Pattern diagnosis is an integral aspect of Chinese medicine (CM). CM differentiates biomedical diseases into patterns, based upon the patient's symptoms and signs. Pattern identification (PI) is used to diagnose, direct the treatment principle and determine the treatment protocol. Most CM research has used fixed formula treatments for Western-defined diseases with outcomes measured using objective biomedical markers. This article presents an innovative method used in a randomised controlled pilot study using acupuncture for participants with hepatitis C virus. Each participant's CM patterns were identified and quantified at baseline which directed the treatment protocol for the treatment group. Data identified that while each participant expressed different patterns at baseline all participants displayed multiple patterns. Six patterns showed some expression by all 16 participants; Liver (Gan) yin vacuity expressing a group aggregate mean percentage of 47.2, binding depression of Liver qi 46.9, and Liver Kidney (Shen) yin vacuity 45.1. Further sub category gender grouping revealed that pattern ranking changed with gender; Liver yin vacuity (male 53.4%, female 51.93%), binding depression of Liver qi (male 50.0%, female 42.86%) and Liver Kidney yin vacuity (male 42.9%, female 47.96%). The quantification of CM patterns described in this article permitted statistical evaluation of presenting CM patterns. Although this methodology is in its infancy it may have potential use in the integration of PI with rigorous evidence based clinical research. Biomedical markers often do not relate to symptom/signs and therefore this innovative measure may offer an additional CM evaluation methodology and further CM PI understanding.
Acupuncture Therapy ; Adult ; Biomedical Research ; Evidence-Based Medicine ; Female ; Hepacivirus ; physiology ; Hepatitis C ; therapy ; virology ; Humans ; Male ; Medicine, Chinese Traditional ; methods ; Middle Aged ; Pilot Projects ; Practice Patterns, Physicians' ; Randomized Controlled Trials as Topic