S A Ferguson1, P Gupta, W S Marras, C Heaney. 1. Biodynamics Laboratory, The Ohio State University, 210 Baker Systems, 1971 Neil Avenue, Columbus, OH 43210, USA. ferguson.4@osu.edu
Abstract
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 pain outcomes based on the quantification of initial conditions. STUDY DESIGN/ SETTING: This was a prospective study where patients were enrolled within the first month of low back pain symptoms and evaluated for 3 months. Patients were recruited from several primary care facilities. PATIENT SAMPLE: Thirty-two patients with local low back pain 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 patients 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.
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 pain outcomes based on the quantification of initial conditions. STUDY DESIGN/ SETTING: This was a prospective study where patients were enrolled within the first month of low back pain symptoms and evaluated for 3 months. Patients were recruited from several primary care facilities. PATIENT SAMPLE: Thirty-two patients with local low back pain 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 patients 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.
Authors: Steven J Kamper; Tasha R Stanton; Christopher M Williams; Christopher G Maher; Julia M Hush Journal: Eur Spine J Date: 2010-06-16 Impact factor: 3.134
Authors: Luciola da C Menezes Costa; Christopher G Maher; Mark J Hancock; James H McAuley; Robert D Herbert; Leonardo O P Costa Journal: CMAJ Date: 2012-05-14 Impact factor: 8.262