Development of a Multivariate Prognostic Model for Pain and Activity Limitation in People With Low Back Disorders Receiving Physiotherapy

Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain

This study aims to compare the variable selection strategies of different machine learning (ML) and statistical algorithms in the prognosis of neck pain (NP) recovery. A total of 3001 participants with NP were included. Three dichotomous outcomes of an improvement in NP, arm pain (AP), and disability at 3 months follow-up were used. Twenty-five variables (twenty-eight parameters) were included as predictors. There were more parameters than variables, as some categorical variables had >2 levels. Eight modelling techniques were compared: stepwise regression based on unadjusted p values (stepP), on adjusted p values (stepPAdj), on Akaike information criterion (stepAIC), best subset regression (BestSubset) least absolute shrinkage and selection operator [LASSO], Minimax concave penalty (MCP), model-based boosting (mboost), and multivariate adaptive regression splines (MuARS). The algorithm that selected the fewest predictors was stepPAdj (number of predictors, p = 4 to 8). MuARS was the algorithm with the second fewest predictors selected (p = 9 to 14). The predictor selected by all algorithms with the largest coefficient magnitude was "having undergone a neuroreflexotherapy intervention" for NP (β = from 1.987 to 2.296) and AP (β = from 2.639 to 3.554), and "Imaging findings: spinal stenosis" (β = from -1.331 to -1.763) for disability. Stepwise regression based on adjusted p-values resulted in the sparsest models, which enhanced clinical interpretability. MuARS appears to provide the optimal balance between model sparsity whilst retaining high predictive performance across outcomes. Different algorithms produced similar performances but resulted in a different number of variables selected. Rather than relying on any single algorithm, confidence in the variable selection may be increased by using multiple algorithms.

How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data

PURPOSE

Individualised physiotherapy is an effective treatment for low back pain. We sought to determine how this treatment works by using randomised controlled trial data to develop a Bayesian Network model.

METHODS

300 randomised controlled trial participants (153 male, 147 female, mean age 44.1) with low back pain (of duration 6-26 weeks) received either individualised physiotherapy or advice. Variables with potential to explain how individualised physiotherapy works were included in a multivariate Bayesian Network model. Modelling incorporated the intervention period (0-10 weeks after study commencement-"early" changes) and the follow-up period (10-52 weeks after study commencement-"late" changes). Sequences of variables in the Bayesian Network showed the most common direct and indirect recovery pathways followed by participants with low back pain receiving individualised physiotherapy versus advice.

RESULTS

Individualised physiotherapy directly reduced early disability in people with low back pain. Individualised physiotherapy exerted indirect effects on pain intensity, recovery expectations, sleep, fear, anxiety, and depression via its ability to facilitate early improvement in disability. Early improvement in disability, led to an early reduction in depression both directly and via more complex pathways involving fear, recovery expectations, anxiety, and pain intensity. Individualised physiotherapy had its greatest influence on early change variables (during the intervention period).

CONCLUSION

Individualised physiotherapy for low back pain appears to work predominately by facilitating an early reduction in disability, which in turn leads to improvements in other biopsychosocial outcomes. The current study cannot rule out that unmeasured mechanisms (such as tissue healing or reduced inflammation) may mediate the relationship between individualised physiotherapy treatment and improvement in disability. Further data-driven analyses involving a broad range of plausible biopsychosocial variables are recommended to fully understand how treatments work for people with low back pain.

TRIALS REGISTRATION

ACTRN12609000834257.

Machine learning approaches applied in spinal pain research

The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management.

Lumbopelvic sagittal standing posture associations with anthropometry, physical activity levels and trunk muscle endurance in healthy adults

Background

Various factors, inherited and acquired, are associated with habitual spinal postures.

Objective

The purpose of this study was to identify the relationships between trunk muscle endurance, anthropometry and physical activity/inactivity and the sagittal standing lumbopelvic posture in pain-free young participants.

