Presenter/Author:

Leonard HO

Other Author:

Yulong Xu, Nevin L Zhang, Fai Fai Ho, Irene XY Wu, Shuijiao Chen, Xiaowei Liu, Charlene HL Wong, Jessica YL Ching, Pui Kuan Cheong, Wing Fai Yeung, Justin CY Wu, Vincent CH Chung

Author Affiliation:

School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong

Keywords:

Chinese Medicine; Diagnosis; Dyspepsia; Machine Learning; Cluster Analysis

Abstract:

Background: Traditional Chinese Medicine (TCM) treatment strategies are guided by pattern differentiation, as documented in the eleventh edition of the International Classification of Diseases (ICD). However, no standards for pattern differentiation are proposed to ensure inter-rater agreement. Without standardisation, research on associations between TCM diagnostic patterns, clinical features, and geographical characteristics is also not feasible. This diagnostic cross-sectional study aimed to (i) establish the pattern differentiation rules of functional dyspepsia (FD) using latent tree analysis (LTA); (ii) compare the prevalence of diagnostic patterns in Hong Kong and Hunan; (iii) discover the co-existence of diagnostic patterns; and (iv) reveal the associations between diagnostic patterns and FD common comorbidities.

Methods: A total of 250 and 150 participants with FD consecutively sampled in Hong Kong and Hunan, respectively, completed a questionnaire on TCM clinical features. LTA was performed to reveal TCM diagnostic patterns of FD and derive relevant pattern differentiation rules. Multivariate regression analyses were performed to quantify correlations between different diagnostic patterns and between diagnostic patterns and clinical and geographical variables.

Results: At least one TCM diagnostic pattern was differentiated in 70.7%, 73.6%, and 64.0% of the participants in the overall (n = 400), Hong Kong (n = 250), and Hunan (n = 150) samples, respectively, using the eight pattern differentiation rules derived. 52.7% to 59.6% of the participants were diagnosed with two or more diagnostic patterns. Cold-heat complex (59.8%) and spleen-stomach dampness-heat (77.1%) were the most prevalent diagnostic patterns in Hong Kong and Hunan, respectively. Spleen-stomach deficiency cold was highly likely to co-exist with spleen-stomach qi deficiency (adjusted odds ratio (AOR): 53.23; 95% confidence interval (CI): 21.77 to 130.16). Participants with severe anxiety tended to have liver qi invading the stomach (AOR: 1.20; 95% CI: 1.08 to 1.33).

Conclusions: Future updates of the ICD, textbooks, and guidelines should emphasise the importance of clinical and geographical variations in TCM diagnosis. Location-specific pattern differentiation rules should be derived from local data using LTA. In the future, patients’ pattern differentiation results, local prevalence of TCM diagnostic patterns, and corresponding TCM treatment choices should be accessible to practitioners on online clinical decision support systems to streamline service delivery.

Reference:

Zhang NL. Hierarchical Latent Class Models for Cluster Analysis. J Mach Learn Res. 2004;5:697–723.
Zhang NL, Fu C, Liu TF, Chen BX, Poon KM, Chen PX, et al. A data-driven method for syndrome type identification and classification in traditional Chinese medicine. J Integr Med. 2017;15(2):110-23.

Funding Source:

Hong Kong Health and Medical Research Fellowship Scheme (Reference number: 03170027), the National Natural Science Foundation of China (Reference number: 81973709), and the Natural Science Foundation of Hunan Province (Reference number: 2019JJ40348)

Conflict of Interests:

The authors declare that they have no competing interests.