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Background: Effective allocation of limited donor lungs in cystic fibrosis(CF) requires accurate survival predictions, so that high-risk patients may beprioritized for transplantation. In practice, decisions about allocation aremade dynamically, using routinely updated assessments. We present a novel toolfor evaluating risk prediction models that, unlike traditional methods,captures the dynamic nature of decision-making. Methods: Predicted risk is usedas a score to rank incident deaths versus patients who survive, with the goalof ranking the deaths higher. The mean rank across deaths at a given timemeasures time-specific predictive accuracy; when assessed over time, itreflects time-varying accuracy. Results: Applying this approach to CF Registrydata on patients followed from 1993-2011, we show that traditional methods donot capture the performance of models used dynamically in the clinical setting.Previously proposed multivariate risk scores perform no better than forcedexpiratory volume in 1 second as a percentage of predicted normal (FEV1%)alone. Despite its value for survival prediction, FEV1% has a low sensitivityof 45% over time (for fixed specificity of 95%), leaving room for improvementin prediction. Finally, prediction accuracy with annually-updated FEV1% showsminor differences compared to FEV1% updated every 2 years, which may haveclinical implications regarding the optimal frequency of updating clinicalinformation. Conclusions: It is imperative to continue to develop models thataccurately predict survival in CF. Our proposed approach can serve as the basisfor evaluating the predictive ability of these models by better accounting fortheir dynamic clinical use.

A Novel Tool to Evaluate the Accuracy of Predicting Survival in Cystic Fibrosis