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

J Heart Lung Transplant. 2010 Jul;29(7):731-8. doi: 10.1016/j.healun.2010.02.007. Epub 2010 Apr 9.


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 1-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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Decision Support Techniques*
  • Female
  • Humans
  • Lung Transplantation / mortality*
  • Male
  • Middle Aged
  • Models, Statistical*
  • Patient Selection
  • Predictive Value of Tests
  • ROC Curve
  • Regression Analysis
  • Resource Allocation
  • Survival Rate
  • Time Factors
  • Tissue and Organ Procurement*
  • Young Adult