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 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.
Gries
Cynthia J
CJ
Department of Medicine, University of Washington, Seattle, WA, USA. cderuit1@u.washington.edu
Rue
Tessa C
TC
Heagerty
Patrick J
PJ
Edelman
Jeffrey D
JD
Mulligan
Michael S
MS
Goss
Christopher H
CH
D000293:Adolescent; D000328:Adult; D000368:Aged; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016040:Lung Transplantation / Q000401:mortality*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018579:Patient Selection; D011237:Predictive Value of Tests; D012372:ROC Curve; D012044:Regression Analysis; D040841:Resource Allocation; D015996:Survival Rate; D013997:Time Factors; D009927:Tissue and Organ Procurement; D055815:Young Adult
Chest. 2005 Jul;128(1):407-15
16002964
Stat Med. 2008 Jul 30;27(17):3227-46
18203127
Stat Med. 1991 Apr;10(4):585-98
2057657
J Heart Lung Transplant. 2008 Sep;27(9):957-69
18765187
Am J Transplant. 2004;4 Suppl 9:93-105
15113358
Biometrics. 2000 Jun;56(2):337-44
10877287
J Heart Lung Transplant. 1996 Jun;15(6):631-45
8794030
Am J Transplant. 2006;6(5 Pt 2):1212-27
16613597