构建囊性纤维化急性加重的因果模型，指导临床试验设计。
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
- 基于专家共识构建囊性纤维化急性加重的因果图与贝叶斯网络。
- 明确背景因素、治疗与病原体定植对急性加重结局的影响关系。
- 为临床研究设计提供透明可复用的框架，适合研究者与决策者参考。
囊性纤维化（CF）患者肺功能呈渐进性下降，伴随急性肺部加重（PEx），其肺功能骤降无法完全恢复。过去半个世纪中，针对PEx的治疗被认为是延缓肺功能恶化、改善患者生存的关键。然而，目前尚无关于最佳管理策略的共识。为建立循证知识体系，研究团队提出BEAT-CF（贝叶斯证据自适应治疗）计划，并开发了囊性纤维化急性加重的因果模型。该模型采用有向无环图（DAG）与贝叶斯网络（BN）形式，刻画背景风险因素、治疗干预、气道病原体定植与每次急性加重结局之间的因果关系。关键变量与路径由临床专家共识提炼而成，反映当前对急性加重病理生理机制的理解。本研究展示该因果图的构建过程与逻辑，增强试验设计与研究流程的透明度，提供一个可复用的框架，供未来临床研究与分析使用。
原文摘要 · Abstract (English)
Loss of lung function in cystic fibrosis (CF) occurs progressively, punctuated by acute pulmonary exacerbations (PEx) in which abrupt declines in lung function are not fully recovered. A key component of CF management over the past half century has been the treatment of PEx to slow lung function decline. This has been credited with improvements in survival for people with CF (PwCF), but there is no consensus on the optimal approach to PEx management. BEAT-CF (Bayesian evidence-adaptive treatment of CF) was established to build an evidence-informed knowledge base for CF management. The BEAT-CF causal model is a directed acyclic graph (DAG) and Bayesian network (BN) for PEx that aims to inform the design and analysis of clinical trials comparing the effectiveness of alternative approaches to PEx management. The causal model describes relationships between background risk factors, treatments, and pathogen colonisation of the airways that affect the outcome of an individual PEx episode. The key factors, outcomes, and causal relationships were elicited from CF clinical experts and together represent current expert understanding of the pathophysiology of a PEx episode, guiding the design of data collection and studies and enabling causal inference. Here, we present the DAG that documents this understanding, along with the processes used in its development, providing transparency around our trial design and study processes, as well as a reusable framework for others.
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