Carcinoembryonic antigen predicts waitlist mortality in lung transplant candidates with idiopathic pulmonary fibrosis

Carcinoembryonic antigen predicts waitlist mortality in lung transplant candidates with... Abstract OBJECTIVES Elevated serum carcinoembryonic antigen (CEA) has been reported in lung transplant candidates with idiopathic pulmonary fibrosis, but its association with waitlist mortality is not known. In this study, we evaluated the ability of the serum CEA level to predict waitlist mortality in these patients. METHODS Fifty-nine patients with idiopathic pulmonary fibrosis who were enrolled as lung transplant candidates between January 2004 and December 2014 were retrospectively reviewed. Serum CEA was measured as part of routine evaluation. RESULTS Thirty-seven of the 59 patients underwent lung transplantation with a median waiting time of 91 days. Twenty-two patients died while on the waitlist. In univariable analysis, 6-min walking distance, lung allocation score and serum CEA level were identified as being significant prognostic factors. We constructed 2 multivariable models using forced vital capacity, CEA and 6-min walking distance (Model 1, concordance index 0.758) and CEA and lung allocation score (Model 2, concordance index 0.689). CEA was independently associated with waitlist mortality in Model 1 [hazard ratio 1.074, 95% confidence interval (CI)_ 1.004–1.137] and in Model 2 (hazard ratio 1.065, 95% CI 1.008–1.126). The cut-off values that best discriminated 30-day mortality and 6-month mortality by receiver-operating characteristic curve analysis were 8.55 ng/ml and 4.50 ng/ml, respectively. CONCLUSIONS There was a significant association between elevated serum CEA and increased risk of mortality in waitlisted transplant candidates with idiopathic pulmonary fibrosis. Idiopathic pulmonary fibrosis , Lung transplant , Tumour markers , Waitlist mortality INTRODUCTION Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive fibrosing interstitial pneumonia of unknown cause that occurs primarily in adults, is limited to the lungs and is associated with the histopathological and/or radiological pattern of usual interstitial pneumonia [1]. Given the progressive and incurable nature of IPF, lung transplantation is often considered in patients with moderate-to-severe disease [2]. The clinical course of IPF is highly variable and unpredictable [3], so predicting the risk of waitlist mortality is a challenge for clinicians. Carcinoembryonic antigen (CEA) is overexpressed in adenocarcinomas of the colon and several other organs, including the pancreas, lung, prostate, urinary bladder, ovary and breast. It is used as a serological marker for malignant tumours [4] and in many lung transplant centres to screen for malignancy in patients who are candidates for lung transplantation [5, 6]. Given the low prevalence of solid organ malignancy in lung transplant candidates and the elevated CEA levels in patients with terminal lung disease, such as IPF, cystic fibrosis and chronic obstructive lung disease, CEA has limited value in predicting solid organ cancer post-transplant [5]. However, patients with IPF have higher CEA levels than those with other types of terminal lung disease [5], and a correlation of IPF severity with the serum CEA level has been reported [7]. We hypothesized that the CEA level would be related to the severity and/or activity of IPF and could predict waitlist mortality in lung transplant candidates with IPF. The CEA level was measured routinely at our institution until 2014. Therefore, we conducted this study to determine whether there was a correlation between the CEA level and waitlist mortality. MATERIALS AND METHODS Patients This single-institution retrospective study was conducted at the Yonsei University College of Medicine with the approval of its institutional review board (No. 4-2017-1106). The requirement for informed consent was waived. Patients who were evaluated and listed for lung transplantation between January 2004 and December 2014 were reviewed to identify those with IPF. The diagnosis of IPF was made according to the guidelines [1, 8]. Data collection Clinical data were collected from electronic medical records. At our institution, patients being considered for lung transplantation are admitted for evaluation. During the study period, serum CEA was routinely measured to screen for occult malignancy at the time of evaluation for lung transplantation. Pulmonary hypertension was defined as a mean pulmonary arterial pressure ≥25 mmHg using right heart catheterization or a systolic pulmonary arterial pressure ≥40 mmHg using echocardiography. The 6-min walking distance (6MWD) was measured in all patients. If patients were not able to walk because of respiratory failure at the time of evaluation, the 6MWD was regarded as 0. The Gender–Age–Physiology (GAP) stage was defined as previously described [9]. The lung allocation score (LAS) was calculated based on the patient’s condition at the time of lung transplant evaluation in July 2017 [10]. Survival was measured from the date of evaluation for lung transplantation to the date of death. Patients who underwent lung transplantation were censored on the date of their operation. Waiting time was measured from the date of evaluation for lung transplantation to the date of the operation. Statistical analysis The statistical analyses were performed using SPSS version 18.0 (IBM Corp., Armonk, NY, USA) and R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were compared using the Student’s t-test or the Mann–Whitney U-test. Categorical variables were compared using the χ2 test or Fisher’s exact test. The maxstat package for maximally selected rank statistics was used to detect the optimal cut-off of CEA for waitlist mortality [11]. Using this package, the cut-off that best separates patient outcomes according to a maximum relative risk and a minimum P-value is chosen. This P-value is adjusted to account for the problem of multiple testing [12]. The survival analysis was performed using the Kaplan–Meier method, and comparisons were made using the log-rank test. The Cox proportional hazards analysis was performed to identify predictors of waitlist mortality. Variables with a P-value ≤0.1 were included in multivariable analysis. Because the LAS is calculated by a multivariable model using various clinical variables including age, sex, pulmonary function test results and 6MWD, 2 multivariable models