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Exploring effective core drug patterns in primary insomnia treatment with Chinese herbal medicine: study protocol for a randomized controlled trial.
Abstract
BACKGROUND:
Chinese herbal medicine is one of the most popular Chinese medicine (CM) therapies for primary insomnia. One of the important characteristics of CM is that different Chinese clinicians give different prescriptions even for the same patient. However, there must be some fixed drug patterns in every clinician’s prescriptions. This study aims to screen the effective core drug patterns in primary insomnia treatment of three prestigious Chinese clinicians.
METHODS/DESIGN:
A triple-blind, randomized, placebo-controlled, parallel-group clinical trial will be performed. Three clinicians will diagnose and treat every eligible patient individually and independently, producing three prescriptions from three clinicians for every patient. Patients will equally be randomized to one of four groups – medical group A, medical group B, medical group C, or placebo group – and observed for efficacy of treatment. The sample will include primary insomnia patients meeting DSM IV-TR criteria, Spiegel scale score >18, and age 18 to 65 years. A sequential design is employed. Interim analysis will be conducted when between 80 and 160 patients complete the study. The interim study could be stopped and treated as final if a statistically significant difference between treatment and placebo groups can be obtained and core effective drug patterns can be determined. Otherwise, the study continues until the maximum sample size reaches 300. Treatment of the CM group is one of three Chinese clinicians’ prescriptions, who provide independently prescriptions based on their own CM theory and the patient’s disease condition. Assessment will be by sleep diary and Pittsburgh sleep quality index, and CM symptoms and signs will be measured. Primary outcome is total sleep time. Assessment will be carried out at the washout period, weeks 1, 2, 3, and 4 and 4th week after the end of treatment. Effectiveness analysis will be per intent to treat. A multi-dimension association rule and scale-free networks method will be used to explore the effective core drug patterns.
DISCUSSION:
The effective core drug patterns will be found through analyzing several prestigious CM clinicians’ treatment information. Screening the effective core drug patterns from prestigious clinicians can accelerate the development of new CM drugs.
Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.
Abstract
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 {d382666222b3cff0b1122f689bebcc8d35b41f83934c69a0d9586603ddee8f2f} 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 {d382666222b3cff0b1122f689bebcc8d35b41f83934c69a0d9586603ddee8f2f}) 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.
