Brief pain re-assessment provided more accurate prognosis than baseline information for low-back or shoulder pain

Abstract

Background Research investigating prognosis in musculoskeletal pain conditions has only been moderately successful in predicting which patients are unlikely to recover. Clinical decision making could potentially be improved by combining information taken at baseline and re-consultation. Methods Data from four prospective clinical cohorts of adults presenting to UK and Dutch primary care with low-back or shoulder pain was analysed, assessing long-term disability at 6 or 12 months and including baseline and 4–6 week assessments of pain. Baseline versus short-term assessments of pain, and previously validated multivariable prediction models versus repeat assessment, were compared to assess predictive performance of long-term disability outcome. A hypothetical clinical scenario was explored which made efficient use of both baseline and repeated assessment to identify patients likely to have a poor prognosis and decide on further treatment. Results Short-term repeat assessment of pain was better than short-term change or baseline score at predicting long-term disability improvement across all cohorts. Short-term repeat assessment of pain was only slightly more predictive of long-term recovery (c-statistics 0.78, 95% CI 0.74 to 0.83 and 0.75, 95% CI 0.69 to 0.82) than a multivariable baseline prognostic model in the two cohorts presenting such a model (c-statistics 0.71, 95% CI 0.67 to 0.76 and 0.72, 95% CI 0.66 to 0.78). Combining optimal prediction at baseline using a multivariable prognostic model with short-term repeat assessment of pain in those with uncertain prognosis in a hypothetical clinical scenario resulted in reduction in the number of patients with an uncertain probability of recovery, thereby reducing the instances where patients may be inappropriately referred or reassured. Conclusions Incorporating short-term repeat assessment of pain into prognostic models could potentially optimise the clinical usefulness of prognostic information.

Publication DOI: https://doi.org/10.1186/s12891-017-1502-8
Divisions: College of Health & Life Sciences
Additional Information: © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Publication ISSN: 1471-2474
Last Modified: 15 Apr 2024 07:30
Date Deposited: 30 Jan 2019 16:21
Full Text Link:
Related URLs: http://bmcmuscu ... 2891-017-1502-8 (Publisher URL)
PURE Output Type: Article
Published Date: 2017-04-04
Accepted Date: 2017-03-24
Authors: Mansell, G. (ORCID Profile 0000-0002-5479-2678)
Jordan, K. P.
Peat, G. M.
Dunn, K. M.
Lasserson, D.
Kuijpers, T.
Swinkels-meewisse, I.
Van Der Windt, D. A. W. M.

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