Abstract
An objective method to evaluate patient suitability for cognitive behavioral therapy (CBT) for chronic low back pain (LBP) is currently lacking. Inappropriate application can result in prolonged hospital visits and increased medical costs. Therefore, identifying an objective biomarker for evaluating suitability is crucial. This study focused on electroencephalogram (EEG) complexity as a potential biomarker for evaluating CBT suitability for chronic LBP, assessing its discriminative ability and identifying factors that impede treatment. Complexity was analyzed as multiscale fuzzy sample entropy (MFSE). Fifty patients with suspected psychosocial factors causing LBP along with 20 healthy volunteers were included. The analysis included 25 responders and 25 non-responders for CBT. MFSE showed significant effects of scale factor [F(19,171) = 14.82, p < 0.01, partial η(2) = 0.622] and interaction between group and scale factor [F(38,171) = 7.34, p < 0.01, partial η(2) = 0.620]. The low-frequency band MFSE score had an odds ratio of 10.768 (95% confidence interval: 8.263-10.044, p < 0.001). The low-frequency band showed a high discriminative ability (area under the curve: 0.825), with a cut-off value of 1.25. The low-frequency FMSE is a superior biomarker for predicting suitability for CBT. This method can quickly evaluate suitability, reducing the burden on medical professionals and patients, and lowering medical costs.