William F Parker1, Nicole E Dussault2, Renea Jablonski3, Edward R Garrity4, Matthew M Churpek5. 1. Department of Medicine, Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois; MacLean Center for Clinical Medical Ethics, University of Chicago, Chicago, Illinois. Electronic address: wparker@uchicago.edu. 2. Pritzker School of Medicine, University of Chicago, Chicago, Illinois. 3. Department of Medicine, Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois. 4. Department of Medicine, Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois; MacLean Center for Clinical Medical Ethics, University of Chicago, Chicago, Illinois. 5. Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
Abstract
BACKGROUND: The United States (US) Lung Allocation Score (LAS) relies on the performance of 2 survival models that estimate waitlist and post-transplant survival. These models were developed using data from 2005 to 2008, and it is unknown if they remain accurate. METHODS: We performed an observational cohort study of US lung transplantation candidates and recipients greater than 12 years of age between February 19, 2015 and February 19, 2019. We evaluated the LAS waitlist and post-transplant models with the concordance probability estimate and by comparing predicted vs observed 1-year restricted mean survival times by risk decile. We then compared a nonparametric estimate of the observed LAS with the predicted LAS for each percentile of recipients. RESULTS: The waitlist model ranked candidates (N = 11,539) in the correct risk order 72% of the time (95% CI 71%-73%), and underestimated candidate one-year survival by 136 days for the highest risk decile (p < 0.001). The post-transplant model ranked recipients (N = 9,377) in the correct risk order 57% of the time (95% CI 55-58%), and underestimated recipient one-year survival by 70 days for the highest risk decile (p < 0.001). Overall, the LAS at transplant explained only 56% of the variation in observed outcomes, and was increasingly inaccurate at higher predicted values. CONCLUSIONS: The waitlist and the post-transplant models that constitute the LAS are inaccurate, limiting the ability of the system to rank candidates on the waitlist in the correct order. The LAS should therefore be updated and the underlying models should be modernized.
BACKGROUND: The United States (US) Lung Allocation Score (LAS) relies on the performance of 2 survival models that estimate waitlist and post-transplant survival. These models were developed using data from 2005 to 2008, and it is unknown if they remain accurate. METHODS: We performed an observational cohort study of US lung transplantation candidates and recipients greater than 12 years of age between February 19, 2015 and February 19, 2019. We evaluated the LAS waitlist and post-transplant models with the concordance probability estimate and by comparing predicted vs observed 1-year restricted mean survival times by risk decile. We then compared a nonparametric estimate of the observed LAS with the predicted LAS for each percentile of recipients. RESULTS: The waitlist model ranked candidates (N = 11,539) in the correct risk order 72% of the time (95% CI 71%-73%), and underestimated candidate one-year survival by 136 days for the highest risk decile (p < 0.001). The post-transplant model ranked recipients (N = 9,377) in the correct risk order 57% of the time (95% CI 55-58%), and underestimated recipient one-year survival by 70 days for the highest risk decile (p < 0.001). Overall, the LAS at transplant explained only 56% of the variation in observed outcomes, and was increasingly inaccurate at higher predicted values. CONCLUSIONS: The waitlist and the post-transplant models that constitute the LAS are inaccurate, limiting the ability of the system to rank candidates on the waitlist in the correct order. The LAS should therefore be updated and the underlying models should be modernized.
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