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

Stynes, Siobhan; Konstantinou, Kika; Ogollah, Reuben; Hay, Elaine; Dunn, Kate

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


Authors

Kika Konstantinou

Reuben Ogollah



Abstract

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.

Journal Article Type Article
Acceptance Date Jan 12, 2018
Publication Date Apr 5, 2018
Publicly Available Date Mar 28, 2024
Journal PLoS One
Print ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Pages 1 -14
DOI https://doi.org/10.1371/journal.pone.0191852
Publisher URL http://doi.org/10.1371/journal.pone.0191852

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