A clinical return-to-work rule for patients with back pain

Background

Tools for early identification of workers with back pain who are at high risk of adverse occupational outcome would help concentrate clinical attention on the patients who need it most, while helping reduce unnecessary interventions (and costs) among the others. This study was conducted to develop and validate clinical rules to predict the 2-year work disability status of people consulting for nonspecific back pain in primary care settings.

Methods

This was a 2-year prospective cohort study conducted in 7 primary care settings in the Quebec City area. The study enrolled 1007 workers (participation, 68.4% of potential participants expected to be eligible) aged 18–64 years who consulted for nonspecific back pain associated with at least 1 day''s absence from work. The majority (86%) completed 5 telephone interviews documenting a large array of variables. Clinical information was abstracted from the medical files. The outcome measure was “return to work in good health” at 2 years, a variable that combined patients'' occupational status, functional limitations and recurrences of work absence. Predictive models of 2-year outcome were developed with a recursive partitioning approach on a 40% random sample of our study subjects, then validated on the rest.

Results

The best predictive model included 7 baseline variables (patient''s recovery expectations, radiating pain, previous back surgery, pain intensity, frequent change of position because of back pain, irritability and bad temper, and difficulty sleeping) and was particularly efficient at identifying patients with no adverse occupational outcome (negative predictive value 78%– 94%).

Interpretation

A clinical prediction rule accurately identified a large proportion of workers with back pain consulting in a primary care setting who were at a low risk of an adverse occupational outcome.Since the 1950s, back pain has taken on the proportions of a veritable epidemic, counting now among the 5 most frequent reasons for visits to physicians'' offices in North America^1,2,3 and ranking sixth among health problems generating the highest direct medical costs.⁴ Because of its high incidence and associated expense, effective intervention for back pain has great potential for improving population health and for freeing up extensive societal resources.So-called red flags to identify pain that is specific (i.e., pain in the back originating from tumours, fractures, infections, cauda equina syndrome, visceral pain and systemic disease)⁵ account for about 3% of all cases of back pain.⁶ The overwhelming majority of back-pain problems are thus nonspecific. One important feature of nonspecific back pain among workers is that a small proportion of cases (< 10%) accounts for most of the costs (> 70%).^{7,8,9,10,11,12,13,14} This fact has led investigators to focus on the early identification of patients who are at higher risk of disability, so that specialized interventions can be provided earlier, whereas other patients can be expected to recover with conservative care.^{9,15,16,17,18,19,20,21,22,23,24,25} Although this goal has become much sought-after in back-pain research, most available studies in this area have 3 methodological problems:

• Potential predictors are often limited to administrative or clinical data, whereas it is clear that back pain is a multidimensional health problem.
• The outcome variable is most often a 1-point dichotomous measure of return to work, time off work or duration of compensation, although some authors have warned against the use of first return to work as a measure of recovery. Baldwin and colleagues,²⁶ for instance, point out that first return to work is frequently followed by recurrences of work absence.
• Most published prediction rules developed for back pain have not been successfully validated on any additional samples of patients.

Our study aimed to build a simple predictive tool that could be used by primary care physicians to identify workers with nonspecific back pain who are at higher risk of long-term adverse occupational outcomes, and then to validate this tool on a fresh sample of subjects.