were constructed separately, i.e. a model without the LAS (Model 1) and a model that included the LAS (Model 2). The survcomp package was used for calculation of the concordance indices for each multivariable model [13]. The optimal CEA for discriminative accuracy in predicting waitlist mortality was assessed by constructing receiver-operating characteristic (ROC) curves using the pROC package. P-values less than 0.05 were considered statistically significant. RESULTS Fifty-nine patients of mean age 56.4 ± 7.6 years were included in the study. Forty-one (69.5%) patients were men. Forty-four (74.6%) patients had secondary pulmonary hypertension and 4 (6.8%) were dependent on mechanical ventilation. The mean LAS was 48.3 ± 10.0, and the mean CEA level was 8.0 ± 7.1 ng/ml (Table 1). Using maximally selected rank statistics, the optimal cut-off value for CEA was 4.05 ng/ml. The patients were divided into a low-CEA group (≤4.05 ng/ml, n = 20, 33.9%) and a high-CEA group (>4.05 ng/ml, n = 39, 66.1%). According to the Kaplan–Meier survival model, survival was poorer in the high-CEA group than in the low-CEA group (P = 0.007; Fig. 1). Table 1: Baseline characteristics of patients according to CEA level Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 a Available in 43 patients (17 with low CEA and 26 with high CEA). b Available in 55 patients (19 with low CEA group and 36 with high CEA). c Available in 34 patients (13 with low CEA and 21 with high CEA). BMI: body mass index; CEA: carcinoembryonic antigen; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; IQR: interquartile range; LAS: lung allocation score; PAP: pulmonary artery pressure; SD: standard deviation. Table 1: Baseline characteristics of patients according to CEA level Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 a Available in 43 patients (17 with low CEA and 26 with high CEA). b Available in 55 patients (19 with low CEA group and 36 with high CEA). c Available in 34 patients (13 with low CEA and 21 with high CEA). BMI: body mass index; CEA: carcinoembryonic antigen; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; IQR: interquartile range; LAS: lung allocation score; PAP: pulmonary artery pressure; SD: standard deviation. Figure 1: View largeDownload slide Survival curves according to CEA level. CEA: carcinoembryonic antigen. Figure 1: View largeDownload slide Survival curves according to CEA level. CEA: carcinoembryonic antigen. No variable except for the CEA level was identified as being significantly different between the low-CEA group and the high-CEA group. The 6MWD was longer in the low-CEA group than in the high-CEA group but not significantly so [low CEA, median 165 m (interquartile range, IQR 47.5–235 m) versus high CEA, median 80 m (IQR 40–160 m); P = 0.059]. More patients in the high CEA group died before transplantation [low CEA, 3 (15.0%) versus high CEA, 19 (48.7%); P = 0.010, Table 1]. Among the patients who underwent lung transplantation, the waiting time was not significantly different [low CEA, median 120 days (IQR 23–303 days) versus high CEA, median 89 days (IQR 38.5–191 days); P = 0.784]. We performed a univariable Cox proportional hazards analysis to identify predictors of waitlist mortality (Table 2). The CEA, 6MWD and LAS were significantly associated with mortality. We constructed 2 multivariable models using forced vital capacity (FVC), CEA and 6MWD for Model 1 [concordance index 0.758, 95% confidence interval (CI) 0.653–0.863] and the CEA and LAS for Model 2 (concordance index 0.689, 95% CI 0.556–0.822). Forced expiratory volume in 1 s was not included in Model 1 because of its correlation with FVC (Pearson correlation coefficient 0.955, P <0.001). CEA was independently associated with waitlist mortality in Model 1 [hazard ratio (HR) 1.074, 95% CI 1.014–1.137] and Model 2 (HR 1.065, 95% CI 1.008–1.126). In the ROC curve analysis, the cut-off values for 30-day mortality and 6-month mortality were 8.55 (95% CI 4.50–18.20) and 4.50 (95% CI 3.85–17.60), respectively (Fig. 2). Table 2: Prognostic factors predicting mortality on waitlist using a Cox regression model Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 6MWD: 6-min walk distance; BMI: body mass index; C index: concordance index; CEA: carcinoembryonic antigen; CI: confidence interval; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; HR: hazard ratio; LAS: lung allocation score. Table 2: Prognostic factors predicting mortality on waitlist using a Cox regression model Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 6MWD: 6-min walk distance; BMI: body mass index; C index: concordance index; CEA: carcinoembryonic antigen; CI: confidence interval; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; HR: hazard ratio; LAS: lung allocation score. Figure 2: View largeDownload slide Receiver-operating characteristic curve analysis for (A) 30-day waitlist mortality and (B) 6-month waitlist mortality. AUC: area under the curve; CI: confidence interval. Figure 2: View largeDownload slide Receiver-operating characteristic curve analysis for (A) 30-day waitlist mortality and (B) 6-month waitlist mortality. AUC: area under the curve; CI: confidence interval. DISCUSSION Vancheri et al. [14, 15] reviewed the characteristics and pathogenesis of IPF, noted its biological similarity to that of cancer and proposed a concept of IPF as a cancer-like disease. Nintedanib, a tyrosine kinase inhibitor, was first developed as a treatment for lung cancer but has since been found to attenuate the decline in pulmonary function in patients with IPF [14, 16]. Thus, we were able to identify a new application for a tumour marker such as CEA, i.e. as a prognostic marker of IPF. Even though CEA may be elevated in patients with terminal lung diseases other than cancer, patients with IPF tend to have higher CEA levels than those with other types of terminal lung disease [5]. In a study of 41 patients with IPF, Fahim et al. [7] reported that the CEA level was correlated with poor lung function and with the extent of fibrosis seen on high-resolution computed tomography. In that study, the authors demonstrated expression of CEA in metaplastic cuboidal epithelium lining the respiratory bronchioles and in metaplastic epithelial cells lining honeycomb cysts in one of their patients and speculated that these cells were a possible source of CEA. Moreover, Rusanov et al. [6] reported that levels of other tumour markers (CA15-3, CA19-9 and CA 