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

TL;DR

The paper presents BEAT-CF, a causal DAG/BN framework for CF exacerbations designed to guide trial design and observational analyses. Developed through a structured, expert-driven elicitation process (2017–2019) and qualitatively validated, the model links background factors, treatments, pathogen dynamics, and outcomes to enable causal inference. It offers a transparent, reusable framework that informs data collection and registry design, with planned extensions to incorporate modulators and longer-term dynamics. The BEAT-CF framework aims to improve causal estimation, trial efficiency, and decision-support in CF care.

Abstract

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.

Figures (8)

• Figure 1: A high-level view of the BEAT-CF causal model, showing the 4 domains and the causal influences between them.
• Figure 2:
• Figure 3: Background factors that affect the incidence and nature of an exacerbation
• Figure 4: Treatments for a CF exacerbation included in the BEAT-CF causal model
• Figure 5: Transient and persistent consequences of a CF exacerbation. Longer term outcomes include changes to lung function and mortality