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.
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.
Authors: Richard N Pierson; Mark L Barr; Keith P McCullough; Thomas Egan; Edward Garrity; Mariell Jessup; Susan Murray Journal: Am J Transplant Date: 2004 Impact factor: 8.086
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Authors: T M Egan; S Murray; R T Bustami; T H Shearon; K P McCullough; L B Edwards; M A Coke; E R Garrity; S C Sweet; D A Heiney; F L Grover Journal: Am J Transplant Date: 2006 Impact factor: 8.086
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Authors: Jay M Brahmbhatt; Travis Hee Wai; Christopher H Goss; Erika D Lease; Christian A Merlo; Siddhartha G Kapnadak; Kathleen J Ramos Journal: J Heart Lung Transplant Date: 2022-05-20 Impact factor: 13.569
Authors: Patrick J Smith; James A Blumenthal; Benson M Hoffman; Sarah K Rivelli; Scott M Palmer; Robert D Davis; Joseph P Mathew Journal: Ann Am Thorac Soc Date: 2016-02
Authors: Andrew T Braun; Elliott C Dasenbrook; Ashish S Shah; Jonathan B Orens; Christian A Merlo Journal: J Heart Lung Transplant Date: 2015-06-11 Impact factor: 10.247
Authors: Patrick J Smith; Gregory L Stonerock; Krista K Ingle; Caroline K Saulino; Benson Hoffman; Brian Wasserman; James A Blumenthal; Scott M Palmer; Jacob A Klapper; Matthew G Hartwig; Valentine R Esposito; Laurie D Snyder Journal: Transplant Direct Date: 2018-03-19