The diagnosis and classification of low back-related leg pain

Low back-related leg pain (LBLP) is clinically diagnosed as referred leg pain or sciatica. The clinical task of differentiating sciatica from referred leg pain can be challenging but is important for the purpose of treatment choices. There is currently no agreement on which clinical criteria best identify sciatica in clinical or research settings and the spectrum of clinical presentation in patients with LBLP is variable. This thesis aimed to identify diagnostic criteria for sciatica and explore and describe clusters of LBLP patients using cross-sectional data from 609 primary care LBLP consulters. A systematic literature search of LBLP classification systems showed very few systems specifically addressed LBLP classification. Within the systems, there was wide variation in definitions and clinical features of sciatica, with most systems based on clinical opinion. Reliability was merely fair (kappa = 0.35) amongst clinicians diagnosing sciatica but at higher levels of confidence in diagnosis (≥80%), reliability improved (kappa =0.68). Using high confidence clinical diagnosis as a reference standard, with and without confirmatory MRI findings, diagnostic models for sciatica were developed and compared. A simple scoring tool based on the best performing model was devised showing the probability of having sciatica based on results from five clinical items (subjective sensory changes, below knee pain, leg pain worse than back pain, positive neural tension, neurological deficit). Latent class analysis identified five classes of LBLP patients. One class was clearly a referred leg pain group, the other four classes seemed to represent sciatica with varying clinical profiles. This thesis provides a diagnostic tool for sciatica with potential application in clinical and research settings. It also reveals clusters of LBLP patients which could represent more homogenous groups amenable to different treatment approaches. This thesis has provided a strong basis for future work to further explore the clinical utility of the findings

Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain

Background Identification of sciatica may assist timely management but can be challenging in clinical practice. Diagnostic models to identify sciatica have mainly been developed in secondary care settings with conflicting reference standard selection. This study explores the challenges of reference standard selection and aims to ascertain which combination of clinical assessment items best identify sciatica in people seeking primary healthcare. Methods Data on 394 low back-related leg pain consulters were analysed. Potential sciatica indicators were seven clinical assessment items. Two reference standards were used: (i) high confidence sciatica clinical diagnosis; (ii) high confidence sciatica clinical diagnosis with confirmatory magnetic resonance imaging findings. Multivariable logistic regression models were produced for both reference standards. A tool predicting sciatica diagnosis in low back-related leg pain was derived. Latent class modelling explored the validity of the reference standard. Results Model (i) retained five items; model (ii) retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests and neurological deficit. Model (i) was well calibrated (p = 0.18), discrimination was area under the receiver operating characteristic curve (AUC) 0.95 (95% CI 0.93, 0.98). Model (ii) showed good discrimination (AUC 0.82; 0.78, 0.86) but poor calibration (p = 0.004). Bootstrapping revealed minimal overfitting in both models. Agreement between the two latent classes and clinical diagnosis groups defined by model (i) was substantial, and fair for model (ii). Conclusion Four clinical assessment items were common in both reference standard definitions of sciatica. A simple scoring tool for identifying sciatica was developed. These criteria could be used clinically and in research to improve accuracy of identification of this subgroup of back pain patients

Novel approach to characterising individuals with low back-related leg pain: cluster identification with latent class analysis and 12-month follow-up

Traditionally, low back-related leg pain (LBLP) is diagnosed clinically as referred leg pain or sciatica (nerve root involvement). However, within the spectrum of LBLP, we hypothesised that there may be other unrecognised patient subgroups. This study aimed to identify clusters of patients with LBLP using latent class analysis and describe their clinical course. The study population was 609 LBLP primary care consulters. Variables from clinical assessment were included in the latent class analysis. Characteristics of the statistically identified clusters were compared, and their clinical course over 1 year was described. A 5 cluster solution was optimal. Cluster 1 (n = 104) had mild leg pain severity and was considered to represent a referred leg pain group with no clinical signs, suggesting nerve root involvement (sciatica). Cluster 2 (n = 122), cluster 3 (n = 188), and cluster 4 (n = 69) had mild, moderate, and severe pain and disability, respectively, and response to clinical assessment items suggested categories of mild, moderate, and severe sciatica. Cluster 5 (n = 126) had high pain and disability, longer pain duration, and more comorbidities and was difficult to map to a clinical diagnosis. Most improvement for pain and disability was seen in the first 4 months for all clusters. At 12 months, the proportion of patients reporting recovery ranged from 27% for cluster 5 to 45% for cluster 2 (mild sciatica). This is the first study that empirically shows the variability in profile and clinical course of patients with LBLP including sciatica. More homogenous groups were identified, which could be considered in future clinical and research settings

Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study

Objective The clinical presentation and outcome of patients with back and leg pain in primary care are heterogeneous and may be better understood by identification of homogeneous and clinically meaningful subgroups. Subgroups of patients with different back pain trajectories have been identified, but little is known about the trajectories for patients with back‐related leg pain. This study sought to identify distinct leg pain trajectories, and baseline characteristics associated with membership of each group, in primary care patients. Methods Monthly data on leg pain intensity were collected over 12 months for 609 patients participating in a prospective cohort study of adult patients seeking healthcare for low back and leg pain including sciatica, of any duration and severity, from their general practitioner. Growth mixture modelling was used to identify clusters of patients with distinct leg pain trajectories. Trajectories were characterised using baseline demographic and clinical examination data. Multinomial logistic regression was used to predict latent class‐membership with a range of covariates. Results Four clusters were identified: (1) improving mild pain (58%), (2) persistent moderate pain (26%), (3) persistent severe pain (13%), and (4) improving severe pain (3%). Clusters showed statistically significant differences with a number of baseline characteristics. Conclusion Four trajectories of leg pain were identified. Clusters 1, 2 and 3 were generally comparable to back pain trajectories, while cluster 4, with major improvement in pain, is infrequently identified. Awareness of such distinct patient groups improves understanding of the course of leg pain and may provide a basis of classification for intervention

New insight to the characteristics and clinical course of clusters of patients with imaging confirmed disc-related sciatica.

