Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis

Chinese Medicine

Yan-Yee Ho⁴  Pei-Xian Chen¹  Kam-Ping Yung²  Shi Ping Zhang³  Nevin Lian-Wen Zhang¹  Ka-Fai Chung⁵  Wing-Fai Yeung⁶ 

[1] Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China;Department of Psychology, The Chinese University of Hong Kong, Hong Kong SAR, China;School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China;Department of Psychology, The University of Hong Kong, Hong Kong SAR, China;Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China;School of Chinese Medicine, University of Hong Kong, Pokfulam Road, Hong Kong SAR, China

关键词: Patterns;    Latent tree model;    Latent tree analysis;    Insomnia;    Zheng;    Chinese medicine syndrome;   

Others  :  1235867
DOI  :  10.1186/s13020-016-0076-y

 received in 2014-10-31, accepted in 2016-01-26,  发布年份 2016

【 摘 要 】

Background

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes.

Methods

One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes.

Results

The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony,Hyperactivity of fire due to yin deficiency, Heart–kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified.

Conclusion

Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

【 授权许可】

   
2016 Yeung et al.

【 图 表 】

Fig.1.

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