Quantification of prevalence, clinical characteristics, co-existence, and geographic variations of traditional Chinese medicine diagnostic patterns via latent tree analysis-based differentiation rules among functional dyspepsia patients.

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 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.

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

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.

Applying latent tree analysis to classify Traditional Chinese Medicine syndromes (Zheng) in patients with psoriasis vulgari.

OBJECTIVE: To treat patients with psoriasis vulgaris using Traditional Chinese Medicine (TCM), one must stratify patients into subtypes (known as TCM syndromes or Zheng) and apply appropriate TCM treatments to different subtypes. However, no unified symptom-based classification scheme of subtypes (Zheng) exists for psoriasis vulgaris. The present paper aims to classify patients with psoriasis vulgaris into different subtypes via the analysis of clinical TCM symptom and sign data. METHODS: A cross-sectional survey was carried out in Beijing from 2005-2008, collecting clinical TCM symptom and sign data from 2764 patients with psoriasis vulgaris. Roughly 108 symptoms and signs were initially analyzed using latent tree analysis, with a selection of the resulting latent variables then used as features to cluster patients into subtypes. RESULTS: The initial latent tree analysis yielded a model with 43 latent variables. The second phase of the analysis divided patients into three subtype groups with clear TCM Zheng connotations: 'blood deficiency and wind dryness'; 'blood heat'; and 'blood stasis'. CONCLUSIONS: Via two-phase analysis of clinic symptom and sign data, three different Zheng subtypes were identified for psoriasis vulgaris. Statistical characteristics of the three subtypes are presented. This constitutes an evidence-based solution to the syndromedifferentiation problem that exists with psoriasis vulgaris.

Evaluating traditional Chinese medicine diagnostic instruments for functional dyspepsia: systematic review on measurement properties.

BACKGROUND: Pattern diagnosis-guided treatments in Traditional Chinese Medicine (TCM) has been recognised by the eleventh revision of the International Classification of Diseases (ICD-11). Accurate pattern diagnosis requires reliable and valid diagnostic instruments that guide the collection of TCM clinical data without bias. This study synthesised the existing TCM diagnostic instruments for functional dyspepsia (FD) and appraised their quality regarding their development process and measurement properties. METHODS: Seven electronic databases were searched for validation studies on TCM diagnostic instruments for FD. Synthesis and appraisal of the included studies were performed following the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) Initiative guidelines adapted for TCM diagnostic instruments. Risk of bias assessment was conducted using the COSMIN Risk of Bias Checklist. RESULTS: Five studies were included, with five unique TCM diagnostic instruments for FD identified. All five diagnostic instruments were of inadequate quality in terms of their development process, implying a shortcoming in their relevance, comprehensibility, and comprehensiveness. Only the criterion validity of Stomach Qi Deficiency Pattern Assessment Scale was of sufficient quality and had no risk of bias in its validation. CONCLUSION: The quality of TCM diagnostic instruments for FD warrants urgent improvements. None of them was considered reliable or valid for guiding TCM pattern diagnosis. To support the evidence base of the standardization of TCM patterns in ICD-11, TCM diagnostic instruments should be developed and validated rigorously under the COSMIN guidelines. Amendments should be made on the guidelines to accommodate the features and uniqueness of TCM diagnostic process.

Discovering symptom co-occurrence patterns from 604 cases of depressive patient data using latent tree models.

OBJECTIVES: In order to treat depressive patients using Traditional Chinese Medicine (TCM), it is necessary to classify them into subtypes from the TCM perspective. Those subtypes are called Zheng types. This article aims at providing evidence for the classification task by discovering symptom co-occurrence patterns from clinic data. METHODS: Six hundred four (604) cases of depressive patient data were collected. The subjects were selected using the Chinese classification of mental disorder clinic guideline CCMD-3. The symptoms were selected based on the TCM literature on depression. The data were analyzed using latent tree models (LTMs). RESULTS: An LTM with 29 latent variables was obtained. Each latent variable represents a partition of the subjects into 2 or more clusters. Some of the clusters capture probabilistic symptom co-occurrence patterns, while others capture symptom mutual-exclusion patterns. Most of the co-occurrence patterns have clear TCM Zheng connotations. CONCLUSIONS: From clinic data about depression, probabilistic symptom co-occurrence patterns have been discovered that can be used as evidence for the task of classifying depressive patients into Zheng types.

Statistical validation of Traditional Chinese Medicine syndrome postulates in the context of patients with cardiovascular disease.

OBJECTIVE: Traditional Chinese Medicine (TCM) has many postulates that explain the occurrence and co-occurrence of symptoms using syndrome factors such as yang deficiency and yin deficiency. A fundamental question is whether the syndrome factors have verifiable scientific content or are purely subjective notions. This analysis investigated the issue in the context of patients with cardiovascular disease (CVD). DESIGN: In the past, researchers have tried to show that TCM syndrome factors correspond to real entities by means of laboratory tests, with little success. An alternative approach, called latent tree analysis, has recently been proposed. The idea is to discover latent variables behind symptom variables by analyzing symptom data and comparing them with TCM syndrome factors. If there is a good match, then statistical evidence supports the validity of the relevant TCM postulates. This study used latent tree analysis. SETTING: TCM symptom data of 3021 patients with CVD were collected from the cardiology departments of four hospitals in Shanghai, China, between January 2008 and June 2010. RESULTS: Latent tree analysis of the data yielded a model with 34 latent variables. Many of them correspond to TCM syndrome factors. CONCLUSIONS: The results provide statistical evidence for the validity of TCM postulates in the context of patients with CVD; in other words, they show that TCM postulates are applicable to such patients. This finding is important because it is a precondition for the TCM treatment of those patients.

Statistical validation of traditional chinese medicine theories.

The theories of Traditional Chinese Medicine (TCM) originated from experiences doctors had with patients in ancient times. We ask the question whether aspects of TCM theories can be reconstructed through data analysis. To answer the question, we have developed a data analysis method called latent tree models and have used it to analyze several TCM data sets. This paper reports the results we obtained on one of the data sets and explains how they provide statistical validation to the relevant TCM theories.

Latent tree models and diagnosis in traditional Chinese medicine.

OBJECTIVE: TCM (traditional Chinese medicine) is an important avenue for disease prevention and treatment for the Chinese people and is gaining popularity among others. However, many remain skeptical and even critical of TCM because of a number of its shortcomings. One key shortcoming is the lack of objective diagnosis standards. We endeavor to alleviate this shortcoming using machine learning techniques. METHOD: TCM diagnosis consists of two steps, patient information gathering and syndrome differentiation. We focus on the latter. When viewed as a black box, syndrome differentiation is simply a classifier that classifies patients into different classes based on their symptoms. A fundamental question is: do those classes exist in reality? To seek an answer to the question from the machine learning perspective, one would naturally use cluster analysis. Previous clustering methods are unable to cope with the complexity of TCM. We have therefore developed a new clustering method in the form of latent tree models. We have conducted a case study where we first collected a data set about a TCM domain called kidney deficiency and then used latent tree models to analyze the data set. RESULTS: Our analysis has found natural clusters in the data set that correspond well to TCM syndrome types. This is an important discovery because (1) it provides statistical validation to TCM syndrome types and (2) it suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation. In this paper, we provide a summary of research work on latent tree models and report the aforementioned case study.

Latent variable discovery in classification models.

The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical applications, because it can lead to a better understanding of application domains. It can also improve classification accuracy and boost user confidence in classification models.