593 hits screened) with 34 models met our inclusion criteria, of which six are development studies and four are external validation studies. Five studies reported the area under the curve of the models (ranging from 0.48 to 0.84), whereas no study reported calibration indices. The most promising model is the Örebro Musculoskeletal Pain Screening Questionnaire Short-Form. CONCLUSIONS: Given the high risk of bias and lack of external validation, we cannot recommend that clinicians use prognostic models for patients with chronic LBP in primary care settings. J Orthop Sports Phys Ther 2024;54(5):1-13. Epub 15 February 2024. doi:10.2519/jospt.2024.12081.

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.

The Management of Chronic Pain: Re-Centring Person-Centred Care

The drive for a more person-centred approach in the broader field of clinical medicine is also gaining traction in chronic pain treatment. Despite current advances, a further departure from 'business as usual' is required to ensure that the care offered or received is not only effective but also considers personal values, goals, abilities, and day-to-day realities. Existing work typically focuses on explaining pain symptoms and the development of standardised interventions, at the risk of overlooking the broader consequences of pain in individuals' lives and individual differences in pain responses. This review underscores the importance of considering additional factors, such as the influence of chronic pain on an individual's sense of self. It explores innovative approaches to chronic pain management that have the potential to optimise effectiveness and offer person-centred care. Furthermore, it delves into research applying hybrid and individual formulations, along with self-monitoring technologies, to enhance pain assessment and the tailoring of management strategies. In conclusion, this review advocates for chronic pain management approaches that align with an individual's priorities and realities while fostering their active involvement in self-monitoring and self-management.

Predictive performance of the STarT Back tool for poor outcomes in patients with low back pain: protocol for a systematic review and meta-analysis

INTRODUCTION

Subgroups for Targeted Treatment Back Tool (SBT) is a brief multiple-construct risk prediction tool for patients with low back pain (LBP). Thus far, the predictive ability of this tool has been inconsistent. Therefore, we aim to conduct a literature review on the predictive ability of the SBT to determine the outcomes of patients with LBP. The results of this review should improve the ability of the SBT to predict poor outcomes in patients with LBP.

METHODS AND ANALYSIS

Databases including PubMed, EMBASE, Cochrane Central, Web of Science, Chinese National Knowledge Infrastructure Databases, Chinese Science and Technology Journal Database, and Wanfang will be searched for studies on SBT and LBP from their inception until 31 March 2023. Longitudinal studies investigating the association between SBT subgroups and LBP outcomes, including pain, disability and quality of life, will be included. The identified studies will be independently screened for eligibility by two reviewers. A standardised sheet will be used to extract data. The Newcastle-Ottawa Scale will be used to assess the methodological quality of the included studies. Heterogeneity will be evaluated by the χ2 test with Cochran's Q statistic and quantified by the I2 statistic. The results will be synthesised qualitatively and presented as pooled risk ratios or beta coefficients quantitatively. The results will also be presented using their 95% confidence limits. Publication bias will be assessed using the method proposed by Egger and by visual inspection of funnel plots.

ETHICS AND DISSEMINATION

This study is a secondary analysis of original studies that received ethics approval. Therefore, prior ethical approval is not required for this study. The findings will be submitted to relevant peer-reviewed journals for publication and presented at profession-specific conferences.

TRIAL REGISTRATION NUMBER

PROSPERO registration numberCRD42022309189.

Effect of integrated exercise therapy and psychosocial interventions on self-efficacy in patients with chronic low back pain: A systematic review

OBJECTIVE

Investigate if integrated exercise and psychosocial (EP) interventions effect self-efficacy to manage pain and self-efficacy for physical functioning compared to alternate interventions, usual care, waitlists and attention controls for individuals with chronic low back pain (CLBP).

METHODS

MEDLINE, Embase, CINAHL, Web of Science, PsychINFO, PEDro, and Cochrane Library were searched. Included randomized controlled trials utilized an EP intervention for CLBP and measured self-efficacy. Independent reviewers screened abstracts, reviewed full-texts, extracted data, and assessed risk of bias. GRADE, synthesis without meta-analysis, and ranges of effects (Hedges' g) were used.

