IPF has a variable course with an overall poor prognosis, and while non-IPFILDs have a better prognosis, both can progress to respiratory failure that may be fatal without lung transplantation. IPF/ILD has risen to the number one indication for LT in the United States since the application of the LAS in 2005 . The GAP model was developed to prognosticate mortality in patients with IPF/ILD. We hypothesized that given the poorer outcome of IPF/ILD post lung transplant, disease specific prognostication methods might prove superior to the LAS at predicting one year post transplant mortality. In this study, we find no correlation between the disease specific model, GAP index, and the LAS once listed for LT. This lack of correlation may be explained by several factors. The GAP index was designed for a different function than the LAS: to predict the natural history of mortality in those with IPF/ILD, in order to aid in decision making for therapy, enrollment in clinical trials, and timing of lung transplant referral or referral to palliative care. The LAS was designed to predict 1-year pre-transplant mortality, but also to take into account posttransplant mortality, so as to prevent transplantation in those patients in whom the procedure is likely to be futile . Some may argue that the LAS does not do the latter very well, as it has been shown in those with the highest LAS, there is increased early posttransplant mortality [21-23], though this did not hold true in our cohort of patients, potentially due to the small subject number. This is the first study to compare these two predictive models in IPF/ILD.
In our cohort of subjects, the LAS and GAP model performed poorly as predictors of early post-transplant mortality. Interestingly, the GAP and LAS also had poor correlation to one another.
There are a number of significant limitations to our study, the greatest being the small sample size. As an exploratory analysis, subjects included are limited to our institution and, therefore, are small compared to the number of lung transplants done yearly. Some components needed to calculate GAP, specifically diffusion capacity are not included in the LAS, thus, it was not possible to derive this data on a national level from the UNOS data base. The small sample size may explain why individual variables which have been shown previously to correlate with mortality were not significant in our study including age, high LAS, presence of pulmonary hypertension, BMI, and FVC. Interestingly, 29% of non-survivors did not undergo pre-transplant right heart catheterization, whereas only 5% of survivors did not have this procedure. This may reflect the urgent nature of listing for transplantation in the non-survivors, a clinical scenario which is not always reflected in the calculation of risk prediction models, but which may affect outcomes. Because of the retrospective nature, another limitation in our study is that the GAP index was not calculated at the same time as the LAS when the patient was listed for LT, with a median time of 5 weeks apart. This is due to the timing of their most recent pulmonary function testing from time of listing. This could account for the lack of correlation in GAP and LAS severity if the clinical condition worsened between one score and another, as is not uncommon in end-stage IPF/ILD of the six transplanted patients who were excluded from analysis due to limited data to calculate GAP, 4 lived and 2 died within first posttransplant year which does not significantly alter the mortality outcome (26.9% in our cohort vs. 27.5% if include 6 missing subject data). The one-year mortality rate seems quite high when compared to all comers for primary lung transplant, but is consistent with IPF-specific outcomes with survival at one year of 73-76% [7-9]. An additional limitation is the mean FVC% predicted and the LAS are higher in the 6 excluded subjects, which shows that the sample could be biased.
Previous publications have shown that predictors of poor outcomes within the first year posttransplant include increased lung allocation score , body mass index >30 or <17 [24-26], increasing age , lower lung function , lower six minute walk distance , and pre transplant mechanical ventilator  or extracorporeal support . Our data does not demonstrate a difference in LAS, BMI, age, or lung function between survivors and non-survivors. These risk factors were derived based on all comers to lung transplant. Thus, the lack of correlation in this study may demonstrate the lack of power to show such differences due to the single-center nature of the study. However, it may also suggest some degree of heterogeneity in risk based on the underlying disease diagnosis. Even though we included other ILD diagnoses with IPF in this study, outcomes in end-stage fibrotic lung disease that is not IPF has been demonstrated to be comparable [3-6].
As there are fewer donor organs available than patients in need of lung transplant, continued efforts to best identify those who will most benefit from LT are imperative. The GAP model is a user-friendly tool to help prognosticate those in most need of LT. The LAS will likely evolve over the ensuing years to best deliver donor organs to those in most need, who are also most likely to benefit. This study is the first to compare LAS to a disease-specific model that has been used to prognosticate in patients with ILD/IPF, and emphasizes the need for larger studies focused on posttransplant outcomes in ILD/IPF.