15-3) were higher in lung transplant candidates with IPF than in their counterparts with chronic obstructive lung disease. To the best of our knowledge, this is the first study to demonstrate the prognostic value of serum CEA in predicting mortality in patients with IPF. In this study, there was a relationship between the CEA level and the risk of waitlist mortality. The cut-off values for 30-day and 6-month mortality were 8.55 ng/dl and 4.5 ng/dl, respectively. However, the lung transplant procedure followed in Korea should be borne in mind when interpreting these findings. In Korea, lung transplant candidates seem to be referred to lung transplant centres later than in other countries [17]; the donor lung allocation system is urgency based, so the waiting time is a less important factor. Therefore, the lung transplant candidates in this study had a relatively high LAS (mean 48.3 ± 10.0) and a low 6MWD [median 100 m (IQR 47.5–235 m)]. In total, 37.3% of the candidates died before transplant, and those who proceeded to a lung transplant had a relatively short waiting time [median 91 days (IQR 36–227)]. The 6MWD is known to be a significant prognostic factor both in lung transplant candidates and in patients with IPF [18–20]. Recently, Castleberry et al. [18] analysed 12 298 lung transplant candidates using the Organ Procurement and Transplantation Network/United Network for Organ Sharing transplantation database and excluding patients with ‘0’ as their 6MWD result. In that study, ROC curve analysis showed that the optimal 6MWD cut-off values for 30-day and 6-month waitlist mortality were 167.6 m [area under the curve (AUC) 0.812] and 199.6 m (AUC 0.673), respectively. In a subgroup analysis of patients with IPF, the cut-off value for the 6MWD based on 30-day waitlist mortality was 233.1 m (AUC 0.712). Even though approximately 75% of our patients already had a 6MWD lower than the cut-off value for 30-day mortality reported for patients with IPF in the previous study [18], the 6MWD had significant prognostic value in the univariable Cox regression analysis in our study (HR 0.992, 95% CI 0.987–0.998). Its statistical significance was lost in the multivariable model (Model 1; HR 0.995, 95% CI 0.990–1.000). LAS has been used in the USA since 2005 and is calculated using various clinical data as the net transplant benefit measure (post-transplant survival minus waitlist survival) minus the medical urgency measure (waitlist survival). By counting waitlist survival twice in the calculation, the LAS gives relatively more weight to transplant urgency than to net transplant benefit [21]. Although the LAS was developed for all lung transplant candidates, its correlation with waitlist mortality has been reported in patients with IPF. Fisher et al. [22] found that the LAS was a significant prognostic factor in 302 patients with IPF (HR 1.06, 95% CI 1.04–1.08). The LAS was also a significant prognostic factor in univariable analysis in the present study (HR 1.051, 95% CI 1.010–1.093). However, its statistical significance was lost in the multivariable model (Model 2; HR 1.037, 95% CI 0.996–1.080). Lung transplant candidates with IPF already have very poor pulmonary function tests, and some are too sick to even perform these tests; because of this ‘ceiling effect’, pulmonary function tests might have limited prognostic value in lung transplant candidates with IPF [20]. In the present study, 6.8% of patients could not perform pulmonary tests for FVC or forced expiratory volume in 1 s, and 42.4% could not perform the test for diffusing capacity of the lung for carbon monoxide (DLCO) at the time of evaluation for lung transplant. Lederer et al. compared the 6MWD and FVC as predictors of 6-month mortality in 454 lung transplant candidates listed with the United Network for Organ Sharing. The AUCs for FVC percent predicted and the 6MWD were 0.59 (0.49–0.69) and 0.73 (0.66–0.81), respectively, and the difference was significant (P = 0.02) [20]. Similarly, the GAP stage, which is calculated using 4 variables (gender, age, FVC and diffusing capacity of the lung for carbon monoxide), does not seem to be a suitable predictor of waitlist mortality in lung transplant candidates with IPF [9]. Most candidates for lung transplantation are younger than 60 years, and their GAP stage depends on their FVC and DLCO. Ikezoe et al. investigated the value of the GAP stage as a predictor of waitlist mortality in 33 lung transplant candidates with IPF in Japan. The GAP stage was not a significant predictor in univariable analysis (the GAP Stage II or III, HR 2.70, 95% CI 0.76–9.66; P = 0.13), and this result is consistent with our present findings [23]. Limitations Our study had several limitations. First, it had a retrospective design and a small sample size. The LAS was calculated retrospectively using medical records, so cannot be compared directly with that in the study from the USA. Second, this single-centre study was conducted at a tertiary hospital in Korea, and all patients included were Asian, so it is inevitable that the results would have been influenced by donor allocation policy in Korea. Therefore, our findings need to be confirmed in larger cohorts that include patients of different ethnicities and with countries of origin. Third, the CEA level was measured only once at the time of evaluation for lung transplant, so we have no data on serial changes in CEA levels during the wait time. Fourth, other tumour markers that could potentially be related to the prognosis of IPF were not routinely measured. CONCLUSION In conclusion, we have shown that the CEA is a possible predictor of waitlist mortality in lung transplant candidates with IPF. Patients with IPF and an elevated CEA level should be monitored carefully. We hope that further studies will be conducted to evaluate additional tumour markers as predictors of waitlist mortality in larger and more diverse groups of patients with IPF. ACKNOWLEDGEMENTS The authors are grateful to Anes Kim, BSN, for her valuable contribution to the collection of data for this research. Conflict of interest: none declared. REFERENCES 1 Raghu G , Collard HR , Egan JJ , Martinez FJ , Behr J , Brown KK et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management . Am J Respir Crit Care Med 2011 ; 183 : 788 – 824 . Google Scholar CrossRef Search ADS PubMed 2 Raghu G , Rochwerg B , Zhang Y , Garcia CA , Azuma A , Behr J et al. An official ATS/ERS/JRS/ALAT clinical practice