RESULTS

2207 Participants were included (22-studies). EP interventions positively effected self-efficacy to manage pain short-term compared to usual care (range of effects: -0.02, 0.94) and controls (range of effects: 0.69, 0.80) and intermediately compared to usual care (range of effects: 0.11, 0.29); however, no differences were found when compared to alternate interventions. EP interventions positively effected self-efficacy for physical functioning short-term compared to alternate interventions (range of effects: 0.57, 0.71), usual care (range of effects: -0.15, 0.94), and controls (range of effects: 0.31, 0.56), and intermediately compared to alternate interventions (1-study, effect: 0.57) and controls (1-study, effect: 0.56). Conclusions were limited by low to very low-quality-evidence often from risk of bias, imprecision, and clinical/statistical heterogeneity.

CONCLUSIONS

EP interventions may be more effective short-term for self-efficacy to manage pain than usual care and waitlists, but not alternate interventions. EP interventions may be effective for self-efficacy for physical functioning at short- and intermediate-term compared to alternate interventions, usual care, waitlist and attention controls. Considerations for future research include methods for blinding and measurement of self-efficacy for physical functioning.

Four Variables Were Sufficient for Low Back Pain: Determining Which Patient-Reported Tools Pain and Disability Improvements

OBJECTIVE: To predict 30- and 180-day improvements in disability and pain for patients seeking physical therapy care for low back pain (LBP). DESIGN: Longitudinal cohort. METHODS: Baseline assessment was completed by 259 patients with chief complaint of LBP, and the assessment includes psychosocial measures (Keele STarT Back Screening [SBST] and the Optimal Screening for Prediction of Referral and Outcome Yellow Flag [OSPRO-YF] tools), the Optimal Screening for Prediction of Referral and Outcome Review of Symptoms (OSPRO-ROS) and the Review of Symptoms Plus (OSPRO-ROS+) tools, the Charlson Comorbidity Index (CCI), the Area Deprivation Index (ADI), and the National Institute of Health Chronic Pain Criteria (NIH-CP). Using the Modified Low Back Disability Questionnaire (MDQ) and the Numeric Pain Rating Scale (NPRS) as primary outcomes, statistical analysis determined multiple sets of predictor variables with similar model performance. RESULTS: The parsimonious "best model" for prediction of the 180-day MDQ change included 3 predictors (Admit MDQ, NIH-CP, and OSPRO ROS+) because it had the lowest penalized goodness-of-fit statistic (BIC = -35.21) and the highest explained variance (R2 = 0.295). The parsimonious "best model" for 180-day NPRS change included 2 variables (Admit NPRS and OSPRO-ROS+) with the lowest penalized goodness-of-fit statistic (BIC = -18.2) and the highest explained variance (R2 = 0.190). CONCLUSION: There were many model options with similar statistical performance when using established measures to predict MDQ and NPRS outcomes. A potential variable set for a standard predictive model that balances statistical performance with pragmatic considerations included the OSPRO-ROS+, OSPRO-YF, NIH-CP definition, and admit MDQ and NPRS scores. J Orthop Sports Phys Ther 2022;52(10):685-693. Epub: 12 August 2022. doi:10.2519/jospt.2022.11018.

Back to the Future: A Report From the 16th International Forum for Back and Neck Pain Research in Primary Care and Updated Research Agenda

STUDY DESIGN

The 16th meeting of the International Forum for Back and Neck Pain Research in Primary Care was held in Québec City in July 2019 under the theme of innovation. This paper addresses the state of research in the field.

OBJECTIVE

To ascertain the evolution of knowledge and clinical application in back and neck pain and identify shifting research priorities.

MATERIALS AND METHODS

After a brief presentation of the Forum and its history, the current state of the field was depicted from the scientific program and the recordings of the plenary and parallel oral and poster communications of Forum XVI. Research agendas established in 1995 and 1997 were updated from a survey of a multidisciplinary group of experts in the field. A discussion of the progress made and challenges ahead follows.

