Latent tree analysis for the identification and differentiation of evidence-based Traditional Chinese Medicine diagnostic patterns: A primer for clinicians.

Publisher: Urban & Fischer Verlag Country of Publication: Germany NLM ID: 9438794 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1618-095X (Electronic) Linking ISSN: 09447113 NLM ISO Abbreviation: Phytomedicine Subsets: MEDLINE

Publication: Stuttgart : Urban & Fischer Verlag
Original Publication: Stuttgart ; New York : G. Fischer, c1994-

Evidence-Based Medicine* ; Medicine, Chinese Traditional*; Cross-Sectional Studies ; Diagnosis, Differential ; Humans ; Reproducibility of Results

Background: A supplementary chapter on the diagnostic patterns of Traditional Medicine, including Traditional Chinese Medicine (TCM), was introduced into the latest edition of the International Classification of Diseases (ICD-11). However, evidence-based rules are yet to be developed for pattern differentiation in patients with specific conventional medicine diagnoses. Without such standardised rules, the level of diagnostic agreement amongst practitioners is unsatisfactory. This may reduce the reliability of practice and the generalisability of clinical research.
Purpose: Using cross-sectional study data from patients with functional dyspepsia, we reviewed and illustrated a quantitative approach that combines TCM expertise and computer algorithmic capacity, namely latent tree analysis (LTA), to establish score-based pattern differentiation rules.
Review of Methods: LTA consists of six major steps: (i) the development of a TCM clinical feature questionnaire; (ii) statistical pattern discovery; (iii) statistical pattern interpretation; (iv) TCM diagnostic pattern identification; (v) TCM diagnostic pattern quantification; and (vi) TCM diagnostic pattern differentiation. Step (i) involves the development of a comprehensive questionnaire covering all essential TCM clinical features of the disease of interest via a systematic review. Step (ii) to (iv) required input from TCM experts, with the algorithmic capacity provided by Lantern, a dedicated software for TCM LTA.
Motivational Example to Illustrate the Methods: LTA is used to quantify the diagnostic importance of various clinical features in each TCM diagnostic pattern in terms of mutual information and cumulative information coverage. LTA is also capable of deriving score-based differentiation rules for each TCM diagnostic pattern, with each clinical feature being provided with a numerical score for its presence. Subsequently, a summative threshold is generated to allow pattern differentiation. If the total score of a patient exceeded the threshold, the patient was diagnosed with that particular TCM diagnostic pattern.
Conclusions: LTA is a quantitative approach to improving the inter-rater reliability of TCM diagnosis and addressing the current lack of objectivity in the ICD-11. Future research should focus on how diagnostic information should be coupled with effectiveness evidence derived from network meta-analysis. This will enable the development of an implementable diagnostics-to-treatment scheme for further evaluation. If successful, this scheme will transform TCM practice in an evidence-based manner, while preserving the validity of the model.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that may appear to have influenced the work reported in this paper..
(Copyright © 2022. Published by Elsevier GmbH.)

Keywords: Chinese traditional; Cluster analysis; Dyspepsia; Latent tree analysis; Medicine

Date Created: 20220822 Date Completed: 20220926 Latest Revision: 20220926

20240628

10.1016/j.phymed.2022.154392

35994848