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BMC Musculoskelet Disord. 2019 Jul 4;20(1):313. doi: 10.1186/s12891-019-2686-x.

Subgrouping patients with sciatica in primary care for matched care pathways: development of a subgrouping algorithm.

Author information

1
Primary Care Centre Versus Arthritis, Research Institute for Primary Care & Health Sciences, Keele University, Staffordshire, ST5 5BG, UK. k.konstantinou@keele.ac.uk.
2
Haywood Hospital, Midlands Partnership NHS Foundation Trust, Stoke-on-Trent, Staffordshire, ST6 7AG, UK. k.konstantinou@keele.ac.uk.
3
Primary Care Centre Versus Arthritis, Research Institute for Primary Care & Health Sciences, Keele University, Staffordshire, ST5 5BG, UK.
4
Keele Clinical Trials Unit, Keele University, Staffordshire, ST5 5BG, UK.
5
Present address: Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, UK.
6
Department of Spine Surgery, University Hospital North Midlands, Royal Stoke University Hospital, Newcastle Rd, Stoke-on-Trent, ST4 6QG, UK.

Abstract

BACKGROUND:

Sciatica is a painful condition managed by a stepped care approach for most patients. Currently, there are no decision-making tools to guide matching care pathways for patients with sciatica without evidence of serious pathology, early in their presentation. This study sought to develop an algorithm to subgroup primary care patients with sciatica, for initial decision-making for matched care pathways, including fast-track referral to investigations and specialist spinal opinion.

METHODS:

This was an analysis of existing data from a UK NHS cohort study of patients consulting in primary care with sciatica (nā€‰=ā€‰429). Factors potentially associated with referral to specialist services, were identified from the literature and clinical opinion. Percentage of patients fast-tracked to specialists, sensitivity, specificity, positive and negative predictive values for identifying this subgroup, were calculated.

RESULTS:

The algorithm allocates patients to 1 of 3 groups, combining information about four clinical characteristics, and risk of poor prognosis (low, medium or high risk) in terms of pain-related persistent disability. Patients at low risk of poor prognosis, irrespective of clinical characteristics, are allocated to group 1. Patients at medium risk of poor prognosis who have all four clinical characteristics, and patients at high risk of poor prognosis with any three of the clinical characteristics, are allocated to group 3. The remainder are allocated to group 2. Sensitivity, specificity and positive predictive value of the algorithm for patient allocation to fast-track group 3, were 51, 73 and 22% respectively.

CONCLUSION:

We developed an algorithm to support clinical decisions regarding early referral for primary care patients with sciatica. Limitations of this study include the low positive predictive value and use of data from one cohort only. On-going research is investigating whether the use of this algorithm and the linked care pathways, leads to faster resolution of sciatica symptoms.

KEYWORDS:

Algorithm; Care pathway; Leg pain; Referral; Sciatica; Stratification

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