RESULTS

While much progress has been made at improving knowledge at managing back pain in the past 25 years, most research priorities from earlier decades are still pertinent. The need for integration of physical and psychological interventions represents a key challenge, as is the need to better understand the biological mechanisms underlying back and neck pain to develop more effective interventions. Stemming the tide of back and neck pain in low and middle-income countries and avoiding the adoption of low-value interventions appear particularly important. The Lancet Low Back Pain Series initiative, arising from the previous fora, and thoughts on implementing best practices were extensively discussed, recognizing the challenges to evidence-based knowledge and practice given competing interests and incentives.

CONCLUSION

With the quantity and quality of research on back and neck pain increasing over the years, an update of research priorities helped to identify key issues in primary care.

Psychosocial Predictors of Pain and Disability Outcomes in People with Chronic Low Back Pain Treated Conservatively by Guideline-Based Intervention: A Systematic Review

Objective

Previous evidence has recommended conservative interventions as the best treatment in individuals with chronic low back pain (CLBP). However, the influence of psychosocial factors on the treatment outcomes is unclear. Therefore, this systematic review aimed to address the psychosocial factors that influence changes in pain and disability in patients with CLBP after a guideline-based conservative intervention.

Methods

Four electronic databases were systematically searched from inception until September 2020 for prospective studies examining the relationship between psychosocial factors and the outcomes of pain and disability after conservative intervention. All included studies were selected, extracted, and critically evaluated by two independent reviewers.

Results

In total, 15 studies were included in this systematic review. The results support the link between the baseline fear of movement, depression, self-efficacy, and catastrophizing with future functional disability outcomes after conservative interventions. However, these factors were less likely to predict changes in pain intensity outcomes after conservative interventions. Self-efficacy seems to mediate between some of the baseline psychosocial factors (eg, fear) and future pain and disability.

Conclusion

Fear of movement, self-efficacy, catastrophizing and depression were consistently reported to predict disability outcomes irrespective of the type of conservative intervention. This highlights the importance of addressing these factors in conservative management of CLBP.

Do chronic low back pain subgroups derived from dynamic quantitative sensory testing exhibit differing multidimensional profiles?

OBJECTIVES

The relationship of pain sensitivity with pain and disability in low back pain (LBP) is complicated. It has been suggested increased understanding of dynamic quantitative sensory testing (QST) might be useful in increasing understanding of these relationships. This study aimed to create subgroups based on participant responses to dynamic QST, profile these subgroups based on multidimensional variables (including clinical measures of pain and disability, psychological and lifestyle variables and static QST), and investigate the association of subgroup membership with levels of pain intensity, LBP-related disability and disability risk at 12-month follow up.

METHODS

Participants (n=273) with dominant axial chronic non-specific LBP with duration of pain >3 months were included in this study. At baseline, eligible participants completed a self-report questionnaire to collect demographic, clinical, psychological and lifestyle data prior to dynamic and static QST. Dynamic QST measures were conditioned pain modulation (CPM) and temporal summation (TS). At 12-months follow up, clinical data were collected, including pain intensity and LBP-related disability. Sub-groups were formed by cross-tabulation. Analysis was undertaken to profile dynamic QST subgroup on demographic, clinical, psychological, lifestyle and static QST measures. Associations between dynamic QST subgroups and follow-up clinical variables were examined.

RESULTS

Based on dynamic QST, participants were allocated into four subgroups; normal CPM and normal TS (n=34, 12.5%); normal CPM and facilitated TS (n=6, 2.2%); impaired CPM and normal TS (n=186, 68.1%); impaired CPM and facilitated TS (n=47, 17.2%). At baseline no differences were demonstrated between subgroups across most clinical variables, or any psychological or lifestyle measures. The two subgroups with impaired CPM were more likely to have a higher number of painful body areas. Cold pain sensitivity was heightened in both the subgroups with facilitated TS. Subgroups did not differ across pain intensity, LBP-related disability and disability risk stratification at follow-up.

CONCLUSIONS

The profiles of people with axial LBP did not vary significantly across dynamic QST subgroups, save for those in groups with impaired CPM being more likely to have more widespread symptoms and those with facilitated TS having heightened cold pain sensitivity. Further, subgroup membership was not related to future pain and disability. The role of dynamic QST profiles in LBP remains unclear. Further work is required to understand the role of pain sensitivity in LBP. The utility of dynamic QST subgrouping might not be in determining of future disability. Future research might focus on treatment modifying effects of dynamic QST subgroups.