guideline: treatment of idiopathic pulmonary fibrosis. An update of the 2011 clinical practice guideline . Am J Respir Crit Care Med 2015 ; 192 : e3 – 19 . Google Scholar CrossRef Search ADS PubMed 3 Kim DS , Collard HR , King TE Jr. Classification and natural history of the idiopathic interstitial pneumonias . Proc Am Thorac Soc 2006 ; 3 : 285 – 92 . Google Scholar CrossRef Search ADS PubMed 4 Ishizaka N , Ishizaka Y , Toda E , Koike K , Yamakado M , Nagai R. Are serum carcinoembryonic antigen levels associated with carotid atherosclerosis in Japanese men? Arterioscler Thromb Vasc Biol 2008 ; 28 : 160 – 5 . Google Scholar CrossRef Search ADS PubMed 5 Hadjiliadis D , Tapson VF , Davis RD , Palmer SM. Prognostic value of serum carcinoembryonic antigen levels in patients who undergo lung transplantation . J Heart Lung Transplant 2001 ; 20 : 1305 – 9 . Google Scholar CrossRef Search ADS PubMed 6 Rusanov V , Kramer MR , Raviv Y , Medalion B , Guber A , Shitrit D. The significance of elevated tumor markers among patients with idiopathic pulmonary fibrosis before and after lung transplantation . Chest 2012 ; 141 : 1047 – 54 . Google Scholar CrossRef Search ADS PubMed 7 Fahim A , Crooks MG , Wilmot R , Campbell AP , Morice AH , Hart SP. Serum carcinoembryonic antigen correlates with severity of idiopathic pulmonary fibrosis . Respirology 2012 ; 17 : 1247 – 52 . Google Scholar CrossRef Search ADS PubMed 8 American Thoracic Society. Idiopathic pulmonary fibrosis: diagnosis and treatment. International consensus statement. American Thoracic Society (ATS), and the European Respiratory Society (ERS) . Am J Respir Crit Care Med 2000 ; 161 : 646 – 64 . CrossRef Search ADS PubMed 9 Ley B , Ryerson CJ , Vittinghoff E , Ryu JH , Tomassetti S , Lee JS et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis . Ann Intern Med 2012 ; 156 : 684 – 91 . Google Scholar CrossRef Search ADS PubMed 10 Organ Procurement and Transplantation Network. LAS calculator. https://optn.transplant.hrsa.gov/resources/allocation-calculators/las-calculator/ (10 April 2018, date last accessed). 11 Lausen B , Schumacher M. Maximally selected rank statistics . Biometrics 1992 ; 48 : 73 – 85 . Google Scholar CrossRef Search ADS 12 Yuan J , Zhou J , Dong Z , Tandon S , Kuk D , Panageas KS et al. Pretreatment serum VEGF is associated with clinical response and overall survival in advanced melanoma patients treated with ipilimumab . Cancer Immunol Res 2014 ; 2 : 127 – 32 . Google Scholar CrossRef Search ADS PubMed 13 Schroder MS , Culhane AC , Quackenbush J , Haibe-Kains B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models . Bioinformatics 2011 ; 27 : 3206 – 8 . Google Scholar CrossRef Search ADS PubMed 14 Vancheri C. Idiopathic pulmonary fibrosis and cancer: do they really look similar? BMC Med 2015 ; 13 : 220. Google Scholar CrossRef Search ADS PubMed 15 Vancheri C , Failla M , Crimi N , Raghu G. Idiopathic pulmonary fibrosis: a disease with similarities and links to cancer biology . Eur Respir J 2010 ; 35 : 496 – 504 . Google Scholar CrossRef Search ADS PubMed 16 Richeldi L , Du Bois RM , Raghu G , Azuma A , Brown KK , Costabel U et al. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis . N Engl J Med 2014 ; 370 : 2071 – 82 . Google Scholar CrossRef Search ADS PubMed 17 Yu WS , Paik HC , Haam SJ , Lee CY , Nam KS , Jung HS et al. Transition to routine use of venoarterial extracorporeal oxygenation during lung transplantation could improve early outcomes . J Thorac Dis 2016 ; 8 : 1712 – 20 . Google Scholar CrossRef Search ADS PubMed 18 Castleberry A , Mulvihill MS , Yerokun BA , Gulack BC , Englum B , Snyder L et al. The utility of 6-minute walk distance in predicting waitlist mortality for lung transplant candidates . J Heart Lung Transplant 2017 ; 36 : 780 – 6 . Google Scholar CrossRef Search ADS PubMed 19 Du Bois RM , Albera C , Bradford WZ , Costabel U , Leff JA , Noble PW et al. 6-minute walk distance is an independent predictor of mortality in patients with idiopathic pulmonary fibrosis . Eur Respir J 2014 ; 43 : 1421 – 9 . Google Scholar CrossRef Search ADS PubMed 20 Lederer DJ , Arcasoy SM , Wilt JS , D’Ovidio F , Sonett JR , Kawut SM. Six-minute-walk distance predicts waiting list survival in idiopathic pulmonary fibrosis . Am J Respir Crit Care Med 2006 ; 174 : 659 – 64 . Google Scholar CrossRef Search ADS PubMed 21 Eberlein M , Garrity ER , Orens JB. Lung allocation in the United States . Clin Chest Med 2011 ; 32 : 213 – 22 . Google Scholar CrossRef Search ADS PubMed 22 Fisher JH , Al-Hejaili F , Kandel S , Hirji A , Shapera S , Mura M. Multi-dimensional scores to predict mortality in patients with idiopathic pulmonary fibrosis undergoing lung transplantation assessment . Respir Med 2017 ; 125 : 65 – 71 . Google Scholar CrossRef Search ADS PubMed 23 Ikezoe K , Handa T , Tanizawa K , Chen-Yoshikawa TF , Kubo T , Aoyama A et al. Prognostic factors and outcomes in Japanese lung transplant candidates with interstitial lung disease . PLoS One 2017 ; 12 : e0183171. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Cardio-Thoracic Surgery Oxford University Press

Carcinoembryonic antigen predicts waitlist mortality in lung transplant candidates with idiopathic pulmonary fibrosis

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
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1010-7940
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1873-734X
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10.1093/ejcts/ezy170
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Abstract

