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Evid Based Complement Alternat Med. 2015;2015:869894. doi: 10.1155/2015/869894. Epub 2015 May 18.

Extraction of Clinical Indicators That Are Associated with the Heat/Nonheat and Excess/Deficiency Patterns in Pattern Identifications for Stroke.

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Medical Research Division, Brain Disease Research Center, Korea Institute of Oriental Medicine, 1672 Yuseongdae-ro, Yuseong-gu, Daejeon 305-811, Republic of Korea.
Korean National Rehabilitation Center, Seoul, Republic of Korea.
University College of Health Sciences, Institute of Acupuncture, 0855 Oslo, Norway ; National Research Center in Complementary and Alternative Medicine (NAFKAM), Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
University College of Health Sciences, Institute of Acupuncture, 0855 Oslo, Norway.


The aim of this study is to extract indicators that are associated with the heat/nonheat and excess/deficiency patterns in stroke pattern identification through the large-scale analysis of clinical data. Two experts, who had more than three years of clinical experience with stroke, independently performed the pattern identification. We analyzed indicators of clinical data with two doctors' concurrent diagnoses on the patient's pattern identification. To verify heat/nonheat and excess/deficiency patterns, which are the basic elements of pattern identification, we grouped 960 patients diagnosed as the fire-heat pattern, the Yin deficiency pattern, and the Qi deficiency pattern in to two groups, the heat/nonheat group and the excess/deficiency group. We then extracted significant indicators using univariate and multivariate analysis. As a result of the comparison of 65 indicators, we were able to extract 10 indicators for the heat pattern, 6 for the nonheat pattern, 9 for the excess pattern, and 10 for the deficiency pattern. Extracted indicators in this study can be used for pattern identification in the context of stroke. These are positive indicators from large-scale clinical studies and are greatly expected to be crucial discriminant indicators in individual pattern identification henceforth.

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