Low back pain

Low back pain covers a spectrum of different types of pain (eg, nociceptive, neuropathic and nociplastic, or non-specific) that frequently overlap. The elements comprising the lumbar spine (eg, soft tissue, vertebrae, zygapophyseal and sacroiliac joints, intervertebral discs, and neurovascular structures) are prone to different stressors, and each of these, alone or in combination, can contribute to low back pain. Due to numerous factors related to low back pain, and the low specificity of imaging and diagnostic injections, diagnostic methods for this condition continue to be a subject of controversy. The biopsychosocial model posits low back pain to be a dynamic interaction between social, psychological, and biological factors that can both predispose to and result from injury, and should be considered when devising interdisciplinary treatment plans. Prevention of low back pain is recognised as a pivotal challenge in high-risk populations to help tackle high health-care costs related to therapy and rehabilitation. To a large extent, therapy depends on pain classification, and usually starts with self-care and pharmacotherapy in combination with non-pharmacological methods, such as physical therapies and psychological treatments in appropriate patients. For refractory low back pain, a wide range of non-surgical (eg, epidural steroid injections and spinal cord stimulation for neuropathic pain, and radiofrequency ablation and intra-articular steroid injections for mechanical pain) and surgical (eg, decompression for neuropathic pain, disc replacement, and fusion for mechanical causes) treatment options are available in carefully selected patients. Most treatment options address only single, solitary causes and given the complex nature of low back pain, a multimodal interdisciplinary approach is necessary. Although globally recognised as an important health and socioeconomic challenge with an expected increase in prevalence, low back pain continues to have tremendous potential for improvement in both diagnostic and therapeutic aspects. Future research on low back pain should focus on improving the accuracy and objectivity of diagnostic assessments, and devising treatment algorithms that consider unique biological, psychological, and social factors. High-quality comparative-effectiveness and randomised controlled trials with longer follow-up periods that aim to establish the efficacy and cost-effectiveness of low back pain management are warranted.

Convergent and known group validity of the STarT Back Tool in a Nigerian population with chronic low back pain

Background The STarT Back Tool (SBT) was developed to aid the stratification of patients with low-back pain (LBP), based on future risks for physical disability.

Objective Investigation of the convergent and known group validity of the SBT in a Nigerian population with chronic LBP using disability-related psychosocial outcomes.

Method Cross-sectional study involved 30 consenting patients with chronic LBP in an outpatient physiotherapy clinic of a tertiary health institution in Nigeria. Future risk of disability was assessed using the SBT. Psychosocial variables of pain catastrophizing, fear-avoidance beliefs (FAB), and kinesiophobia were assessed using the Pain Catastrophizing Scale, the Fear-Avoidance Beliefs Questionnaire and the Tampa Scale of Kinesiophobia, respectively. Data was analysed using percentages and Spearman correlation.

Results Based on the SBT, there were rates of 43.3 % and 23.3 % for low and high future risks of physical disability. The median score of pain catastrophizing was 13.5, that of FAB came in at 16.5 related to physical activity and 14.0 related to work, and the score for kinesiophobia amounted to 39. The SBT total scores moderately correlated with the FAB related to work (rho = 0.45 (95 % CI 0.09–0.700). FAB related to physical activity (p = 0.040) significantly differed across the SBT subgroups.

Conclusion The SBT and the other psychosocial instruments used in this study did not correlate to a sufficient degree. In addition, patients exhibiting catastrophizing, fear-avoidance beliefs, or kinesiophobia could not be differentiated based on SBT risk groups. The results should be interpreted with caution until findings from additional studies with sufficient sample sizes are at hand.

Use of the STarT Back Screening Tool in patients with chronic low back pain receiving physical therapy interventions

BACKGROUND

The STarT Back Screening Tool (SBST) is used to stratify care. It is unclear if the SBST approach works as well for patients in low- and medium-income countries as for patients from high-income countries.