Abstract OBJECTIVES Elevated serum carcinoembryonic antigen (CEA) has been reported in lung transplant candidates with idiopathic pulmonary fibrosis, but its association with waitlist mortality is not known. In this study, we evaluated the ability of the serum CEA level to predict waitlist mortality in these patients. METHODS Fifty-nine patients with idiopathic pulmonary fibrosis who were enrolled as lung transplant candidates between January 2004 and December 2014 were retrospectively reviewed. Serum CEA was measured as part of routine evaluation. RESULTS Thirty-seven of the 59 patients underwent lung transplantation with a median waiting time of 91 days. Twenty-two patients died while on the waitlist. In univariable analysis, 6-min walking distance, lung allocation score and serum CEA level were identified as being significant prognostic factors. We constructed 2 multivariable models using forced vital capacity, CEA and 6-min walking distance (Model 1, concordance index 0.758) and CEA and lung allocation score (Model 2, concordance index 0.689). CEA was independently associated with waitlist mortality in Model 1 [hazard ratio 1.074, 95% confidence interval (CI)_ 1.004–1.137] and in Model 2 (hazard ratio 1.065, 95% CI 1.008–1.126). The cut-off values that best discriminated 30-day mortality and 6-month mortality by receiver-operating characteristic curve analysis were 8.55 ng/ml and 4.50 ng/ml, respectively. CONCLUSIONS There was a significant association between elevated serum CEA and increased risk of mortality in waitlisted transplant candidates with idiopathic pulmonary fibrosis. Idiopathic pulmonary fibrosis , Lung transplant , Tumour markers , Waitlist mortality INTRODUCTION Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive fibrosing interstitial pneumonia of unknown cause that occurs primarily in adults, is limited to the lungs and is associated with the histopathological and/or radiological pattern of usual interstitial pneumonia [1]. Given the progressive and incurable nature of IPF, lung transplantation is often considered in patients with moderate-to-severe disease [2]. The clinical course of IPF is highly variable and unpredictable [3], so predicting the risk of waitlist mortality is a challenge for clinicians. Carcinoembryonic antigen (CEA) is overexpressed in adenocarcinomas of the colon and several other organs, including the pancreas, lung, prostate, urinary bladder, ovary and breast. It is used as a serological marker for malignant tumours [4] and in many lung transplant centres to screen for malignancy in patients who are candidates for lung transplantation [5, 6]. Given the low prevalence of solid organ malignancy in lung transplant candidates and the elevated CEA levels in patients with terminal lung disease, such as IPF, cystic fibrosis and chronic obstructive lung disease, CEA has limited value in predicting solid organ cancer post-transplant [5]. However, patients with IPF have higher CEA levels than those with other types of terminal lung disease [5], and a correlation of IPF severity with the serum CEA level has been reported [7]. We hypothesized that the CEA level would be related to the severity and/or activity of IPF and could predict waitlist mortality in lung transplant candidates with IPF. The CEA level was measured routinely at our institution until 2014. Therefore, we conducted this study to determine whether there was a correlation between the CEA level and waitlist mortality. MATERIALS AND METHODS Patients This single-institution retrospective study was conducted at the Yonsei University College of Medicine with the approval of its institutional review board (No. 4-2017-1106). The requirement for informed consent was waived. Patients who were evaluated and listed for lung transplantation between January 2004 and December 2014 were reviewed to identify those with IPF. The diagnosis of IPF was made according to the guidelines [1, 8]. Data collection Clinical data were collected from electronic medical records. At our institution, patients being considered for lung transplantation are admitted for evaluation. During the study period, serum CEA was routinely measured to screen for occult malignancy at the time of evaluation for lung transplantation. Pulmonary hypertension was defined as a mean pulmonary arterial pressure ≥25 mmHg using right heart catheterization or a systolic pulmonary arterial pressure ≥40 mmHg using echocardiography. The 6-min walking distance (6MWD) was measured in all patients. If patients were not able to walk because of respiratory failure at the time of evaluation, the 6MWD was regarded as 0. The Gender–Age–Physiology (GAP) stage was defined as previously described [9]. The lung allocation score (LAS) was calculated based on the patient’s condition at the time of lung transplant evaluation in July 2017 [10]. Survival was measured from the date of evaluation for lung transplantation to the date of death. Patients who underwent lung transplantation were censored on the date of their operation. Waiting time was measured from the date of evaluation for lung transplantation to the date of the operation. Statistical analysis The statistical analyses were performed using SPSS version 18.0 (IBM Corp., Armonk, NY, USA) and R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were compared using the Student’s t-test or the Mann–Whitney U-test. Categorical variables were compared using the χ2 test or Fisher’s exact test. The maxstat package for maximally selected rank statistics was used to detect the optimal cut-off of CEA for waitlist mortality [11]. Using this package, the cut-off that best separates patient outcomes according to a maximum relative risk and a minimum P-value is chosen. This P-value is adjusted to account for the problem of multiple testing [12]. The survival analysis was performed using the Kaplan–Meier method, and comparisons were made using the log-rank test. The Cox proportional hazards analysis was performed to identify predictors of waitlist mortality. Variables with a P-value ≤0.1 were included in multivariable analysis. Because the LAS is calculated by a multivariable model using various clinical variables including age, sex, pulmonary function test results and 6MWD, 2 multivariable models were constructed separately, i.e. a model without the LAS (Model 1) and a model that included the LAS (Model 2). The survcomp package was used for calculation of the concordance indices for each multivariable model [13]. The optimal CEA for discriminative accuracy in predicting waitlist mortality was assessed by constructing receiver-operating characteristic (ROC) curves using the pROC package. P-values less than 0.05 were considered statistically significant. RESULTS Fifty-nine patients of mean age 56.4 ± 7.6 years were included in the study. Forty-one (69.5%) patients were men. Forty-four (74.6%) patients had secondary pulmonary hypertension and 4 (6.8%) were dependent on mechanical ventilation. The mean LAS was 48.3 ± 10.0, and the mean CEA level was 8.0 ± 7.1 ng/ml (Table 1). Using maximally selected rank statistics, the optimal cut-off value for CEA was 4.05 ng/ml. The patients were divided into a low-CEA group (≤4.05 ng/ml, n = 20, 33.9%) and a high-CEA group (>4.05 ng/ml, n = 39, 66.1%). According to the Kaplan–Meier survival model, survival was poorer in the high-CEA group than in the low-CEA group (P = 