RESUMO
BACKGROUND: Multiple listing (ML) is a practice utilized to increase the potential for transplant but is controversial due to concerns that it disproportionately benefits patients with greater access to healthcare 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 old) 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 UNOS Standard Transplant Analysis and Research File. The first exposure of interest was social deprivation index (SDI) with a primary outcome of ML status, to assess disparities between ML and 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: 35,890 subjects 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 had lower median LAS (35.3 vs 37.3). ML patients were more likely to be transplanted compared to SL patients (OR=1.42, 95%CI [1.17-1.73]), but there was a significantly quicker time to transplant only for whom ML was early (within 6 months of initial listing) (sHR=1.17, 95%CI [1.04-1.32]). INTERPRETATION: ML is an uncommon practice with disparities existing between ML and SL patients on the basis of several factors including social deprivation. ML patients are more likely to be transplanted, but only if they ML 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.
RESUMO
Lung size measurements play an important role in transplantation, as optimal donor-recipient size matching is necessary to ensure the best possible outcome. While several strategies for size matching are currently used, all have limitations, and none has proven superior. In this pilot study, we leveraged deep learning and computer vision to develop an automated system for generating standardized lung size measurements using portable chest radiographs to improve accuracy, reduce variability, and streamline donor/recipient matching. We developed a two-step framework involving lung mask extraction from chest radiographs followed by feature points detection to generate six distinct lung height and width measurements, which we validated against measurements reported by two radiologists for 50 lung transplant recipients. Our system demonstrated <2.5% error (< 7 mm) with robust inter- and intra-rater agreement compared to expert radiologist review. This is especially promising given that the radiographs used in this study were purposely chosen to include images with technical challenges such as consolidations, effusions, and patient rotation. While validation in a larger cohort is necessary, this study highlights AI's potential to both provide reproducible lung size assessment in real patients and enable studies on the effect of lung size matching on transplant outcomes in large datasets.