Development of a predictive model for long-term survival after lung transplantation and implications for the lung allocation score

BACKGROUND: Improving long-term survival after lung transplantation can be facilitated by identifying patient characteristics that are predictors of positive long-term outcomes. Validated survival modeling is important for guiding clinical decision-making, case-mix adjustment in comparative effectiveness research and refinement of the lung allocation system (LAS). METHODS: We used the registry of the International Society for Heart and Lung Transplantation (ISHLT) to develop and validate a predictive model of 5-year survival after lung transplantation. A total of 18,072 eligible cases were randomly split into development and validation datasets. Pre-transplant recipient variables considered included age, gender, diagnosis, body mass index, serum creatinine, hemodynamic variables, pulmonary function variables, viral status and comorbidities. Predictors were considered in a stepwise approach with the Akaike Information Criteria (AIC). Time-dependent receiver operator characteristic (ROC) curves assessed predictive ability. A 1-year conditional model and three models for disease subgroups were considered. ROC methods were used to characterize the predictive potential of the LAS post-transplant model at 1 and 5 years. RESULTS: The baseline model included age, diagnosis, creatinine, bilirubin, oxygen requirement, cardiac output, Epstein Barr virus status, transfusion history and diabetes history. Prediction of long-term survival was poor (area under the curve [AUC] = 0.582). Neither the I-year conditional model (AUC -= 0.573) nor models designed for separate diseases (AUC = 0.553 to 0.591) improved survival prediction. The predictive ability of the LAS post-transplant parameters was similar to that of our model (1-year AUC = 0.580 and 5-year AUC = 0.566). CONCLUSIONS: Models developed from pre-transplant characteristics poorly predict long-term survival. Models for separate diseases and 1-year conditional models did not improve prediction. Better databases and approaches to predict survival are needed to improve lung allocation. J Heart Lung Transplant 2010;29:731-8 Published by Elsevier Inc.