Evidence-based precision in TCM

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Background: The quest for personalized management of acute asthma exacerbations continues. Traditional Chinese Medicine (TCM) offers a rich repository of empirical knowledge, but its pattern-driven recommendations lack systematic, data-driven validation. Methods: We performed a systematic data mining analysis of TCM clinical literature on acupoint sticking therapy for acute asthma. Latent structure analysis and frequent itemset analysis were applied to 40 studies involving 2107 patients to objectively identify core syndrome patterns and their associated acupoint combinations. Results: We objectively delineated 2 predominant and clinically distinct syndrome patterns. For the pattern characterized by dyspnea with chills and clear sputum (analogous to “exterior-cold), the combination of Feishu (BL13), Dingchuan (EX-B1), and Shenshu (BL23) was most strongly associated. For the pattern with dyspnea, yellow sputum, and fever (analogous to “phlegm-heat), the combination shifted to Feishu (BL13), Dingchuan (EX-B1), and Dazhui (GV14). Conclusion: This computational study transforms qualitative TCM experience into quantifiable, syndrome-specific treatment rules. It provides an evidence-based framework for acupoint selection in acute asthma and demonstrates a novel methodology for extracting actionable clinical knowledge from historical texts. These findings offer a foundation for designing targeted clinical trials to validate personalized TCM interventions.