Do prognostic variables predict a set of outcomes for patients with chronic low back pain: a long-term follow-up secondary analysis of a randomized control trial.
Alessandra Narciso GarciaLeonardo Oliveira Pena CostaMark HancockChad CookPublished in: The Journal of manual & manipulative therapy (2019)
Objective: The objective was to explore for universal prognostic variables, or predictors, across three different outcome measures in patients with chronic low back pain (LBP). We hypothesized that selected prognostic variables would be 'universal' prognostic variables, regardless of the outcome measures used. Methods: This study was a secondary analysis of data from a previous randomized controlled trial comparing the McKenzie treatment approach with placebo in patients with chronic LBP. Ten baseline prognostic variables were explored in predictive models for three outcomes: pain intensity, disability, and global perceived effect, at 6 and 12 months. Predictive models were created using backward stepwise logistic and linear multivariate regression analyses. Results: Several predictors were present including age, expectancy of improvement, global perceived effect; however, we only identified baseline disability as a universal predictor of outcomes at 6 months. The second most represented universal predictor was baseline pain intensity for outcomes at 12 months. Discussion: Only two predictors demonstrated an association with more than one outcome measure. High baseline disability predicts multidimensional outcome measures at 6 months in patients with chronic LBP while baseline pain intensity can best predict the outcome at 12 months. Nevertheless, other predictors seem to be unique to the outcome used. Level of evidence: 2c.
Keyphrases
- randomized controlled trial
- chronic pain
- multiple sclerosis
- pain management
- neuropathic pain
- depressive symptoms
- physical activity
- social support
- systematic review
- type diabetes
- electronic health record
- spinal cord injury
- adipose tissue
- deep learning
- data analysis
- big data
- machine learning
- smoking cessation
- replacement therapy