Peak post-transplant lung function in bilateral lung transplant recipients using a prediction model based on donor and recipient demographic characteristics.

RATIONALE: There are no reliable methods to predict lung function following lung transplantation. We sought to devise a prediction model of peak pulmonary function testing (PFT) post-transplant based on donor and recipient demographic characteristics. METHODS: Single center retrospective analysis of bilateral lung transplant recipients between 2011 and 2015 without evidence of allograft dysfunction in the first year was performed. Peak PFT post-transplant was determined by serially measured FEV1 and FVC. Using the NHANES III equation, donor demographic characteristics were used to calculate predicted lung function. Multivariable linear regression helped determine which donor and recipient characteristics affected peak lung function and identify the discrepancy between donor predicted and recipient observed PFT post-transplant. RESULTS: 146 donor/recipient patients were analyzed. 80 had obstructive lung disease, 66 had restrictive disease. Peak post-transplant FEV1 and FVC was reached in 64.30 ±â€¯48.96 and 78.14 ±â€¯50.68 weeks, respectively. Spirometry values peaked earlier in restrictive lung disease recipients. Higher peak FEV1 was significantly associated with younger donor age, non-African American donor race, male recipient sex, greater recipient height, underlying obstructive lung disease. Greater absolute differences between donor predicted and observed FEV1 were significantly associated with male donor sex, greater donor height, non-African-American donor race, female recipient sex, greater recipient height. CONCLUSIONS: Donor and recipient characteristics can help predict lung function post-transplant. Patients without complications in the first year post-transplant may take greater than one year to achieve peak lung function. Such predictions can help guide clinical decision making in the right setting.

Predictors of Nodal and Metastatic Failure in Early Stage Non-small-cell Lung Cancer After Stereotactic Body Radiation Therapy.

INTRODUCTION/BACKGROUND: Many patients with early stage non-small-cell lung cancer (ES-NSCLC) undergoing stereotactic body radiation therapy (SBRT) develop metastases, which is associated with poor outcomes. We sought to identify factors predictive of metastases after lung SBRT and created a risk stratification tool. MATERIALS AND METHODS: We included 363 patients with ES-NSCLC who received SBRT; the median follow-up was 5.8 years. The following patient and tumor factors were retrospectively analyzed for their association with metastases (defined as nodal and/or distant failure): gender; age; lobe involved; centrality; previous NSCLC; smoking status; gross tumor volume (GTV); T-stage; histology; dose; minimum, maximum, and mean GTV dose; and parenchymal lung failure. A metastasis risk-score linear-model using beta coefficients from a multivariate Cox model was built. RESULTS: A total of 111 (27.3%) of 406 lesions metastasized. GTV and dose were significantly associated with metastases on univariate and multivariate Cox proportional hazards modeling (P < .001 and hazard ratio [HR], 1.02 per mL; P < .05 and HR, 0.99 per Gy, respectively). Histology, T-stage, centrality, lung parenchymal failures, and previous NSCLC were not associated with development of metastasis. A metastasis risk-score model using GTV and prescription dose was built: risk score = (0.01611 × GTV) - (0.00525 × dose [BED10]). Two risk-score cutoffs separating the cohort into low-, medium-, and high-risk subgroups were examined. The risk score identified significant differences in time to metastases between low-, medium-, and high-risk patients (P < .001), with 3-year estimates of 81.1%, 63.8%, and 38%, respectively. CONCLUSION: GTV and radiation dose are associated with time to metastasis and may be used to identify patients at higher risk of metastasis after lung SBRT.