BACKGROUND: Referral to secondary care is common for a considerable proportion of patients with persistent sciatica symptoms. It is unclear if information from clinical assessment can further identify distinct subgroups of disc-related sciatica, with perhaps different clinical courses. AIMS: This study aims to identify and describe clusters of imaging confirmed disc-related sciatica patients using latent class analysis, and compare their clinical course. METHODS: The study population were 466 patients with disc-related sciatica. Variables from clinical assessment were included in the analysis. Characteristics of the identified clusters were described and their clinical course over two years, was compared. RESULTS: A four cluster solution was optimal. Cluster 1 (n=110) had mild back and leg pain; cluster 2 (n=59) had moderate back and leg pain, cluster 3 (n=158) had mild back pain and severe leg pain; cluster 4 (n=139) had severe back and leg pain. Patients in cluster 4 had the most severe profile in terms of disability, distress and comorbidity and the lowest reported global change and the smallest proportion of patients with a successful outcome at two years. Of the 135 patients who underwent surgery, 42% and 41% were in clusters 3 and 4 respectively. CONCLUSIONS: Using a strict diagnosis of sciatica, this work identified four clusters of patients primarily differentiated by back and leg pain severity. Patients with severe back and leg pain had the most severe profile at baseline and follow-up irrespective of intervention. This simple classification system may be useful when considering prognosis and management with sciatica patients. This article is protected by copyright. All rights reserved

Self‐reported prognostic factors in adults reporting neck or low back pain: An umbrella review

Background: Numerous systematic reviews have attempted to synthesize evidence on prognostic factors for predicting future outcomes such as pain, disability and return‐to‐work/work absence in neck and low back pain populations. Databases and datatreatment: An umbrella review of systematic reviews was conducted to summarize the magnitude and quality of the evidence for each prognostic factor investigated. Searches were limited to the last 10 years (2008‐11th April 2018, updated 28th September 2020). A two‐stage approach was undertaken: in stage one, data on prognostic factors was extracted from systematic reviews identified from the systematic search that met the inclusion criteria. Where a prognostic factor was investigated in ≥1 systematic review and where 50% or more of those reviews found an association between the prognostic factor and one of the outcomes of interest, it was taken forward to stage two. In stage two, additional information extracted included the strength of association found, consistency of effects and risk of bias. The GRADE approach was used to grade confidence in the evidence. Results: Stage one identified 41 reviews (90 prognostic factors), with 35 reviews (25 prognostic factors) taken forward to stage two. Seven prognostic factors (disability/activity limitation, mental health; pain intensity; pain severity; coping; expectation of outcome/recovery and fear‐avoidance) were judged as having moderate confidence for robust findings. Conclusions: Although there was conflicting evidence for the strength of association with outcome, these factors may be used for identifying vulnerable subgroups or people able to self‐manage. Further research can investigate the impact of using such prognostic information on treatment/referral decisions and patient outcomes

Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings

OBJECTIVE: The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings.METHODS: Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance.RESULTS: Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]).CONCLUSION: Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance.IMPACT: External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.</p

Factors associated with physiotherapists' preference for MRI in primary care patients with low back and leg pain

© 2018 Background: Criticisms about overuse of MRI in low back pain are well documented. Yet, with the exception of suspicion of serious pathology, little is known about factors that influence clinicians’ preference for magnetic resonance imaging (MRI) at first consultation. Objective: To explore factors associated with physiotherapists’ preference for MRI for patients consulting with benign low back and leg pain (LBLP) including sciatica. Design: Cross-sectional cohort study. Methods: Data were collected from 607 primary care LBLP patients participating in the ATLAS cohort study. Following clinical assessment, physiotherapists documented whether he/she wanted the patient to have an MRI. Factors potentially associated with physiotherapists’ preference for imaging were selected a priori from patient characteristics and clinical assessment findings. A mixed-effects logistic regression model examined the associations between these factors and physiotherapists’ preference for MRI. Results: Physiotherapists expressed a preference for MRI in 32% (196/607) of patients, of whom 22 did not have a clinical diagnosis of sciatica (radiculopathy). Factors associated with preference for MRI included; clinical diagnosis of sciatica (OR 4.23: 95% CI 2.29, 7.81), greater than 3 months pain duration (2.61: 1.58, 4.30), high pain intensity (1.24: 1.11, 1.37), patient's low expectation of improvement (2.40: 1.50, 3.83), physiotherapist's confidence in their diagnosis (1.19: 1.07, 1.33), with greater confidence associated with higher probability for MRI preference. Conclusion: A clinical diagnosis of sciatica and longer symptom duration were most strongly associated with physiotherapists’ preference for MRI. Given current best practice guidelines, these appear to be justifiable reasons for MRI preference at first consultation