New Model Correctly Classifies Low Back Pain Patients
High correlation between results of regression equation and Oswestry Disability Index scale
WEDNESDAY, July 13 (HealthDay News) -- A new logistic regression model using kinematic analysis variables from two everyday tasks can correctly classify 97.3 percent of patients with nonspecific low back pain (LBP), according to a study published in the July 15 issue of Spine.
Daniel Sánchez-Zuriaga, M.D., Ph.D., from Universitat de València in Spain, and colleagues developed a logistic regression model using kinematic analysis variables to distinguish between LBP patients and controls, and to obtain objective parameters for LBP functional assessment. The study enrolled 16 controls and 39 patients with LBP who performed a sit-to-stand task and lifted three weights from a standing position. The relative position of the lower limb and the cervical, thoracic, lumbar, and sacroiliac regions, as well as the vertical force exerted, were recorded. To verify the validity of the procedure for the measurement of functional disability, binary logistic regression analyses were computed and results compared to the results of the Oswestry Disability Index Scale.
The investigators found that the reliability of the parameters was good. Two variables were used in the selected regression model, and 97.3 percent of patients were accurately categorized. The results of this regression equation had high correlations with the Oswestry Disability Index Scale.
"It is possible to distinguish LBP patients from healthy subjects by means of the biomechanical analysis of everyday tasks. This kind of analysis can produce objective and reliable indexes about the patients' degree of functional impairment," the authors write.