Paper List

期刊: ArXiv Preprint
发布日期: 2025-12
BioinformaticsClinical Research

The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations

Bayesian Intelligence | Monash University | The Kids Research Institute Australia | University of Sydney | The Children's Hospital at Westmead

Steven Mascaro, Owen Woodberry, Charlie McLeod, Mitch Messer, Hiran Selvadurai, Yue Wu, Andre Schultz, Thomas L Snelling
Figure
Figure
Figure
Figure
Figure

30秒速读

IN SHORT: This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowledge to guide clinical trial design and enable robust causal inference.

核心创新

  • Methodology Developed a comprehensive Bayesian causal model (DAG/BN) integrating 4 domains (background factors, treatments, exacerbation episode, outcomes) with 30+ nodes representing key pathophysiological processes
  • Methodology Implemented a structured expert elicitation process involving 30+ CF clinicians across multiple workshops (2017-2019) using Delphi/nominal group techniques for variable selection and validation
  • Biology Explicitly models the causal pathways between abnormal mucus clearance, pathogen colonization (Pseudomonas aeruginosa, MRSA, etc.), infection, and inflammation - enabling targeted treatment effect analysis

主要结论

  • The BEAT-CF causal model successfully integrates expert knowledge from 30+ clinicians into a formal DAG structure with 4 domains and 30+ nodes, validated through multiple workshops (2017-2019)
  • The framework enables explicit causal inference by identifying necessary adjustments for statistical analyses, directly guiding data collection design for clinical trials
  • The model provides a reusable, transparent framework that captures key relationships between background factors (lung disease age, CFTR mutations), treatments (antibiotics, anti-inflammatories), and outcomes (lung function decline, mortality)
研究空白： Current CF exacerbation management lacks consensus on optimal approaches due to heterogeneous interventions, small inconclusive studies, and implicit causal assumptions in trial design that hinder evidence synthesis and causal inference.

摘要: 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.