A statistical model to predict one-year risk of death in patients with cystic fibrosis.

OBJECTIVES We constructed a statistical model to assess the risk of death for cystic fibrosis (CF) patients between scheduled annual clinical visits. Our model includes a CF health index that shows the influence of risk factors on CF chronic health and on the severity and frequency of CF exacerbations. STUDY DESIGN AND SETTING Our study used Canadian CF registry data for 3,794 CF patients born after 1970. Data up to 2010 were analyzed, yielding 44,390 annual visit records. Our stochastic process model postulates that CF health between annual clinical visits is a superposition of chronic disease progression and an exacerbation shock stream. Death occurs when an exacerbation carries CF health across a critical threshold. The data constitute censored survival data, and hence, threshold regression was used to connect CF death to study covariates. Maximum likelihood estimates were used to determine which clinical covariates were included within the regression functions for both CF chronic health and CF exacerbations. RESULTS Lung function, Pseudomonas aeruginosa infection, CF-related diabetes, weight deficiency, pancreatic insufficiency, and the deltaF508 homozygous mutation were significantly associated with CF chronic health status. Lung function, age, gender, age at CF diagnosis, P aeruginosa infection, body mass index <18.5, number of previous hospitalizations for CF exacerbations in the preceding year, and decline in forced expiratory volume in 1 second in the preceding year were significantly associated with CF exacerbations. When combined in one summative model, the regression functions for CF chronic health and CF exacerbation risk provided a simple clinical scoring tool for assessing 1-year risk of death for an individual CF patient. Goodness-of-fit tests of the model showed very encouraging results. We confirmed predictive validity of the model by comparing actual and estimated deaths in repeated hold-out samples from the data set and showed excellent agreement between estimated and actual mortality. CONCLUSION Our threshold regression model incorporates a composite CF chronic health status index and an exacerbation risk index to produce an accurate clinical scoring tool for prediction of 1-year survival of CF patients. Our tool can be used by clinicians to decide on optimal timing for lung transplant referral.