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. DESIGN: 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. LIMITATIONS: 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.
RCT Entities:
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. DESIGN: 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. LIMITATIONS: 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: Patricia M Herman; Sarah E Edgington; Melony E Sorbero; Eric L Hurwitz; Christine M Goertz; Ian D Coulter Journal: Pain Physician Date: 2021-01 Impact factor: 4.965
Authors: Emma L Karran; James H McAuley; Adrian C Traeger; Susan L Hillier; Luzia Grabherr; Leslie N Russek; G Lorimer Moseley Journal: BMC Med Date: 2017-01-19 Impact factor: 8.775
Authors: Ian D Coulter; Cindy Crawford; Eric L Hurwitz; Howard Vernon; Raheleh Khorsan; Marika Suttorp Booth; Patricia M Herman Journal: Spine J Date: 2018-01-31 Impact factor: 4.166
Authors: David M Rowland; Amanda A Murphy; Hannah R Manik; Chris Y Lane; Deborah L Givens; Chad E Cook; Alessandra Narciso Garcia Journal: BMC Med Educ Date: 2020-07-11 Impact factor: 2.463