Evaluating US Multiple Listing Practices in Lung Transplantation: Unveiling Hidden Disparities.

Background: Multiple listing (ML) is a practice used to increase the potential for transplant but is controversial due to concerns that it disproportionately benefits patients with greater access to health care resources.

Research Question: Is there disparity in ML practices based on social deprivation in the United States and does ML lead to quicker time to transplant?

Study Design And Methods: A retrospective cohort study of adult (≥ 18 years of age) lung transplant candidates listed for transplant (2005-2018) was conducted. Exclusion criteria included heart only or heart and lung transplant and patients relisted during the observation period. Data were obtained from the United Network for Organ Sharing Standard Transplant Analysis and Research File. The first exposure of interest was the Social Deprivation Index with a primary outcome of ML status, to assess disparities between ML and single listing (SL) participants. The second exposure of interest was ML status with a primary outcome of time to transplant, to assess whether implementation of ML leads to quicker time to transplant.

Results: A total of 35,890 patients were included in the final analysis, of whom 791 (2.2%) were ML and 35,099 (97.8%) were SL. ML participants had lower median level of social deprivation (5 units, more often female: 60.0% vs 42.3%) and lower median lung allocation score (35.3 vs 37.3). ML patients were more likely to be transplanted than SL patients (OR, 1.42; 95% CI, 1.17-1.73), but there was a significantly quicker time to transplant only for those whom ML was early (within 6 months of initial listing) (subdistribution hazard ratio, 1.17; 95% CI, 1.04-1.32).

Interpretation: ML is an uncommon practice with disparities existing between ML and SL patients based on several factors including social deprivation. ML patients are more likely to be transplanted, but only if they have ML status early in their transplant candidacy. With changing allocation guidelines, it is yet to be seen how ML will change with the implementation of continuous distribution.