• Spine · Jul 2009

    Discriminating healthy controls and two clinical subgroups of nonspecific chronic low back pain patients using trunk muscle activation and lumbosacral kinematics of postures and movements: a statistical classification model.

    • Wim Dankaerts, Peter O'Sullivan, Angus Burnett, Leon Straker, Paul Davey, and Ritu Gupta.
    • Musculoskeletal Unit, Department of Rehabilitation Sciences, Faculty of Kinesiology and Rehabilitation Sciences K.U. Leuven, Leuven, Belgium. wim.dankaerts@faber.kuleuven.be
    • Spine. 2009 Jul 1;34(15):1610-8.

    Study DesignStatistical Classification Model for nonspecific chronic low back pain (NS-CLBP) patients and controls based on parameters of motor control.ObjectiveDevelop a Statistical Classification Model to discriminate between 2 subgroups of NS-CLBP (Flexion Pattern [FP] and Active Extension Pattern [AEP]) and a control group using biomechanical variables quantifying parameters of motor control.Summary Of Background DataIt has been well documented that many CLBP patients have motor control impairments of their lumbar spine. O'Sullivan proposed a mechanism-based classification system for NS-CLBP with motor control impairments based on a comprehensive subjective and physical examination to establish the relationship between pain provocation and spinal motor control. For the FP and AEP s, 2 groups defined by O'Sullivan and under investigation is this study, the motor control impairment is considered to be the mechanism maintaining their CLBP. No previous studies have used a Statistical Model with measurements of motor control impairment to subclassify NS-CLBP patients.MethodsThirty-three NS-CLBP patients (20 FP and 13 AEP) and 34 asymptomatic subjects had synchronized lumbosacral kinematics and trunk muscle activation recorded during commonly reported aggravating postures and movements. Biomechanical variables were quantified and a Statistical Classification Model was developed.ResultsThe Statistical Model used 5 kinematic and 2 electromyography variables. The model correctly classified 96.4% of cases.ConclusionSelected biomechanical variables were predictors for subgroup membership and were able to discriminate the 3 subgroups. This study adds further support toward the validation of the proposed classification system.

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