Journal of Pulmonary Medicine & Respiratory Research Category: Medical Type: Research Article
Comparison of the GAP Model and the Lung Allocation Score in Patients with Idiopathic Pulmonary Fibrosis/Interstitial Lung Disease Undergoing Lung Transplantation
- Leann L Silhan1*, Cheilonda Johnson1, Pali D Shah1, Sonye K Danoff1
- 1 Department Of Medicine, Division Of Pulmonary And Critical Care Medicine, Johns Hopkins University School Of Medicine, Baltimore, United States
*Corresponding Author:Leann L Silhan
Department Of Medicine, Division Of Pulmonary And Critical Care Medicine, Johns Hopkins University School Of Medicine, Baltimore, United States
Received Date: Jun 17, 2016 Accepted Date: Jul 29, 2016 Published Date: Aug 12, 2016
Idiopathic Pulmonary Fibrosis (IPF) and other advanced Interstitial Lung Diseases (ILD) are often fatal without Lung Transplantation (LT). Several models for predicting mortality risk have been developed including the GAP (gender, age, physiology) model. Similarly, the Lung Allocation Score (LAS) has been shown to predict the risk of mortality within the first year posttransplant. We hypothesized that a disease specific mortality model (GAP) might be superior compared to the predictive power of the LAS model for overall mortality within the first year posttransplant in patients with IPF and ILD.
A retrospective analysis of 72 patients with IPF or ILD listed for transplant between March 2005 and September 2013 at a single academic medical center was conducted. Logistic regression models were used to compare the relative contribution and explanatory power of the LAS and GAP for predicting mortality within the first year posttransplant using likelihood ratio chi-square tests (G2) and the area under the Receiver Operator Characteristic (ROC) curve.
Fifty-eight subjects received lung transplant and fourteen were removed from the waiting list. Forty-two subjects survived past the first posttransplant year of the 58 transplanted subjects, six had unavailable data. Fifty-two IPF/ILD subjects were included for analysis. Fourteen (26.9%) of the 52 died within the first posttransplant year. GAP and LAS were poorly correlated (r2=0.033). Neither GAP nor LAS was predictive of early posttransplant mortality in IPF/ILD (C-statistic range 0.62-0.67).
Our data demonstrate poor correlation between GAP and LAS, which may be due to the GAP score being dependent on the diffusion capacity variable which is not included in the LAS. In our dataset, neither correlated well with early posttransplant mortality.
|Variables of the GAP index and LAS|
|Physiology (%FVC and %DLCO)|
|LAS (Lung Allocation Score)|
|Diagnosis (IPF/ILD is diagnosis Group D)|
|FVC (% predicted)|
|Pulmonary artery systolic pressure|
|Oxygen requirement at rest|
|6 minute walk distance|
|Mechanical ventilation or extracorporeal support|
|PCO2 increase by >15% in 6 month period|
DLCO: Diffusion Limitation of Carbon Monoxide
BMI: Body Mass Index
PCO2: Partial Pressure of Carbon Dioxide
The baseline characteristics of the 52 patients listed for lung transplant are shown in table 2. The mean age was 57 years (range: 19-71), 34 (55%) were male, 53 (85%) were white, and 24 (40%) had pulmonary hypertension. The mean FVC % predicted was 46.4% and mean DLCO was 33.4% with 19 patients (37%) being unable to perform DLCO testing; in the 6 excluded subjects, the mean FVC% predicted was 54.1% (range 24-80%) and DLCO was not done. The mean LAS was 46.8 (range: 31.9-92.1) with 14 (26.9%) subjects having an LAS >50, and the mean GAP score was 4.85 (range: 2-8). Forty-one of the 52 patients had IPF (79%) with a mean age of 59.1 years, while 11 had another ILD (21%) with a mean age of 50. The other ILDs consisted of hypersensitivity pneumonitis , NSIP , respiratory bronchiolitis-ILD/desquamative interstitial pneumonia , post-bone marrow transplant lung disease , and constrictive bronchiolitis/BOOP . The 6 excluded subjects had a mean LAS of 60.9 (range 38.9-89.5). Two survivor subjects and four non-survivor subjects did not undergo pre-transplant right heart catheterization.
