Survival models to support shared decision-making about advance care planning for people with advanced stage cystic fibrosis. Issue 1 (24th May 2021)

• Record Type: Journal Article
• Authors: Hajizadeh, Negin; Zhang, Meng; Akerman, Meredith; Kohn, Nina; Mathew, Anna; Hadjiliadis, Denis; Wang, Janice; Lesser, Martin L
• Is Part Of: BMJ open respiratory research. Volume 8:Issue 1(2021)
• Publication Date: 2021-05-24
• Subjects: cystic fibrosis -- lung transplantation; Respiratory organs -- Diseases -- Periodicals; Respiratory organs -- Diseases -- Treatment -- Periodicals; Respiratory therapy -- Periodicals; 616.2005
• DOI: 10.1136/bmjresp-2020-000794
• Languages: English
• ISSNs: 2052-4439

Abstract

Abstract : Background: For people with advanced stage cystic fibrosis (CF), tailored survival estimates could facilitate preparation for decision-making in the event of acutely deteriorating respiratory function. Methods: We used the US CF Foundation national database (2008–2013) to identify adult people with incident advanced stage CF (forced expiratory volume in 1 s (FEV1) ≤45% predicted). Using the lasso method for variable selection, we divided the dataset into training and validation samples (2:1), and developed two multivariable Cox proportional hazards models to calculate probabilities of survival from baseline (T0 model), and from 1 year after (T12 model). We also performed Kaplan-Meier survival analyses. Results: 4752 people were included. For the T0 model, FEV1; insurance; non-invasive ventilation; supplemental oxygen; Burkholderia colonisation; cirrhosis; depression; dialysis; current smoking; unclassifiable mutation class and cumulative CF exacerbations predicted increased mortality. Baseline transplant evaluation status of 'accepted, on waiting list' predicted decreased mortality. For the T12 model, interim decrease in FEV1 >10%, and pulmonary exacerbations additionally increased predicted mortality. Lung transplantation was associated with lower mortality. Of the 4752, 93.5%, 86.4%, 79.7% and 73.9% survived to 1, 2, 3 and 4 years, respectively, without considering any confounding variables. The models had moderate predictive ability indicated by the area under the time-dependent receiver operating characteristic curve (0.787, 95% CI 0.769 to 0.794 for T0 model; and 0.779, 95% CI 0.767 to 0.797 for T12 model). Conclusion: We have developed models predicting survival in people with incident advanced stage CF, which can be reapplied over time to support shared decision-making about end-of-life treatment choices and lung transplantation. These estimates must be updated as data become available regarding long-term outcomes for people treated with CF transmembrane conductance regulator modulators.