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Orthopaedic Proceedings
Vol. 104-B, Issue SUPP_9 | Pages 7 - 7
1 Oct 2022
Evans D Rushton A Bishop J Middlebrook N Barbero M Patel J Falla D
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Background

Serious traumatic injury is a leading cause of death and disability globally, with the majority of survivors developing chronic pain.

Methods

The aims of this study were to describe early predictors of poor long-term outcome for post-trauma pain. We conducted a prospective observational study, recruiting patients admitted to a Major Trauma Centre hospital in England within 14 days of their injuries, and followed them for 12 months. We defined a poor outcome as Chronic Pain Grade ≥ II and measured this at both 6-months and 12-months. A broad range of candidate predictors were used, including surrogates for pain mechanisms, quantitative sensory testing, and psychosocial factors. Univariate models were used to identify the strongest predictors of poor outcome, which were entered into multivariate models.


Orthopaedic Proceedings
Vol. 101-B, Issue SUPP_9 | Pages 25 - 25
1 Sep 2019
Williams F Palmer M Tsepilov Y Freidin M Boer C Yau M Evans D Gelemanovic A Bartz T Nethander M Arbeeva L Karssen L Neogi T Campbell A Mellstrom D Ohlsson C Marshall L Orwoll E Uitterlinden A Rotter J Lauc G Psaty B Karlsson M Lane N Jarvik G Polasek O Hochberg M Jordan J van Meurs J Jackson R Nielson C Mitchell B Smith B Hayward C Smith N Aulchenko Y Suri P
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Purpose

Back pain is the primary cause of disability worldwide yet surprisingly little is known of the underlying pathobiology. We conducted a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP). Adults of European ancestry from 15 cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and UK Biobank were studied.

Methods

CBP cases were defined as reporting back pain present for ≥3–6 months; non-cases were included as comparisons (“controls”). Each cohort conducted genotyping followed by imputation. GWAS used logistic regression with additive genetic effects adjusting for age, sex, study-specific covariates, and population substructure. Suggestive (p<5×10–7) & genome-wide significant (p<5×10–8) variants were carried forward for replication in an independent sample of UK Biobank participants. Discovery sample n = 158,025 individuals, including 29,531 CBP cases.


Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_2 | Pages 42 - 42
1 Feb 2018
Rushton A Evans D Middlebrook N Heneghan N Falla D
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Introduction

Pain is an expected and appropriate experience following traumatic musculoskeletal injury. By contrast, chronic pain and disability are unhelpful yet common sequelae of trauma-related injuries. Presently, the mechanisms that underlie the transition from acute to chronic disabling post-traumatic pain are not fully understood. The aim of this study is to identify prognostic factors for risk of developing chronic pain and disability following acute musculoskeletal trauma.

Methods

A prospective observational study will recruit two temporally staggered cohorts (n=250 each cohort; 10 cases per candidate predictor) of consecutive acute musculoskeletal trauma patients aged ≥16 years, who are emergency admissions into a Major Trauma Centre in the United Kingdom, with an episode inception defined as the traumatic event. The first cohort will identify prognostic factors to develop a screening tool to predict development of chronic and disabling pain, and the second will allow evaluation of the predictive performance of the tool (validation). The outcome being predicted is an individual's absolute risk of poor outcome measured at 6-months follow-up using the Chronic Pain Grade Scale (poor outcome ≥Grade II). Candidate predictors encompass the four primary mechanisms of pain: nociceptive (e.g. injury characteristics), neuropathic (e.g. painDETECT), inflammatory (biomarkers), and central hypersensitivity (e.g. quantitative sensory testing). Concurrently, patient-reported outcome measures will assess general health and psychosocial factors. Risk of poor outcome will be calculated using multiple variable regression analysis.