title: Which prognostic factors for low back pain are generic predictors of outcome across a range of recovery domains?
abstract: BACKGROUND
  Recovery from low back pain (LBP) is multidimensional and requires the use of multiple-response (outcome) measures to fully reflect these many dimensions. Predictive prognostic variables that are present or stable in all or most predictive models that use different outcome measures could be considered "universal" prognostic variables.
  
  
  OBJECTIVE
  The aim of this study was to explore the potential of universal prognostic variables in predictive models for 4 different outcome measures in patients with mechanical LBP.
  
  
  METHODS
  Predictive modeling was performed using data extracted from a randomized controlled trial. Four prognostic models were created using backward stepwise deletion logistic, Poisson, and linear regression.
  
  
  METHODS
  Data were collected from 16 outpatient physical therapy facilities in 10 states. All 149 patients with LBP were treated with manual therapy and spine strengthening exercises until discharge. Four different measures of response were used: Oswestry Disability Index and Numeric Pain Rating Scale change scores, total visits, and report of rate of recovery.
  
  
  RESULTS
  The set of statistically significant predictors was dependent on the definition of response. All regression models were significant. Within both forms of the 4 models, meeting the clinical prediction rule for manipulation at baseline was present in all 4 models, whereas no irritability at baseline and diagnosis of sprains and strains were present in 2 of 4 of the predictive models.
  
  
  CONCLUSIONS
  The primary limitation is that this study evaluated only 4 of the multiple outcome measures that are pertinent for patients with LBP.
  
  
  CONCLUSIONS
  Meeting the clinical prediction rule was prognostic for all outcome measures and should be considered a universal prognostic predictor. Other predictive variables were dependent on the outcomes measure used in the predictive model.
authors.affiliation: Division of Physical Therapy, Walsh University, 2020 East Maple, North Canton, OH 44720, USA. ccook@walsh.edu
authors.forename: Chad E
authors.initials: CE
authors.lastname: Cook
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Learman
authors.forename: Bryan J
authors.initials: BJ
authors.lastname: O'Halloran
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Showalter
authors.forename: Vincent J
authors.initials: VJ
authors.lastname: Kabbaz
authors.forename: Adam P
authors.initials: AP
authors.lastname: Goode
authors.forename: Alexis A
authors.initials: AA
authors.lastname: Wright
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003661:Decision Support Techniques; D004185:Disability Evaluation; D005260:Female; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D017116:Low Back Pain / Q000503:physiopathology* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D026741:Physical Therapy Modalities; D016012:Poisson Distribution; D011237:Predictive Value of Tests; D011379:Prognosis; D055815:Young Adult