0.007; Fig. 1). Table 1: Baseline characteristics of patients according to CEA level Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 a Available in 43 patients (17 with low CEA and 26 with high CEA). b Available in 55 patients (19 with low CEA group and 36 with high CEA). c Available in 34 patients (13 with low CEA and 21 with high CEA). BMI: body mass index; CEA: carcinoembryonic antigen; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; IQR: interquartile range; LAS: lung allocation score; PAP: pulmonary artery pressure; SD: standard deviation. Table 1: Baseline characteristics of patients according to CEA level Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 Variables Total (n = 59) Low CEA (≤4.05) (n = 20) High CEA (>4.05) (n = 39) P-value Age (years), mean ± SD 56.4 ± 7.6 56.0 ± 8.8 56.7 ± 7.0 0.736 Male sex, n (%) 41 (69.5) 14 (70.0) 27 (69.2) >0.999 BMI, mean ± SD 21.94 ± 3.64 20.97 ± 3.71 22.44 ± 3.55 0.144 Pulmonary hypertension, n (%) 44 (74.6) 14 (70.0) 30 (76.9) 0.563 Mechanical ventilation, n (%) 4 (6.8) 1 (5.0) 3 (7.7) >0.999 Hospitalization, n (%) 12 (20.3) 2 (10.0) 10 (25.6) 0.192 pCO2 (mmHg), mean ± SD 40.2 ± 11.2 40.7 ± 7.2 39.9 ± 12.8 0.811 Creatinine (mg/dl), mean ± SD 0.7 ± 0.1 0.7 ± 0.2 0.7 ± 0.1 0.469 Total bilirubin (mg/dl), mean ± SD 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.2 0.920 CEA (ng/ml), mean ± SD 8.0 ± 7.1 2.5 ± 0.9 10.8 ± 7.2 <0.001 Mean PAP (mmHg),a mean ± SD 33.1 ± 12.1 30.2 ± 10.6 35.1 ± 12.83 0.200 FVC percent predicted,b mean ± SD 44.7 ± 13.6 41 ± 13 46 ± 13 0.229 FEV1 percent predicted,b mean ± SD 53.2 ± 16.2 49 ± 15 55 ± 16 0.190 DLCO percent predicted,c mean ± SD 37.0 ± 28.5 35 ± 12 38 ± 35 0.756 DLCO could not performed, n (%) 25 (42.4) 7 (35.0) 18 (46.2) 0.412 6-Min walking distance (m), median (IQR) 100 (47.5–235) 165 (70–278) 80 (40–160) 0.059 GAP stage, n (%) 0.812  1 9 (15.3) 4 (20.0) 5 (12.8)  2 29 (49.2) 9 (45.0) 20 (51.3)  3 21 (35.6) 7 (35.0) 14 (35.9) LAS, mean ± SD 48.3 ± 10.0 46.3 ± 9.9 49.4 ± 10.0 0.261 Death before transplantation, n (%) 22 (37.3) 3 (15.0) 19 (48.7) 0.010 Transplantation, n (%) 37 (62.7) 17 (85.0) 20 (51.3) 0.010 Waiting time, median (IQR) 91 (36–227) 120 (23–303) 89 (38.5–191) 0.784 a Available in 43 patients (17 with low CEA and 26 with high CEA). b Available in 55 patients (19 with low CEA group and 36 with high CEA). c Available in 34 patients (13 with low CEA and 21 with high CEA). BMI: body mass index; CEA: carcinoembryonic antigen; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; IQR: interquartile range; LAS: lung allocation score; PAP: pulmonary artery pressure; SD: standard deviation. Figure 1: View largeDownload slide Survival curves according to CEA level. CEA: carcinoembryonic antigen. Figure 1: View largeDownload slide Survival curves according to CEA level. CEA: carcinoembryonic antigen. No variable except for the CEA level was identified as being significantly different between the low-CEA group and the high-CEA group. The 6MWD was longer in the low-CEA group than in the high-CEA group but not significantly so [low CEA, median 165 m (interquartile range, IQR 47.5–235 m) versus high CEA, median 80 m (IQR 40–160 m); P = 0.059]. More patients in the high CEA group died before transplantation [low CEA, 3 (15.0%) versus high CEA, 19 (48.7%); P = 0.010, Table 1]. Among the patients who underwent lung transplantation, the waiting time was not significantly different [low CEA, median 120 days (IQR 23–303 days) versus high CEA, median 89 days (IQR 38.5–191 days); P = 0.784]. We performed a univariable Cox proportional hazards analysis to identify predictors of waitlist mortality (Table 2). The CEA, 6MWD and LAS were significantly associated with mortality. We constructed 2 multivariable models using forced vital capacity (FVC), CEA and 6MWD for Model 1 [concordance index 0.758, 95% confidence interval (CI) 0.653–0.863] and the CEA and LAS for Model 2 (concordance index 0.689, 95% CI 0.556–0.822). Forced expiratory volume in 1 s was not included in Model 1 because of its correlation with FVC (Pearson correlation coefficient 0.955, P <0.001). CEA was independently associated with waitlist mortality in Model 1 [hazard ratio (HR) 1.074, 95% CI 1.014–1.137] and Model 2 (HR 1.065, 95% CI 1.008–1.126). In the ROC curve analysis, the cut-off values for 30-day mortality and 6-month mortality were 8.55 (95% CI 4.50–18.20) and 4.50 (95% CI 3.85–17.60), respectively (Fig. 2). Table 2: Prognostic factors predicting mortality on waitlist using a Cox regression model Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 6MWD: 6-min walk distance; BMI: body mass index; C index: concordance index; CEA: carcinoembryonic antigen; CI: confidence interval; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; HR: hazard ratio; LAS: lung allocation score. Table 2: Prognostic factors predicting mortality on waitlist using a Cox regression model Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 Variables n HR 95% CI P-value C index 95% CI Male sex 59 0.801 0.339–1.891 0.612 Age (years) 59 1.018 0.961–1.079 0.539 BMI 59 0.974 0.874–1.086 0.636 pCO2 (mmHg) 59 1.016 0.975–1.059 0.445 CEA (ng/ml) 59 1.075 1.020–1.113 0.007 FVC, percent predicted 55 1.038 1.000–1.077 0.052 FEV1, percent predicted 55 1.028 0.996–1.061 0.090 DLCO, percent predicted 34 0.940 0.828–1.068 0.343 6MWD (m) 59 0.992 0.987–0.998 0.004 GAP stage 59  1 Reference  2 1.621 0.477–5.508 0.439  3 1.428 0.378–5.395 0.599 LAS 59 1.051 1.010–1.093 0.013 Model 1 55 0.758 0.653-0.863  FVC, percent predicted 1.034 0.993–1.077 0.101  CEA (ng/ml) 1.074 1.014–1.137 0.014  6MWD (m) 0.995 0.990–1.000 0.064 Model 2 59 0.689 0.556–0.822  CEA (ng/ml) 1.065 1.008–1.126 0.026  LAS 1.037 0.996–1.080 0.081 6MWD: 6-min walk distance; BMI: body mass index; C index: concordance index; CEA: carcinoembryonic antigen; CI: confidence interval; DLCO: diffusing capacity of the lung for carbon monoxide; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; GAP: Gender–Age–Physiology; HR: hazard ratio; LAS: lung allocation score. Figure 2: View largeDownload slide Receiver-operating characteristic curve analysis for (A) 30-day waitlist mortality and (B) 6-month waitlist mortality. AUC: area under the curve; CI: confidence interval. Figure 2: View largeDownload slide Receiver-operating characteristic curve analysis for (A) 30-day waitlist mortality and (B) 6-month waitlist mortality. AUC: area under the curve; CI: confidence interval. DISCUSSION Vancheri