Methods

In this study, 112 healthy young adults (66 females), with median (IQR) age of 20 years (18.2-22 years), without low back pain, injury or trauma were included. Lumbar curve (LC) and sacral slope (SS) angles were measured in standing with a mobile phone application (iHandy level). Anthropometric, physical activity/inactivity levels (leisure-time sport involvement and sitting hours/day) and abdominal (plank prone bridge test) and paraspinal (Sorensen test) isometric muscle endurance measures were collected.

Results

LC and SS angles correlated significantly ( r = 0 . 80 , p < 0 . 001 ). Statistically significant differences for both LC ( p = 0 . 023 ) and SS ( p = 0 . 013 ) angles were identified between the male and female participants. A significant negative correlation was identified between the abdominal endurance time and LC ( r =- 0 . 27 , p = 0 . 004 ); however, the power of this result (56%) was not sufficiently high. The correlation between abdominal endurance and SS was non-significant ( r =- 0 . 17 , p = 0 . 08 ). In addition, no significant associations were identified between either of the sagittal lumbopelvic angles (LC-SS) in standing and the participants' body mass index (BMI), paraspinal endurance, leisure-time sport involvement or sitting hours/day.

Conclusion

The potential role of preventive exercise in controlling lumbar lordosis via enhancement of the abdominal muscle endurance characteristics requires further confirmation. A subsequent study, performed in a larger population of more diverse occupational involvement and leisure-time physical activity levels, is proposed.

Defining pain and interference recovery trajectories after acute non-catastrophic musculoskeletal trauma through growth mixture modeling

BACKGROUND

Recovery trajectories support early identification of delayed recovery and can inform personalized management or phenotyping of risk profiles in patients. The objective of this study was to investigate the trajectories in pain severity and functional interference following non-catastrophic musculoskeletal (MSK) trauma in an international, mixed injury sample.

METHODS

A prospective longitudinal cohort (n = 241) was formed from patients identified within four weeks of trauma, from attendance at emergency or urgent care centres located in London, ON, Canada, or Chicago, IL, USA. Pain interference was measured via the Brief Pain Inventory (London cohort) or the Neck Disability Index (Chicago cohort). Pain severity was captured in both cohorts using the numeric pain rating scale. Growth mixture modeling and RM repeated measures ANOVA approaches identified distinct trajectories of recovery within pain interference and pain severity data.

RESULTS

For pain interference, the three trajectories were labeled accordingly: Class 1 = Rapid recovery (lowest intercept, full or near full recovery by 3 months, 32.0% of the sample); Class 2 = Delayed recovery (higher intercept, recovery by 12 months, 26.7% of the sample); Class 3 = Minimal or no recovery (higher intercept, persistently high interference scores at 12 months, 41.3% of the sample). For pain severity, the two trajectories were labeled: Class 1 = Rapid recovery (lower intercept, recovery by 3 months, 81.3% of the sample); and Class 2 = Minimal or no recovery (higher intercept, flat curve, 18.7% of the sample). The "Minimal or No Recovery" trajectory could be predicted by female sex and axial (vs. peripheral) region of trauma with 74.3% accuracy across the 3 classes for the % Interference outcome. For the Pain Severity outcome, only region (axial trauma, 81.3% accuracy) predicted the "Minimal or No Recovery" trajectory.

CONCLUSIONS

These results suggest that three meaningful recovery trajectories can be identified in an international, mixed-injury sample when pain interference is the outcome, and two recovery trajectories emerge when pain severity is the outcome. Females in the sample or people who suffered axial injuries (head, neck, or low back) were more likely to be classed in poor outcome trajectories.

TRIAL REGISTRATION

National Institutes of Health - clinicaltrials.gov ( NCT02711085 ; Retrospectively registered Mar 17, 2016).