OBJECTIVES

(1) To investigate whether patients with chronic low back pain (LBP) stratified by the SBST are different at baseline; (2) to describe the clinical course for each SBST subgroup; (3) to investigate the SBST utility to predict clinical outcomes; and (4) to determine which SBST subgroup show greater clinical improvement.

DESIGN

This is a secondary analysis of data derived from a previously published clinical trial.

METHODS

148 patients with chronic nonspecific LBP were included. Pain intensity, disability, global perceived effect, and the SBST were assessed at baseline and at 5, 12, and 24 weeks after baseline. Descriptive data were provided and ANOVA, unadjusted and adjusted regression models, and linear mixed models were used for data analysis.

RESULTS

Duration of symptoms, use of medication, pain, disability, and global perceived effect were different between SBST subgroups. Clinical improvements over a 6-month period were consistently greater in patients classified as high risk. The SBST was able to predict disability but this predictability decreased when the analysis was adjusted for possible confounders.

CONCLUSION

Clinical outcomes were different between SBST subgroups over 6 months. Adjusting for confounders influenced the predictability of SBST. Patients classified as high risk presented higher improvements in terms of disability.

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.

Individual recovery expectations and prognosis of outcomes in non-specific low back pain: prognostic factor review

BACKGROUND

Low back pain is costly and disabling. Prognostic factor evidence can help healthcare providers and patients understand likely prognosis, inform the development of prediction models to identify subgroups, and may inform new treatment strategies. Recent studies have suggested that people who have poor expectations for recovery experience more back pain disability, but study results have differed.

OBJECTIVES

To synthesise evidence on the association between recovery expectations and disability outcomes in adults with low back pain, and explore sources of heterogeneity.

SEARCH METHODS

The search strategy included broad and focused electronic searches of MEDLINE, Embase, CINAHL, and PsycINFO to 12 March 2019, reference list searches of relevant reviews and included studies, and citation searches of relevant expectation measurement tools.

SELECTION CRITERIA

We included low back pain prognosis studies from any setting assessing general, self-efficacy, and treatment expectations (measured dichotomously and continuously on a 0 - 10 scale), and their association with work participation, clinically important recovery, functional limitations, or pain intensity outcomes at short (3 months), medium (6 months), long (12 months), and very long (> 16 months) follow-up.

DATA COLLECTION AND ANALYSIS

We extracted study characteristics and all reported estimates of unadjusted and adjusted associations between expectations and related outcomes. Two review authors independently assessed risks of bias using the Quality in Prognosis Studies (QUIPS) tool. We conducted narrative syntheses and meta-analyses when appropriate unadjusted or adjusted estimates were available. Two review authors independently graded and reported the overall quality of evidence.

MAIN RESULTS

We screened 4635 unique citations to include 60 studies (30,530 participants). Thirty-five studies were conducted in Europe, 21 in North America, and four in Australia. Study populations were mostly chronic (37%), from healthcare (62%) or occupational settings (26%). General expectation was the most common type of recovery expectation measured (70%); 16 studies measured more than one type of expectation. Usable data for syntheses were available for 52 studies (87% of studies; 28,885 participants). We found moderate-quality evidence that positive recovery expectations are strongly associated with better work participation (narrative synthesis: 21 studies; meta-analysis: 12 studies, 4777 participants: odds ratio (OR) 2.43, 95% confidence interval (CI) 1.64 to 3.62), and low-quality evidence for clinically important recovery outcomes (narrative synthesis: 12 studies; meta-analysis: 5 studies, 1820 participants: OR 1.89, 95% CI 1.49 to 2.41), both at follow-up times closest to 12 months, using adjusted data. The association of recovery expectations with other outcomes of interest, including functional limitations (narrative synthesis: 10 studies; meta-analysis: 3 studies, 1435 participants: OR 1.40, 95% CI 0.85 to 2.31) and pain intensity (narrative synthesis: 9 studies; meta-analysis: 3 studies, 1555 participants: OR 1.15, 95% CI 1.08 to 1.23) outcomes at follow-up times closest to 12 months using adjusted data, is less certain, achieving very low- and low-quality evidence, respectively. No studies reported statistically significant or clinically important negative associations between recovery expectations and any low back pain outcome.

