Lung transplant (LT) centers are increasingly evaluating patients with multiple risk factors for adverse outcomes. Effects of these stacked risks remains unclear. Our aim was to determine the relationship between number of comorbidities and post-transplant outcomes. We performed a retrospective cohort study using the National Inpatient Sample (NIS) and UNOS Starfile (USF). We applied a probabilistic matching algorithm using 7 variables (transplant: month, year and type; recipient: age, sex, race, payer). We matched recipients in the USF to transplant patients in the NIS between 2016 to 2019. The Elixhauser methodology was used to identify comorbidities present on admission. We determined the associations between mortality, LOS, total charges and disposition with comorbidity number using penalized cubic splines, Kaplan-Meier, and linear and logistic regression methods. From 28,484,087 NIS admissions, we identified 1,821 LT recipients. Matches were exact in 76.8% of the cohort. While the remaining cohort had a probability match of ≥ 0.94. Penalized splines of Elixhauser comorbidity number identified 3 knots defining 3 groups of stacked risk: low (<3), medium (3-6) and high risk (>6). Inpatient mortality increased from low to medium to high risk categories: (1.6%, 3.9% and 7.0%; p<0.001), as did LOS (16, 21, 29 days, p<0.001), total charges ($553,057, $666,791, $821,641.5; p=0.004) and discharge to skilled nursing facility (15%, 20%, 31%; p <0.001). Stacked risks adversely affect post-LT mortality, LOS, charges and discharge disposition. Further study to understand the details of specific stacked risks is warranted.