Clinical features as predictors of histologically confirmed inflammation in patients with lumbar disc herniation with associated radiculopathy

Background

An understanding of the clinical features of inflammation in low back pain with or without leg symptoms may allow targeted evaluations of anti-inflammatory treatment in randomised-controlled-trials and clinical practice.

Purpose

This study evaluated the diagnostic accuracy of clinical features to predict the presence/absence of histologically confirmed inflammation in herniated disc specimens removed at surgery in patients with lumbar disc herniation and associated radiculopathy (DHR).

Study design

Cohort Study.

Methods

Disc material from patients with DHR undergoing lumbar discectomy was sampled and underwent histological/immunohistochemistry analyses. Control discs were sampled from patients undergoing surgical correction for scoliosis. Baseline assessment comprising sociodemographic factors, subjective examination, physical examination and psychosocial screening was conducted and a range of potential clinical predictors of inflammation developed based on the existing literature. Multi-variate analysis was undertaken to determine diagnostic accuracy.

Results

Forty patients with DHR and three control patients were recruited. None of the control discs had evidence of inflammation compared to 28% of patients with DHR. Predictors of the presence of histologically confirmed inflammation included back pain < 5/10, symptoms worse the next day after injury, lumbar flexion range between 0 and 30° and a positive clinical inflammation score (at least 3 of: constant symptoms, morning pain/stiffness greater than 60-min, short walking not easing symptoms and significant night symptoms). The model achieved a sensitivity of 90.9%, a specificity of 92.9%, and a predictive accuracy of 92.3%.

Conclusion

In a sample of patients with lumbar DHR a combination of clinical features predicted the presence or absence of histologically confirmed inflammation.

Clinical relevance

These clinical features may enable targeted anti-inflammatory treatment in future RCTs and in clinical practice.

Development of an Evidence-Based Practical Diagnostic Checklist and Corresponding Clinical Exam for Low Back Pain

OBJECTIVE

The purpose of this study was to use scientific evidence to develop a practical diagnostic checklist and corresponding clinical exam for patients presenting with low back pain (LBP).

METHODS

An iterative process was conducted to develop a diagnostic checklist and clinical exam for LBP using evidence-based diagnostic criteria. The checklist and exam were informed by a systematic review focused on summarizing current research evidence for office-based clinical evaluation of common conditions causing LBP.

RESULTS

Diagnostic categories contained within the checklist and exam include nociceptive pain, neuropathic pain, and sensitization. Nociceptive pain subcategories include discogenic, myofascial, sacroiliac, and zygapophyseal (facet) joint pain. Neuropathic pain categories include neurogenic claudication, radicular pain, radiculopathy, and peripheral entrapment (piriformis and thoracolumbar syndrome). Sensitization contains 2 subtypes, central and peripheral sensitization. The diagnostic checklist contains individual diagnostic categories containing evidence-based criteria, applicable examination procedures, and checkboxes to record clinical findings. The checklist organizes and displays evidence for or against a working diagnosis. The checklist may help to ensure needed information is obtained from a patient interview and exam in a variety of primary spine care settings (eg, medical, chiropractic).

CONCLUSION

The available evidence informs reasonable working diagnoses for many conditions causing or contributing to LBP. A practical diagnostic process including an exam and checklist is offered to guide clinical evaluation and demonstrate evidence for working diagnoses in clinical settings.

The Evolving Case Supporting Individualised Physiotherapy for Low Back Pain

Low-back pain (LBP) is one of the most burdensome health problems in the world. Guidelines recommend simple treatments such as advice that may result in suboptimal outcomes, particularly when applied to people with complex biopsychosocial barriers to recovery. Individualised physiotherapy has the potential of being more effective for people with LBP; however, there is limited evidence supporting this approach. A series of studies supporting the mechanisms underpinning and effectiveness of the Specific Treatment of Problems of the Spine (STOPS) approach to individualised physiotherapy have been published. The clinical and research implications of these findings are presented and discussed. Treatment based on the STOPS approach should also be considered as an approach to individualised physiotherapy in people with LBP.