Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments

• Published in: The journal of orthopaedic and sports physical therapy 2016-09, Vol.46 (9), p.726-741
• Main Authors: Werneke, Mark W, Edmond, Susan, Deutscher, Daniel, Ward, Jason, Grigsby, David, Young, Michelle, McGill, Troy, McClenahan, Brian, Weinberg, Jon, Davidow, Amy L
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Age Factors; Aged; Exercise Therapy - psychology; Fear; Female; Health Knowledge, Attitudes, Practice; Humans; Likelihood Functions; Logistic Models; Low Back Pain - classification; Low Back Pain - psychology; Male; Middle Aged; Models, Theoretical; Pain Measurement - psychology; Physical Therapy Modalities; Retrospective Studies; Risk Adjustment - classification; Risk Adjustment - statistics & numerical data; Spinal Diseases - classification; Spinal Diseases - psychology; Surveys and Questionnaires; Symptom Assessment - methods; Syndrome; Treatment Outcome; Young Adult
• ISSN: 0190-6011; 1938-1344
• DOI: 10.2519/jospt.2016.6266

Study Design Retrospective cohort. Background Patient-classification subgroupings may be important prognostic factors explaining outcomes. Objectives To determine effects of adding classification variables (McKenzie syndrome and pain patterns, including centralization and directional preference; Symptom Checklist Back Pain Prediction Model [SCL BPPM]; and the Fear-Avoidance Beliefs Questionnaire subscales of work and physical activity) to a baseline risk-adjusted model predicting functional status (FS) outcomes. Methods Consecutive patients completed a battery of questionnaires that gathered information on 11 risk-adjustment variables. Physical therapists trained in Mechanical Diagnosis and Therapy methods classified each patient by McKenzie syndromes and pain pattern. Functional status was assessed at discharge by patient-reported outcomes. Only patients with complete data were included. Risk of selection bias was assessed. Prediction of discharge FS was assessed using linear stepwise regression models, allowing 13 variables to enter the model. Significant variables were retained in subsequent models. Model power (R(2)) and beta coefficients for model variables were estimated. Results Two thousand sixty-six patients with lumbar impairments were evaluated. Of those, 994 (48%), 10 (