Clinical traditional medicine and pharmacology最新文献

{"title":"Baseline predictors of responders to auricular point acupressure in chronic low back pain.","authors":"Nada Lukkahatai, Wanqi Chen, Jennifer Kawi, Hulin Wu, Claudia M Campbell, Johannes Thrul, Xinran Huang, Paul Christo, Constance M Johnson","doi":"10.1016/j.ctmp.2025.200215","DOIUrl":"10.1016/j.ctmp.2025.200215","url":null,"abstract":"<p><strong>Background: </strong>Chronic low back pain (cLBP) is a major cause of disability, with varied patient responses to treatments. Auricular point acupressure (APA) has shown potential as a non-pharmacological intervention, but individual responses may differ significantly.</p><p><strong>Objective: </strong>This study aimed to determine the predictability of baseline characteristics, including functional disability, symptom severity, and treatment expectancy, on clinically significant responses to APA in reducing pain and improving function.</p><p><strong>Methods: </strong>A secondary analysis was performed using data from a randomized controlled trial with 263 cLBP patients. Participants were randomly assigned to targeted APA (T-APA), non-targeted APA (NT-APA), or to a control group. APA responders were defined as those with at least a 1.5-point reduction in pain intensity or a 2.5-point improvement in the Roland-Morris Disability Questionnaire (RMDQ). Predictors of response were assessed using logistic regression and machine learning models, including the Random Forest and Support Vector Machine (SVM).</p><p><strong>Results: </strong>Baseline pain, physical function, sleep disturbance, and treatment expectancy were key predictors. The Random Forest model had the highest accuracy for T-APA; however, logistic regression performed best in NT-APA. SVM was most accurate in the control group, with predictive accuracy varying by group (AUC 60.9%-80%). The Least Absolute Shrinkage and Selection Operator (LASSO) method was found to be overly aggressive, often eliminating important variables.</p><p><strong>Conclusion: </strong>This study highlights the variability in APA treatment responses for cLBP. While predictive models provide useful insights, further research with larger datasets is needed to improve prediction accuracy and generalizability, enhancing personalized treatment approaches for cLBP.</p>","PeriodicalId":520843,"journal":{"name":"Clinical traditional medicine and pharmacology","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}