Fundamental study for the Standardization and Objectification of Pattern Identification in traditional Korean medicine for Stroke (SOPI-Stroke): An overview of the second and third stages

Bibliometric analysis of trends and issues in traditional medicine for stroke research: 2004-2018

BACKGROUND

Stroke is a major cause of death and disability worldwide. Over the years, traditional medicines for stroke treatment have undergone tremendous progress, but few bibliometric studies have been performed. This study explored the trends and issues relating to the application of traditional medicine in stroke research.

METHODS

A bibliometric search was performed in the Web of Science Core Collection database to identify studies that investigated the application of traditional medicine in stroke management. CiteSpace VI and Excel 2016 were used to analyze information from the retrieved studies. Activity index and attractive index were used to explore the worldwide development modes.

RESULTS

A total of 1083 English articles published between 2004 and 2018 were identified. Over the last 15 years, the developments in research occurred in three geographic clusters. The development modes were investigated and classified into 4 categories. In mainland China, the number and impact of research showed an increasing trend over the study period. The United States played a leading role in this topic. Three clusters of institutes and the majority of authors mainly came from South Korea, Taiwan and mainland China. Reperfusion injury and angiogenesis were identified as the potential topics likely to dominate future research in this field.

CONCLUSION

The progress of studies on traditional medicine for stroke could be explained by the global attention to traditional medicine, the geospatial proximity for research collabration, and the increasing resources invested. Based on a large amount of existing research, researchers engaged in this topic should objectively consider the influential studies to identify and solve the common issues worldwide.

Perception of cold and heat pattern identification in diseases: a survey of Korean medicine doctors

Background

This study aimed to investigate the extent to which Korean Medicine doctors consider cold and heat pattern identification when prescribing herbal treatment for a disease.

Methods

A survey was sent by e-mail to 15,841 members of the Association of Korean Medicine for whom member information was registered. Of these, 699 (4.4%) members participated in the survey. The survey included questions regarding the frequency of use of cold and heat pattern identification in deciding a herbal treatment prescription, the diseases for which cold and heat pattern identification-related herbal treatment was most efficacious, the type of herbal treatment prescribed, and the duration of the treatment.

Results

Of the 699 respondents, 591 (84.5%) reported that they considered cold and heat when prescribing herbal treatment. The diseases for which consideration of cold and heat patterns was effective were, in order, menopausal disorder (124, 18.3%), chronic rhinitis (98, 14.5%), dyspepsia (94, 13.9%), hwa-byung (92, 13.6%), diarrhea (83, 12.3%), dysmenorrhea (61, 9.0%), headache (59, 8.7%), inflammation in the digestive tract (58, 8.6%), coldness in hands and feet (58, 8.6%), and atopic dermatitis (55, 8.1%). The typical treatment duration differed widely for different diseases: atopic dermatitis was most frequently treated for >2 months (38, 34.5%), whereas diarrhea was most frequently treated for ≤ 10 days (73, 43.6%).

Conclusion

These findings indicate that cold and heat pattern identification is a useful tool employed by Korean Medicine doctors. This study may provide a basis for clinical research investigating the effect of pattern identification-based treatment of diseases.

Stroke in Traditional Korean Medicine: A Nine-Year Multicentre Community-Based Study in South Korea

In Korea, patients with stroke are commonly treated using traditional Korean medicine (TKM). The aim of this study was to provide information on the clinical characteristics of the pattern identification (PI) of stroke used in TKM. Stroke patients admitted to 15 TKM university hospitals from April 2005 through December 2013 were evaluated. The measured variables included the following factors as they related to the PI: (a) stroke etiology; (b) distribution of symptoms/signs; (c) physical characteristics and lifestyle parameters; (d) medical history; and (e) stroke-related laboratory results. Among 4912 stroke patients, 3466 patients received the same PI by two experts with the following distribution: Qi-Deficiency pattern (n = 810), Fire-Heat (FH) pattern (n = 1031), Dampness-Phlegm (DP) pattern (n = 1127), and Yin-Deficiency pattern (n = 498). Approximately 89.9% of subjects enrolled in this study had cerebral infarction. Some of specific symptoms were related to each type of PI, and obese phenotypes and blood lipids were significantly related to DP and FH. These results showed the characteristics of each type of PI and should lead to the standardization of diagnosis for stroke in TKM.

The Study of Misclassification Probability in Discriminant Model of Pattern Identification for Stroke

Background. Pattern identification (PI) is the basic system for diagnosis of patients in traditional Korean medicine (TKM). The purpose of this study was to identify misclassification objects in discriminant model of PI for improving the classification accuracy of PI for stroke. Methods. The study included 3306 patients with stroke who were admitted to 15 TKM hospitals from June 2006 to December 2012. We derive the four kinds of measure (D, R, S, and C score) based on the pattern of the profile graphs according to classification types. The proposed measures are applied to the data to evaluate how well those detect misclassification objects. Results. In 10–20% of the filtered data, misclassification rate of C score was highest compared to those rates of other scores (42.60%, 41.15%, resp.). In 30% of the filtered data, misclassification rate of R score was highest compared to those rates of other scores (40.32%). And, in 40–90% of the filtered data, misclassification rate of D score was highest compared to those rates of other scores. Additionally, we can derive the same result of C score from multiple regression model with two independent variables. Conclusions. The results of this study should assist the development of diagnostic standards in TKM.