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

https://doi.org/10.1016/j.healun.2010.02.007Get rights and content

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.

Section snippets

Population

The International Society for Heart and Lung Transplantation (ISHLT) has maintained a registry for lung transplants since 1988. This database is compilation of data from registries around the world including those of the Organ Procurement and Transplantation Network (OPTN). As of June 2008, the registry logged a total of 27,075 lung transplant recipients. Because practices have changed substantially since the first recorded lung transplant, we excluded transplants prior to 1997. In addition, we

Baseline characteristics

Baseline characteristics revealed that the median (interquartile range [IQR]) age of recipients was 54 (42 to 59) years; most recipients were male (53%) and most had COPD (44%; Table 1). Although data on age, gender and diagnosis were complete by design, there was a significant amount of missing data for other variables (Table 1).

Univariate analysis

In comparison to patients with COPD, patients with IPF, other types of fibrosis or pulmonary hypertension were significantly less likely to survive to 5 years, whereas

Discussion

In this study we have suggested that the ability to predict long-term survival with pre-transplant characteristics is poor. In addition, although different covariates were identified among our disease subgroup models, our ability to predict long-term survival was not improved. Finally, the LAS was shown to have a poor ability to predict both 1- and 5-year survival.

Despite multiple modeling approaches, the ability to predict 5-year survival was not much better than chance. There are many

Disclosure statement

Supported by a Transplant Registry Junior Faculty Award of the International Society for Heart and Lung Transplantation (ISHLT) and the National Center for Research Resources (NCRR) from a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research (Grant No. 5KL2RR025015-02).

The authors thank Leah Edwards, PhD for preparing the ISHLT Registry data for analysis, and for her helpful comments regarding the manuscript.

The authors have no conflicts of interest to

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