et al. [14, 15] reviewed the characteristics and pathogenesis of IPF, noted its biological similarity to that of cancer and proposed a concept of IPF as a cancer-like disease. Nintedanib, a tyrosine kinase inhibitor, was first developed as a treatment for lung cancer but has since been found to attenuate the decline in pulmonary function in patients with IPF [14, 16]. Thus, we were able to identify a new application for a tumour marker such as CEA, i.e. as a prognostic marker of IPF. Even though CEA may be elevated in patients with terminal lung diseases other than cancer, patients with IPF tend to have higher CEA levels than those with other types of terminal lung disease [5]. In a study of 41 patients with IPF, Fahim et al. [7] reported that the CEA level was correlated with poor lung function and with the extent of fibrosis seen on high-resolution computed tomography. In that study, the authors demonstrated expression of CEA in metaplastic cuboidal epithelium lining the respiratory bronchioles and in metaplastic epithelial cells lining honeycomb cysts in one of their patients and speculated that these cells were a possible source of CEA. Moreover, Rusanov et al. [6] reported that levels of other tumour markers (CA15-3, CA19-9 and CA 15-3) were higher in lung transplant candidates with IPF than in their counterparts with chronic obstructive lung disease. To the best of our knowledge, this is the first study to demonstrate the prognostic value of serum CEA in predicting mortality in patients with IPF. In this study, there was a relationship between the CEA level and the risk of waitlist mortality. The cut-off values for 30-day and 6-month mortality were 8.55 ng/dl and 4.5 ng/dl, respectively. However, the lung transplant procedure followed in Korea should be borne in mind when interpreting these findings. In Korea, lung transplant candidates seem to be referred to lung transplant centres later than in other countries [17]; the donor lung allocation system is urgency based, so the waiting time is a less important factor. Therefore, the lung transplant candidates in this study had a relatively high LAS (mean 48.3 ± 10.0) and a low 6MWD [median 100 m (IQR 47.5–235 m)]. In total, 37.3% of the candidates died before transplant, and those who proceeded to a lung transplant had a relatively short waiting time [median 91 days (IQR 36–227)]. The 6MWD is known to be a significant prognostic factor both in lung transplant candidates and in patients with IPF [18–20]. Recently, Castleberry et al. [18] analysed 12 298 lung transplant candidates using the Organ Procurement and Transplantation Network/United Network for Organ Sharing transplantation database and excluding patients with ‘0’ as their 6MWD result. In that study, ROC curve analysis showed that the optimal 6MWD cut-off values for 30-day and 6-month waitlist mortality were 167.6 m [area under the curve (AUC) 0.812] and 199.6 m (AUC 0.673), respectively. In a subgroup analysis of patients with IPF, the cut-off value for the 6MWD based on 30-day waitlist mortality was 233.1 m (AUC 0.712). Even though approximately 75% of our patients already had a 6MWD lower than the cut-off value for 30-day mortality reported for patients with IPF in the previous study [18], the 6MWD had significant prognostic value in the univariable Cox regression analysis in our study (HR 0.992, 95% CI 0.987–0.998). Its statistical significance was lost in the multivariable model (Model 1; HR 0.995, 95% CI 0.990–1.000). LAS has been used in the USA since 2005 and is calculated using various clinical data as the net transplant benefit measure (post-transplant survival minus waitlist survival) minus the medical urgency measure (waitlist survival). By counting waitlist survival twice in the calculation, the LAS gives relatively more weight to transplant urgency than to net transplant benefit [21]. Although the LAS was developed for all lung transplant candidates, its correlation with waitlist mortality has been reported in patients with IPF. Fisher et al. [22] found that the LAS was a significant prognostic factor in 302 patients with IPF (HR 1.06, 95% CI 1.04–1.08). The LAS was also a significant prognostic factor in univariable analysis in the present study (HR 1.051, 95% CI 1.010–1.093). However, its statistical significance was lost in the multivariable model (Model 2; HR 1.037, 95% CI 0.996–1.080). Lung transplant candidates with IPF already have very poor pulmonary function tests, and some are too sick to even perform these tests; because of this ‘ceiling effect’, pulmonary function tests might have limited prognostic value in lung transplant candidates with IPF [20]. In the present study, 6.8% of patients could not perform pulmonary tests for FVC or forced expiratory volume in 1 s, and 42.4% could not perform the test for diffusing capacity of the lung for carbon monoxide (DLCO) at the time of evaluation for lung transplant. Lederer et al. compared the 6MWD and FVC as predictors of 6-month mortality in 454 lung transplant candidates listed with the United Network for Organ Sharing. The AUCs for FVC percent predicted and the 6MWD were 0.59 (0.49–0.69) and 0.73 (0.66–0.81), respectively, and the difference was significant (P = 0.02) [20]. Similarly, the GAP stage, which is calculated using 4 variables (gender, age, FVC and diffusing capacity of the lung for carbon monoxide), does not seem to be a suitable predictor of waitlist mortality in lung transplant candidates with IPF [9]. Most candidates for lung transplantation are younger than 60 years, and their GAP stage depends on their FVC and DLCO. Ikezoe et al. investigated the value of the GAP stage as a predictor of waitlist mortality in 33 lung transplant candidates with IPF in Japan. The GAP stage was not a significant predictor in univariable analysis (the GAP Stage II or III, HR 2.70, 95% CI 0.76–9.66; P = 0.13), and this result is consistent with our present findings [23]. Limitations Our study had several limitations. First, it had a retrospective design and a small sample size. The LAS was calculated retrospectively using medical records, so cannot be compared directly with that in the study from the USA. Second, this single-centre study was conducted at a tertiary hospital in Korea, and all patients included were Asian, so it is inevitable that the results would have been influenced by donor allocation policy in Korea. Therefore, our findings need to be confirmed in larger cohorts that include