|Characteristic||All||Survivors (>1 yr)||Non-survivors (died <1yr)||p-value*|
|N=52||N=38 (73.1%)||N=14 (26.9%)|
|Age (range)||57.2 (19-71)||57.8 (30-71)||55.7 (19-69)||0.701|
|Male||29 (55.8%)||19 (50%)||10 (71.4%)||0.168|
|White||44 (85%)||32 (84%)||12 (86%)||0.897|
|Black||5 (10%)||5 (13%)||0 (0%)||0.153|
|Other||3 (5%)||1 (3%)||2 (14%)||0.11|
|GAP index (range)||4.85 (2-8)||4.84 (2-7)||4.85 (2-8)||0.92|
|Lung Allocation Score (LAS)||46.8 (31.9-92.1)||45.2 (31.9-92.1)||51.4 (32.5-91.9)||0.133|
|Double LT||30 (58%)||21 (55%)||9 (64%)||0.559|
|Diagnosis of IPF||41 (79%)||30 (78.9%)||11 (78.5%)||0.977|
|Time on wait list (days)||52.6 (1-412)||45.9 (1-408)||71.0 (1-412)||0.329|
|FVC (L)||1.71 (0.47-3.18)||1.70 (0.47-3.18)||1.71 (0.8-2.83)||0.964|
|% FVC||46.4% (15-85%)||46.1% (16.5-85%)||47.2% (15-75.8%)||0.823|
|% DLCO||33.4% (11.0-61.3%)||34.6% (11.0-59.8%)||30.4% (14.0-61.3%)||0.426|
|% FEV1||53.4% (34-90.9%)||52.2% (34-90.9%)||58.2 (40-86.3)||0.283|
|BMI||26.9 (14-35.1)||27.0 (18.6-35.1)||26.6 (14.0-31.8)||0.793|
|PH||24 (40%)||19/36 (53%)||5/10 (50%)**||0.873|
|RHC||46 (88.5%)||36 (94.7%)||10 (71.4%)||0.02|
FVC: Forced Vital Capacity
DLCO: Diffusion Coefficient of Carbon Monoxide
BMI: Body Mass Index
PH: Pulmonary Hypertension
RHC: Right Heart Catheterization
ILD: Interstitial Lung Disease
HP: Hypersensitivity Pneumonitis
NSIP: Non-Specific Interstitial Pneumonia
RB-ILD: Respiratory Bronchiolitis Interstitial Lung Disease
DIP: Desquamative Interstitial Pneumonia
BM: Bone Marrow
BOOP: Bronchiolitis Obliterans Organizing Pneumonia
** Two of the survivors and 4 non-survivors did not have a right heart catheterization
Data are expressed as number (percent) unless otherwise specified. Data are means with (range)
There was poor correlation between the GAP index and LAS with r2=0.033 (Figure 2).
There were 14 deaths (26.9%) within the first year post-LT, with 38 (73.1%) of patients surviving >1 year post-LT with the median follow-up of 30 months. The GAP index was calculated as near to the listing date as possible, with median time between initial LAS and GAP of 5 weeks (range: 1 day-24 weeks). The difference in time from LAS and GAP is due to time between the most recent pulmonary function testing to the date of listing for lung transplant. Age, race, gender, body mass index, lung function, diagnosis of IPF, double vs. single LT, waitlist time, LAS and GAP index were not significantly different in those who survived or died in the first year (Table 2).
The G2 statistic (likelihood ratio) and p-values for the LAS and GAP Index are summarized in table 3: LAS G2 =1.50 (p=0.22), GAP G2=0.06 (p=0.81). The ROC curves are presented for each prediction score in figures 3a and 3b.
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.
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Citation: Silhan LL, Johnson C, Shah PD, Danoff SK (2016) Comparison of the GAP Model and the Lung Allocation Score in Patients with Idiopathic Pulmonary Fibrosis/Interstitial Lung Disease Undergoing Lung Transplantation. J Pulm Med Respir Res 2: 007.
Copyright: © 2016 Leann L Silhan, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.