Scoring donor lungs for graft failure risk: The Lung Donor Risk Index (LDRI).

Lung transplantation lags behind other solid organ transplants in donor lung utilization due, in part, to uncertainty regarding donor quality. We sought to develop an easy-to-use donor risk metric that, unlike existing metrics, accounts for a rich set of donor factors. Our study population consisted of n = 26 549 adult lung transplant recipients abstracted from the United Network for Organ Sharing Standard Transplant Analysis and Research file. We used Cox regression to model graft failure (GF; earliest of death or retransplant) risk based on donor and transplant factors, adjusting for recipient factors. We then derived and validated a Lung Donor Risk Index (LDRI) and developed a pertinent online application (https://shiny.pmacs.upenn.edu/LDRI_Calculator/). We found 12 donor/transplant factors that were independently predictive of GF: age, race, insulin-dependent diabetes, the difference between donor and recipient height, smoking, cocaine use, cytomegalovirus seropositivity, creatinine, human leukocyte antigen (HLA) mismatch, ischemia time, and donation after circulatory death. Validation showed the LDRI to have GF risk discrimination that was reasonable (C = 0.61) and higher than any of its predecessors. The LDRI is intended for use by transplant centers, organ procurement organizations, and regulatory agencies and to benefit patients in decision-making. Unlike its predecessors, the proposed LDRI could gain wide acceptance because of its granularity and similarity to the Kidney Donor Risk Index.

Transplanting candidates with stacked risks negatively affects outcomes.

BACKGROUND: Lung transplant (LT) centers are increasingly evaluating patients with multiple risk factors for adverse outcomes. The effects of these stacked risks remains unclear. Our aim was to determine the relationship between the number of comorbidities and post-transplant outcomes. METHODS: 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 and 2019. The Elixhauser methodology was used to identify comorbidities present on admission. We determined the associations between mortality, length of stay (LOS), total charges, and disposition with comorbidity numbers using penalized cubic splines, Kaplan-Meier, and linear and logistic regression methods. RESULTS: 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 a skilled nursing facility (15%, 20%, 31%; p < 0.001). CONCLUSIONS: Stacked risks adversely affect post-LT mortality, LOS, charges, and discharge disposition. Further study to understand the details of specific stacked risks is warranted.