patients of different ethnicities and with countries of origin. Third, the CEA level was measured only once at the time of evaluation for lung transplant, so we have no data on serial changes in CEA levels during the wait time. Fourth, other tumour markers that could potentially be related to the prognosis of IPF were not routinely measured. CONCLUSION In conclusion, we have shown that the CEA is a possible predictor of waitlist mortality in lung transplant candidates with IPF. Patients with IPF and an elevated CEA level should be monitored carefully. We hope that further studies will be conducted to evaluate additional tumour markers as predictors of waitlist mortality in larger and more diverse groups of patients with IPF. ACKNOWLEDGEMENTS The authors are grateful to Anes Kim, BSN, for her valuable contribution to the collection of data for this research. Conflict of interest: none declared. REFERENCES 1 Raghu G , Collard HR , Egan JJ , Martinez FJ , Behr J , Brown KK et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management . Am J Respir Crit Care Med 2011 ; 183 : 788 – 824 . Google Scholar CrossRef Search ADS PubMed 2 Raghu G , Rochwerg B , Zhang Y , Garcia CA , Azuma A , Behr J et al. An official ATS/ERS/JRS/ALAT clinical practice guideline: treatment of idiopathic pulmonary fibrosis. An update of the 2011 clinical practice guideline . Am J Respir Crit Care Med 2015 ; 192 : e3 – 19 . Google Scholar CrossRef Search ADS PubMed 3 Kim DS , Collard HR , King TE Jr. Classification and natural history of the idiopathic interstitial pneumonias . Proc Am Thorac Soc 2006 ; 3 : 285 – 92 . Google Scholar CrossRef Search ADS PubMed 4 Ishizaka N , Ishizaka Y , Toda E , Koike K , Yamakado M , Nagai R. Are serum carcinoembryonic antigen levels associated with carotid atherosclerosis in Japanese men? Arterioscler Thromb Vasc Biol 2008 ; 28 : 160 – 5 . Google Scholar CrossRef Search ADS PubMed 5 Hadjiliadis D , Tapson VF , Davis RD , Palmer SM. Prognostic value of serum carcinoembryonic antigen levels in patients who undergo lung transplantation . J Heart Lung Transplant 2001 ; 20 : 1305 – 9 . Google Scholar CrossRef Search ADS PubMed 6 Rusanov V , Kramer MR , Raviv Y , Medalion B , Guber A , Shitrit D. The significance of elevated tumor markers among patients with idiopathic pulmonary fibrosis before and after lung transplantation . Chest 2012 ; 141 : 1047 – 54 . Google Scholar CrossRef Search ADS PubMed 7 Fahim A , Crooks MG , Wilmot R , Campbell AP , Morice AH , Hart SP. Serum carcinoembryonic antigen correlates with severity of idiopathic pulmonary fibrosis . Respirology 2012 ; 17 : 1247 – 52 . Google Scholar CrossRef Search ADS PubMed 8 American Thoracic Society. Idiopathic pulmonary fibrosis: diagnosis and treatment. International consensus statement. American Thoracic Society (ATS), and the European Respiratory Society (ERS) . Am J Respir Crit Care Med 2000 ; 161 : 646 – 64 . CrossRef Search ADS PubMed 9 Ley B , Ryerson CJ , Vittinghoff E , Ryu JH , Tomassetti S , Lee JS et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis . Ann Intern Med 2012 ; 156 : 684 – 91 . Google Scholar CrossRef Search ADS PubMed 10 Organ Procurement and Transplantation Network. LAS calculator. https://optn.transplant.hrsa.gov/resources/allocation-calculators/las-calculator/ (10 April 2018, date last accessed). 11 Lausen B , Schumacher M. Maximally selected rank statistics . Biometrics 1992 ; 48 : 73 – 85 . Google Scholar CrossRef Search ADS 12 Yuan J , Zhou J , Dong Z , Tandon S , Kuk D , Panageas KS et al. Pretreatment serum VEGF is associated with clinical response and overall survival in advanced melanoma patients treated with ipilimumab . Cancer Immunol Res 2014 ; 2 : 127 – 32 . Google Scholar CrossRef Search ADS PubMed 13 Schroder MS , Culhane AC , Quackenbush J , Haibe-Kains B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models . Bioinformatics 2011 ; 27 : 3206 – 8 . Google Scholar CrossRef Search ADS PubMed 14 Vancheri C. Idiopathic pulmonary fibrosis and cancer: do they really look similar? BMC Med 2015 ; 13 : 220. Google Scholar CrossRef Search ADS PubMed 15 Vancheri C , Failla M , Crimi N , Raghu G. Idiopathic pulmonary fibrosis: a disease with similarities and links to cancer biology . Eur Respir J 2010 ; 35 : 496 – 504 . Google Scholar CrossRef Search ADS PubMed 16 Richeldi L , Du Bois RM , Raghu G , Azuma A , Brown KK , Costabel U et al. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis . N Engl J Med 2014 ; 370 : 2071 – 82 . Google Scholar CrossRef Search ADS PubMed 17 Yu WS , Paik HC , Haam SJ , Lee CY , Nam KS , Jung HS et al. Transition to routine use of venoarterial extracorporeal oxygenation during lung transplantation could improve early outcomes . J Thorac Dis 2016 ; 8 : 1712 – 20 . Google Scholar CrossRef Search ADS PubMed 18 Castleberry A , Mulvihill MS , Yerokun BA , Gulack BC , Englum B , Snyder L et al. The utility of 6-minute walk distance in predicting waitlist mortality for lung transplant candidates . J Heart Lung Transplant 2017 ; 36 : 780 – 6 . Google Scholar CrossRef Search ADS PubMed 19 Du Bois RM , Albera C , Bradford WZ , Costabel U , Leff JA , Noble PW et al. 6-minute walk distance is an independent predictor of mortality in patients with idiopathic pulmonary fibrosis . Eur Respir J 2014 ; 43 : 1421 – 9 . Google Scholar CrossRef Search ADS PubMed 20 Lederer DJ , Arcasoy SM , Wilt JS , D’Ovidio F , Sonett JR , Kawut SM. Six-minute-walk distance predicts waiting list survival in idiopathic pulmonary fibrosis . Am J Respir Crit Care Med 2006 ; 174 : 659 – 64 . Google Scholar CrossRef Search ADS PubMed 21 Eberlein M , Garrity ER , Orens JB. Lung allocation in the United States . Clin Chest Med 2011 ; 32 : 213 – 22 . Google Scholar CrossRef Search ADS PubMed 22 Fisher JH , Al-Hejaili F , Kandel S , Hirji A , Shapera S , Mura M. Multi-dimensional scores to predict mortality in patients with idiopathic pulmonary fibrosis undergoing lung transplantation assessment . Respir Med 2017 ; 125 : 65 – 71 . Google Scholar CrossRef Search ADS PubMed 23 Ikezoe K , Handa T , Tanizawa K , Chen-Yoshikawa TF , Kubo T , Aoyama A et al. Prognostic factors and outcomes in Japanese lung transplant candidates with interstitial lung disease . PLoS One 2017 ; 12 : e0183171. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

European Journal of Cardio-Thoracic SurgeryOxford University Press

Published: Apr 17, 2018

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