Objective To analyze the substitution mechanism of surrogate endpoints for traditional Chinese medicine (TCM) clinical efficacy evaluation of chronic heart failure (CHF). Methods To obtain data from the occurrence of surrogate endpoints and cardiogenic death of patients with CHF in 7 hospitals. The causal relationship between surrogate endpoints and cardiogenic mortality was inferred by the Bayesian network model, and the interaction among surrogate endpoints was analyzed by non-conditional logistic regression model. Results A total of 2 961 patients with CHF were included. The results of Bayesian network causal inference showed that cardiogenic mortality had a causal relationship with the surrogate endpoints including NYHA classification (P=0.46), amino-terminal pro-B-type natriuretic peptide (NT-proBNP) (P=0.24), left ventricular ejaculation fraction (LVEF) (P=0.19), and hemoglobin (HB) (P=0.11); non-conditional logistic regression analysis showed that NYHA classification had interaction with NT-proBNP, LVEF, and HB prior to and after adjusting confounders. Conclusions The substitution capability of surrogate endpoints for TCM clinical efficacy evaluation of CHF for cardiogenic mortality are NYHA classification, NT-proBNP, LVEF, and HB in turn, and there is a multiplicative interaction between the main surrogate endpoint NYHA classification and the secondary surrogate endpoints including NT-proBNP, LVEF, and HB, suggesting that when the two surrogate endpoints with interaction exist at the same time, it can enhance the substitution capability of surrogate endpoints for cardiogenic mortality.
Evidence-based medicine advocates to support clinical decision-making with the best evidence, which is useful to objectively evaluate the clinical efficacy of traditional Chinese medicine and optimize clinical diagnosis and treatment. However, significant individualized characteristics identified from syndrome differentiation and treatment are incompatible with evidence-based clinical decision-making, which highlights population-level evidence, to some extent. In recent years, a number of new methods and technologies have been introduced into individualized clinical efficacy evaluation research of traditional Chinese medicine to assist managing and processing complex and multivariate information. These methods and technologies share similarities with evidence-based medicine, and are expected to link the clinical practice of traditional Chinese medicine with evidence-based clinical decision-making. They will guide the development of evidence-based clinical decision-making in traditional Chinese medicine.
ObjectiveTo systematically investigate the application status of the minimal clinically important difference (MCID) and minimal important change (MIC) in intragroup and intergroup analyses of functional constipation symptom scales from 2000 to 2025, and provide a reference for the standardized formulation of clinical efficacy evaluation criteria for functional constipation in China. MethodsRandomized controlled trials (RCTs) and meta-analyses on functional constipation were retrieved from WanFang Data, CNKI, PubMed, Embase, and CENTRAL databases between January 1, 2000, and January 7, 2025. Three reviewers independently screened the literature, extracted information on the characteristics of MIC/MCID reported in the studies, and conducted descriptive analyses. ResultsA total of 337 studies were evaluated for readability, with 291 studies meeting the inclusion criteria. Among eligible studies, 6 used MIC/MCID thresholds, and 38 reported responder definitions, including 5 using MIC and 1 using MIC and MCID. Discrepancies were observed between the expected and actual values of MIC/MCID. Six included studies provided explicit citation support for their selected MIC/MCID thresholds. ConclusionThe application and interpretation of MIC and MCID thresholds face fundamental challenges. Using functional constipation research as an example, researchers often derive MIC-like thresholds through intergroup comparisons of individual change proportions and mistakenly equate them with MCID. This conceptual confusion may lead to clinical interpretation bias due to neglecting the essential differences between the two thresholds. Additionally, issues include lack of methodological justification for responder analysis, broad threshold ranges, and near-absence of blinded evaluations. It is recommended that researchers clarify the definitions and analytical pathways of the two thresholds during RCT design, avoid misusing intergroup statistics as individual efficacy criteria, and strengthen the methodological rigor of blinded design and threshold validation.