One-Year Mortality Is Not a Reliable Indicator of Lung Transplant Center Performance.

Publication/Presentation Date

7-1-2022

Abstract

BACKGROUND: In the United States, the Organ Procurement and Transplant Network (OPTN) uses 1-year mortality as the primary measure of transplant center quality. We sought to evaluate the reliability of mortality outcomes in lung transplantation and to compare statistical methods of program performance evaluation.

METHODS: We used the Standard Transplant Analysis and Research files from the United Network for Organ Sharing to identify lung transplant recipients from 2013 to 2018 in the United States. We stratified hospitals on the basis of 30-day, 1-year, and 5-year survival by risk adjustment, reliability adjustment with empirical Bayes technique, and hierarchical bayesian mixed effects models currently used by the OPTN. We measured variation in mortality rates and identification of performance outliers between techniques.

RESULTS: We identified 12,769 recipients in 69 centers. Reliability adjustment reduced variation in hospital outcomes and had a large impact on hospital mortality rankings. For example, with 1-year mortality, 28% (5 hospitals) of the "best" hospitals (top 25%) and 18% (3 hospitals) of the "worst" hospitals (bottom 25%) were reclassified after reliability adjustment. The overall reliability of 1-year mortality was low at 0.42. Compared with the bayesian method used by the OPTN, reliability adjustment identified fewer outliers. The 5-year survival reached a higher reliability plateau with a lower volume of cases required.

CONCLUSIONS: The reliability of 1-year mortality in lung transplantation is low, whereas 5-year survival estimates may be more reliable at lower case volumes. Reliability adjustment yielded more conservative measures of center performance and fewer outliers compared with current bayesian methods.

Volume

114

Issue

1

First Page

225

Last Page

232

ISSN

1552-6259

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

35247344

Department(s)

Administration and Leadership

Document Type

Article

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