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Developing Cystic Fibrosis Lung Transplant Referral Criteria Using Predictors of 2-Year Mortality

Departments of Pediatrics and Medicine, University of Washington; and Statistical Analysis Unit, Cystic Fibrosis Therapeutics Development Network Coordinating Center, Children's Hospital and Regional Medical Center, Seattle, Washington

Correspondence and requests for reprints should be addressed to Nicole Mayer-Hamblett, Ph.D., Department of Pediatrics, University of Washington, 4800 Sand Point Way N.E., Box 5371, CL-11, Seattle, WA 98105-0371. E-mail: nhambl{at}chmc.org

The first objective of our study was to develop a model identifying the best clinical predictors of 2-year mortality among patients with cystic fibrosis (CF), to assist in selection of appropriate candidates for lung transplantation. Using multivariate logistic regression, we found that age, height, FEV₁, respiratory microbiology, number of hospitalizations for pulmonary exacerbations, and number of home intravenous antibiotic courses were all significant predictors of 2-year mortality among 14,572 patients in the Cystic Fibrosis Foundation National Patient Registry who were 6 years of age or older in 1996. The second objective was to compare the diagnostic accuracy of our model when used to guide referral for lung transplant with that of the widely used criterion of an FEV₁ of less than 30% predicted. Surprisingly, this well-fitting model derived from the largest collection of data available on patients with CF provided no better diagnostic accuracy than the simpler FEV₁ criterion. Both had high negative predictive values (98 and 97%, respectively) but only modest positive predictive values (33 and 28%, respectively). Transplant referral decisions based either on a multivariate logistic model or on the criterion of an FEV₁ of less than 30% predicted are likely to result in high rates of premature referral. Better clinical predictors of short-term mortality among patients with CF are needed.

Key Words: cystic fibrosis • mortality • lung transplantation • logistic models

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