Title

Ambient Air Pollution and Adverse Waitlist Events among Lung Transplant Candidates

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Document Type

Presentation

Presentation Date

6-9-2022

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Presentation: 7:24

Mentor: Dorry Segev, MD, PhD, Transplant Surgery, Johns Hopkins University Hospital

Award: NIDDK T32 National Research Service Award

Abstract

Background: Air pollution is associated with cardiopulmonary disease and death in the general population. Fine particulate matter (PM2.5) is particularly harmful due to its ability to penetrate areas of gas exchange within the lungs. Persons with advanced lung disease are believed to be particularly susceptible to PM2.5 exposure but few studies have examined the effect of exposure on this population. Here we investigate the association between PM2.5 exposure and adverse waitlist events among lung transplant (LT) candidates.

Methods: US registry data were used to identify LT candidates listed between 1/1/2010-12/31/2016. Annual PM2.5 concentration at year of listing was estimated for each candidate’s ZIP Code using NASA’s SEDAC Global Annual PM2.5 Grids. We estimated crude and adjusted hazard ratios for adverse waitlist events, defined as death or removal, using Cox proportional hazards regression.

Results: Of the 15,075 included candidates, median age at listing was 60, 43.8% were female and 81.7% were non-Hispanic white. Median ZIP Code PM2.5 concentration was 9.06μg/m3. When compared to those living in ZIP Codes with lower PM2.5 exposure (PM2.5 <10.53μg/m3), candidates in ZIP Codes in the highest quartile of PM2.5 exposure (≥10.53μg/m3) had 1.14-fold (95%CI 1.04-1.25) risk of adverse waitlist events. The result remained significant after adjusting for demographics, education, insurance, smoking, lung allocation score, BMI, and blood type (HR=1.17; 95%CI 1.07-1.29).

Conclusions: Elevated ambient PM2.5 concentration was associated with adverse waitlist events among LT candidates. These findings highlight the impact of air pollution on clinical outcomes in this critically ill population.

Language

English

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