ArticleBMJ open respiratory research2026
Development and validation of a disease-syndrome integrated assessment model for AECOPD severity: protocol for a nationwide multicentre cross-sectional study.
Article in BMJ open respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06918353 (Construction and Evaluation of the Severity Assessment Model for AECOPD With Combination of Disease and Syndrome Based on Machine Learning), which is not on this map. Not yet cited in PubMed.
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Construction and Evaluation of the Severity Assessment Model for AECOPD With Combination of Disease and Syndrome Based on Machine Learning
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6 authors.
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Abstract
introductionAcute exacerbations of chronic obstructive pulmonary disease (AECOPD) accelerate disease progression and increase mortality. Accurate severity assessment is essential for standardised clinical management. Traditional Chinese Medicine (TCM) shows efficacy in treating AECOPD, but no practical TCM-specific severity assessment tool exists. This study integrates multidimensional Western and TCM variables to develop an AECOPD severity assessment model, aiming to support clinical evaluation, improve treatment efficacy and prognosis and offer methodological insights for related research. METHODS AND ANALYSIS: The overall study design comprises four sequential steps. First, factors influencing AECOPD severity will be preliminarily screened through literature review and the first round of expert questionnaire surveys. Second, a multicentre cross-sectional study will be performed to establish a clinical information database; multiple statistical methods, combined with a second expert questionnaire survey, will be employed to identify the relevant variables associated with disease severity. Third, based on the established clinical database and identified variables, a disease-syndrome integrated AECOPD severity assessment model will be developed using both Classification and Regression Trees and Backpropagation Neural Networks. Fourth, the models will undergo comprehensive evaluation and validation to determine an accurate, practical, reproducible and externally valid disease severity assessment tool. ETHICS AND DISSEMINATION: The study protocol and all required documents have been submitted for review and approval to the Independent Ethics Committees of all the participating sites. All participants will provide their written informed consent on study entry, and all the recorded data will be treated as confidential. Ethical approval for this study was granted by the Institutional Review Board of the First Affiliated Hospital of Henan University of Chinese Medicine (Approval No. 2025HL-342) TRIAL REGISTRATION NUMBER: NCT06918353.
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