Evaluation of a Treatment-Based Classification Algorithm for Low Back Pain: A Cross-Sectional Study
Stanton, T. J., Fritz, J. M., Hancock, M. J., Latimer, J., Maher, C. G., Wand, B. M., & Parent, E. C. (2011). Evaluation of a Treatment-Based Classification Algorithm for Low Back Pain: A Cross-Sectional Study. Physical Therapy, 91 (4), 496-509.
Background: Several studies have investigated criteria for classifying patients with low back pain (LBP) into treatment-based subgroups. A comprehensive algorithm was created to translate these criteria into a clinical decision-making guide.
Objective: This study investigated the translation of the individual subgroup criteria into a comprehensive algorithm by studying the prevalence of patients meeting the criteria for each treatment subgroup and the reliability of the classification.
Design: This was a cross-sectional, observational study.
Methods: Two hundred fifty patients with acute or subacute LBP were recruited from the United States and Australia to participate in the study. Trained physical therapists performed standardized assessments on all participants. The researchers used these findings to classify participants into subgroups. Thirty-one participants were reassessed to determine interrater reliability of the algorithm decision.
Results: Based on individual subgroup criteria, 25.2% (95% confidence interval [CI]=19.8%–30.6%) of the participants did not meet the criteria for any subgroup, 49.6% (95% CI=43.4%–55.8%) of the participants met the criteria for only one subgroup, and 25.2% (95% CI=19.8%–30.6%) of the participants met the criteria for more than one subgroup. The most common combination of subgroups was manipulation + specific exercise (68.4% of the participants who met the criteria for 2 subgroups). Reliability of the algorithm decision was moderate (kappa=0.52, 95% CI=0.27–0.77, percentage of agreement=67%).
Limitations: Due to a relatively small patient sample, reliability estimates are somewhat imprecise.
Conclusions: These findings provide important clinical data to guide future research and revisions to the algorithm. The finding that 25% of the participants met the criteria for more than one subgroup has important implications for the sequencing of treatments in the algorithm. Likewise, the finding that 25% of the participants did not meet the criteria for any subgroup provides important information regarding potential revisions to the algorithm's bottom table (which guides unclear classifications). Reliability of the algorithm is sufficient for clinical use.
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