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

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

Abstract

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.

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.

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).

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. Figures

Citation: Stynes S, Konstantinou K, Ogollah R, Hay EM, Dunn KM (2018) Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain. PLoS ONE 13(4): e0191852. https://doi.org/10.1371/journal.pone.0191852

Editor: Mikko Juhani Lammi, University of Umeå, SWEDEN

Received: January 23, 2017; Accepted: January 12, 2018; Published: April 5, 2018

Copyright: © 2018 Stynes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The anonymised data underlying this study are available in the paper and its Supporting Information files. The data used in this analysis are owned by The Arthritis Research UK Primary Care Centre, to which further queries related to data access may be submitted. The Arthritis Research UK Primary Care Centre has established data sharing arrangements to support joint publications and other research collaborations. Applications for access to anonymised data from our research databases are reviewed by the Centre’s Data Custodian and Academic Proposal (DCAP) Committee, and a decision regarding access to the data is made subject to the National Research Ethics Service ethical approval first provided for the study and to new analysis being proposed. Further information on our data sharing procedures can be found on the Centre’s website ( http://www.keele.ac.uk/pchs/publications/datasharingresources/ ) or by emailing the Centre’s data manager ( primarycare.datasharing@keele.ac.uk ).

Funding: SS was supported by an National Institute for Health Research/Chief Nursing Officer Clinical Doctoral Research Fellowship (CDRF-2010-055). KK was supported by a Higher Education Funding Council for England/National Institute for Health Research Senior Clinical Lectureship. EMH is an National Institute for Health Research Senior Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

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