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.

SYmptom-Based STratification of DiabEtes Mellitus by Renal Function Decline (SYSTEM): A Retrospective Cohort Study and Modeling Assessment.

Background: Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine. Methods: This retrospective cohort study aimed to evaluate the predictive power of a symptom-based stratification framework and individual symptoms for diabetes. Three hundred two adult diabetic patients were consecutively sampled from outpatient clinics in Hong Kong for prospective symptom assessment. Demographics and longitudinal measures of biochemical parameters were retrospectively extracted from linked medical records. The association between estimated glomerular filtration rate (GFR) (independent variable) and biochemistry, epidemiological factors, and individual symptoms was assessed by mixed regression analyses. A symptom-based stratification framework of diabetes using symptom clusters was formulated by Delphi consensus method. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were compared between statistical models with different combinations of biochemical, epidemiological, and symptom variables. Results: In the 4.2-year follow-up period, baseline presentation of edema (-1.8 ml/min/1.73m2, 95%CI: -2.5 to -1.2, p < 0.001), epigastric bloating (-0.8 ml/min/1.73m2, 95%CI: -1.4 to -0.2, p = 0.014) and alternating dry and loose stool (-1.1 ml/min/1.73m2, 95%CI: -1.9 to -0.4, p = 0.004) were independently associated with faster annual GFR decline. Eleven symptom clusters were identified from literature, stratifying diabetes predominantly by gastrointestinal phenotypes. Using symptom clusters synchronized by Delphi consensus as the independent variable in statistical models reduced complexity and improved explanatory power when compared to using individual symptoms. Symptom-biologic-epidemiologic combined model had the lowest AIC (4,478 vs. 5,824 vs. 4,966 vs. 7,926) and BIC (4,597 vs. 5,870 vs. 5,065 vs. 8,026) compared to the symptom, symptom-epidemiologic and biologic-epidemiologic models, respectively. Patients co-presenting with a constellation of fatigue, malaise, dry mouth, and dry throat were independently associated with faster annual GFR decline (-1.1 ml/min/1.73m2, 95%CI: -1.9 to -0.2, p = 0.011). Conclusions: Add-on symptom-based diagnosis improves the predictive power on renal function decline among diabetic patients based on key biochemical and epidemiological factors. Dynamic change of symptoms should be considered in clinical practice and research design.

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.

Add-On Chinese Medicine for Coronavirus Disease 2019 (ACCORD): A Retrospective Cohort Study of Hospital Registries.

Chinese medicine (CM) was extensively used to treat COVID-19 in China. We aimed to evaluate the real-world effectiveness of add-on semi-individualized CM during the outbreak. A retrospective cohort of 1788 adult confirmed COVID-19 patients were recruited from 2235 consecutive linked records retrieved from five hospitals in Wuhan during 15 January to 13 March 2020. The mortality of add-on semi-individualized CM users and non-users was compared by inverse probability weighted hazard ratio (HR) and by propensity score matching. Change of biomarkers was compared between groups, and the frequency of CMs used was analyzed. Subgroup analysis was performed to stratify disease severity and dose of CM exposure. The crude mortality was 3.8% in the semi-individualized CM user group and 17.0% among the non-users. Add-on CM was associated with a mortality reduction of 58% (HR = 0.42, 95% CI: 0.23 to 0.77, [Formula: see text] = 0.005) among all COVID-19 cases and 66% (HR = 0.34, 95% CI: 0.15 to 0.76, [Formula: see text] = 0.009) among severe/critical COVID-19 cases demonstrating dose-dependent response, after inversely weighted with propensity score. The result was robust in various stratified, weighted, matched, adjusted and sensitivity analyses. Severe/critical patients that received add-on CM had a trend of stabilized D-dimer level after 3-7 days of admission when compared to baseline. Immunomodulating and anti-asthmatic CMs were most used. Add-on semi-individualized CM was associated with significantly reduced mortality, especially among severe/critical cases. Chinese medicine could be considered as an add-on regimen for trial use.

Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis.

OBJECTIVE: To treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA). METHOD: A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules. RESULTS: Eight syndrome types are identified: Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types. CONCLUSION: A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.

A data-driven method for syndrome type identification and classification in traditional Chinese medicine.

The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.

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

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.

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.