Predicting recovery using continuous low back XXXX outcome measures.

@entity1 @entity158
BACKGROUND CONTEXT: There is a lack of research evaluating multiple follow-up visits, specifically when using continuous outcome measures. Continuous outcome measures with several follow-up assessments would allow us to evaluate rate of recovery. PURPOSE: To predict low back @entity158 outcomes based on the quantification of initial conditions. STUDY DESIGN/SETTING: This was a prospective study where @entity1 were enrolled within the first month of low back @entity158 symptoms and evaluated for 3 months. @entity1 were recruited from several primary care facilities. @entity1 SAMPLE: Thirty-two @entity1 with local low back @entity158 symptoms were recruited for the study. OUTCOME MEASURES: There were four major outcome measures, including functional performance probability, symptom intensity, impairment of activities of daily living, and a summary outcome measure. METHODS: Regression models were constructed using the initial conditions, including psychological, psychosocial, physical workplace, and personal factors, to predict the rate of recovery for each outcome measure. RESULTS: Twenty-eight @entity1 completed the study. The r2 value for the rate of recovery regression models were 0.77 symptom intensity prediction, 0.85 activities of daily living prediction, 0.87 functional performance probability prediction, and 0.96 summary outcome measure prediction. Two functional performance patterns of recovery were found, including a steady improvement and a large jump in improvement. A discriminant function model identified the pattern of recovery in 91% of cases given initial conditions. CONCLUSIONS: Continuous outcome measures can be accurately predicted given the initial conditions.