AUTHORS' CONCLUSIONS

We found that individual recovery expectations are probably strongly associated with future work participation (moderate-quality evidence) and may be associated with clinically important recovery outcomes (low-quality evidence). The association of recovery expectations with other outcomes of interest is less certain. Our findings suggest that recovery expectations should be considered in future studies, to improve prognosis and management of low back pain.

Addition of MoodGYM to physical treatments for chronic low back pain: A randomized controlled trial

Background

Low back pain (LBP) is prevalent, costly and disabling. A biopsychosocial treatment approach involving physical and cognitive behavioural therapy (CBT) is recommended for those with chronic LBP. It is not known if online psychological coaching tools might have a role in the secondary prevention of LBP related disability. To assess the effectiveness of an internet-delivered psychological program (MoodGYM) in addition to standard physical treatment in patients with chronic non-specific LBP at medium risk of ongoing disability.

Methods

A multisite randomized controlled trial was conducted with 108 participants (aged mean 50.4 ± 13.6 years) with chronic LBP attending one of six private physiotherapy or chiropractic clinics. Disability (Roland Morris Disability Questionnaire) and self-efficacy (Patient Self-Efficacy Questionnaire), were assessed at baseline, post-treatment (8-weeks) with follow-up at six- and twelve-months. Participants were randomized into either the intervention group, MoodGYM plus physical treatments, or the control group which received physical treatments alone.

Results

No statistically significant between group differences were observed for either disability at post-treatment (Effect size (standardised mean difference) 95% CI) RMD - 0.06 (- 0.45,0.31), 6-months RMD 0.01 (- 0.38,0.39) and 12-months - 0.20 (- 0.62,0.17) or self-efficacy at post-treatment PSEQ 0.06 (- 0.31,0.45), 6-months 0.02 (- 0.36,0.41) and 12-months 0.21 (- 0.16,0.63).

Conclusion

There was no additional benefit of an internet-delivered CBT program (MoodGYM) to physical treatments in those with chronic non-specific LBP at medium risk of ongoing disability measured at post-treatment, or at 6 and 12 months.

Trial registration

This trial was prospectively registered with Australian New Zealand Clinical Trials Registry Number (ACTRN) 12615000269538.

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).

Prognostic ability of the German version of the STarT Back tool: analysis of 12-month follow-up data from a randomized controlled trial

BACKGROUND

Stratified care is an up-to-date treatment approach suggested for patients with back pain in several guidelines. A comprehensively studied stratification instrument is the STarT Back Tool (SBT). It was developed to stratify patients with back pain into three subgroups, according to their risk of persistent disabling symptoms. The primary aim was to analyse the disability differences in patients with back pain 12 months after inclusion according to the subgroups determined at baseline using the German version of the SBT (STarT-G). Moreover, the potential to improve prognosis for disability by adding further predictor variables, an analysis for differences in pain intensity according to the STarT-Classification, and discriminative ability were investigated.

METHODS

Data from the control group of a randomized controlled trial were analysed. Trial participants were members of a private medical insurance with a minimum age of 18 and indicated as having persistent back pain. Measurements were made for the risk of back pain chronification using the STarT-G, disability (as primary outcome) and back pain intensity with the Chronic Pain Grade Scale (CPGS), health-related quality of life with the SF-12, psychological distress with the Patient Health Questionnaire-4 (PHQ-4) and physical activity. Analysis of variance (ANOVA), multiple linear regression, and area under the curve (AUC) analysis were conducted.

RESULTS

The mean age of the 294 participants was 53.5 (SD 8.7) years, and 38% were female. The ANOVA for disability and pain showed significant differences (p < 0.01) among the risk groups at 12 months. Post hoc Tukey tests revealed significant differences among all three risk groups for every comparison for both outcomes. AUC for STarT-G's ability to discriminate reference standard 'cases' for chronic pain status at 12 months was 0.79. A prognostic model including the STarT-Classification, the variables global health, and disability at baseline explained 45% of the variance in disability at 12 months.

CONCLUSIONS

Disability differences in patients with back pain after a period of 12 months are in accordance with the subgroups determined using the STarT-G at baseline. Results should be confirmed in a study developed with the primary aim to investigate those differences.

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.