• Stupar M
• Côté P
• Carroll LJ
• Brison RJ
• Boyle E
• Shearer HM
• Cassidy JD

• Affiliation / Canadian Memorial Chiropractic College (CMCC). Division of Graduate Education and Research
• Affiliation / Ontario Tech University. Faculty of Health Sciences
• Affiliation / University of Alberta
• Affiliation / University of Toronto. Dalla Lana School of Public Health. Division of Epidemiology
• Clinical Decision-Making
• Neck Pain
• Open Access Document
• Rehabilitation
• Whiplash Injuries

Objective: Few clinical prediction models are available to clinicians to predict the recovery of patients with post-collision neck pain and associated disorders. We aimed to develop evidence-based clinical prediction models to predict (1) self-reported recovery and (2) insurance claim closure from neck pain and associated disorders (NAD) caused or aggravated by a traffic collision.

Methods: The selection of potential predictors was informed by a systematic review of the literature. We used Cox regression to build models in an incident cohort of Saskatchewan adults (n = 4923). The models were internally validated using bootstrapping and replicated in participants from a randomized controlled trial conducted in Ontario (n = 340). We used C-statistics to describe predictive ability.

Results: Participants from both cohorts (Saskatchewan and Ontario) were similar at baseline. Our prediction model for self-reported recovery included prior traffic-related neck injury claim, expectation of recovery, age, percentage of body in pain, disability, neck pain intensity and headache intensity (C = 0.643; 95% CI 0.634–0.653). The prediction model for claim closure included prior traffic-related neck injury claim, expectation of recovery, age, percentage of body in pain, disability, neck pain intensity, headache intensity and depressive symptoms (C = 0.637; 95% CI 0.629–0.648).

Conclusions: We developed prediction models for the recovery and claim closure of NAD caused or aggravated by a traffic collision. Future research needs to focus on improving the predictive ability of the models.

Author keywords: Neck pain - Whiplash - Clinical prediction model - Rehabilitation - Disability - Health recovery

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