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 referen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science
2018
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| Online Access: | https://eprints.nottingham.ac.uk/51105/ |
| _version_ | 1848798417567875072 |
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| author | Stynes, Siobhán Konstantinou, Kika Ogollah, Reuben O. Hay, Elaine M. Dunn, Kate M. |
| author_facet | Stynes, Siobhán Konstantinou, Kika Ogollah, Reuben O. Hay, Elaine M. Dunn, Kate M. |
| author_sort | Stynes, Siobhán |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:19:26Z |
| format | Article |
| id | nottingham-51105 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:19:26Z |
| publishDate | 2018 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-511052018-04-13T18:11:58Z https://eprints.nottingham.ac.uk/51105/ Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain Stynes, Siobhán Konstantinou, Kika Ogollah, Reuben O. Hay, Elaine M. Dunn, Kate M. 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. Public Library of Science 2018-04-05 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/51105/1/Stynes%20et%20al%202018-%20Plos%20One.pdf Stynes, Siobhán, Konstantinou, Kika, Ogollah, Reuben O., Hay, Elaine M. and Dunn, Kate M. (2018) Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain. PLoS ONE, 13 (4). e0191852/1-e0191852/14. ISSN 1932-6203 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191852 doi:10.1371/journal.pone.0191852 doi:10.1371/journal.pone.0191852 |
| spellingShingle | Stynes, Siobhán Konstantinou, Kika Ogollah, Reuben O. Hay, Elaine M. Dunn, Kate M. Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| title | Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| title_full | Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| title_fullStr | Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| title_full_unstemmed | Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| title_short | Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| title_sort | clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain |
| url | https://eprints.nottingham.ac.uk/51105/ https://eprints.nottingham.ac.uk/51105/ https://eprints.nottingham.ac.uk/51105/ |