Aims

To determine the value of scoliosis surgery, it is necessary to evaluate outcomes in domains that matter to patients. Since randomized trials on adolescent idiopathic scoliosis (AIS) are scarce, prospective cohort studies with comparable outcome measures are important. To enhance comparison, a core set of patient-related outcome measures is available. The aim of this study was to evaluate the outcomes of AIS fusion surgery at two-year follow-up using the core outcomes set.

Background. Low back-related leg pain (LBLP) is clinically diagnosed as referred leg pain or sciatica. Within the spectrum of LBLP there may be unrecognised subgroups of patients. This study aimed to identify and describe clusters of LBLP patients using latent class analysis (LCA). Methods. The study population were 609 LBLP primary care consulters. Variables from clinical assessment were included in the LCA. Characteristics of the statistically identified clusters were described and compared to the clinically defined groups of LBLP patients. Results. A five cluster solution was optimal. Cluster one (n=104) had mild leg pain severity, no clinical signs suggestive of sciatica and more anxiety. Cluster two (n=122), three (n=188) and four (n=69) represented mild, moderate and severe sciatica in terms of response to clinical assessment items, pain severity and impact on function. Cluster five (n=126) was more difficult to define based on response to clinical assessment items (below knee pain and possible neural tension) and had a similar severe profile to cluster four in terms of high pain, disability, psychosocial factors, work impact and risk of poor outcome; but had longer duration pain and more comorbidities. Cluster three consistently mirrored the profile of the overall group of patients with a clinical diagnosis of sciatica. Cluster one mirrored the referred leg pain group. Conclusion. This is the first study that used LCA to classify LBLP patients, including sciatica. These clusters could represent more homogenous groups that may require different treatment approaches. Further work will describe the clinical course and longer term outcomes of these clusters. No conflicts of interest. S Stynes is supported by an NIHR/CNO Clinical Doctoral Research Fellowship (CDRF-2010-055). Dr Konstantinou is supported by a HEFCE/NIHR Senior Clinical Lectureship. Professor Hay is a NIHR Senior Investigator

Background:. Identification of nerve root involvement (NRI) in patients with low back-related leg pain (LBLP) can be challenging. Diagnostic models have mainly been developed in secondary care with conflicting reference standards and predictor selection. This study aims to ascertain which cluster of items from clinical assessment best identify NRI in primary care consulters with LBLP. Methods:. Cross-sectional data on 395 LBLP consulters were analysed. Potential NRI indicators were seven clinical assessment items. Two definitions of NRI formed the reference standards: (i) high confidence (≥80%) NRI clinical diagnosis (ii) high confidence (≥80%) NRI clinical diagnosis with confirmatory magnetic resonance imaging (MRI) findings. Multivariable logistic regression models were constructed and compared for both reference standards. Model performances were summarised using the Hosmer-Lemeshow statistic and area under the curve (AUC). Bootstrapping assessed internal validity. Results:. NRI clinical diagnosis model retained five items. The model with MRI in the reference standard retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests, neurological deficit (myotome, reflex or sensory). NRI clinical diagnosis model was well calibrated (p=0.17) and discrimination was AUC 0.96 (95%CI: 0.93, 0.98). Performance measures for clinical diagnosis plus confirmatory MRI model showed good discrimination (AUC 0.83, 95% CI: 0.78, 0.86) but poor calibration (p=0.01). Bootstrapping revealed minimal overfitting in both models. Conclusion:. A cluster of items identified NRI in LBLP consulters. These criteria could be used clinically and in research to improve accuracy of identification and homogeneity of this subgroup of low back pain patients