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Simulated Regionalization of Heart and Lung Transplantation in the United States
Author(s) -
Magruder J. T.,
Shah A. S.,
Crawford T. C.,
Grimm J. C.,
Kim B.,
Orens J. B.,
Bush E. L.,
Higgins R. S.,
Merlo C. A.
Publication year - 2017
Publication title -
american journal of transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.89
H-Index - 188
eISSN - 1600-6143
pISSN - 1600-6135
DOI - 10.1111/ajt.13967
Subject(s) - medicine , confidence interval , lung transplantation , heart transplantation , transplantation , logistic regression , referral , surgery , demography , emergency medicine , cardiology , pediatrics , family medicine , sociology
We simulated the impact of regionalization of isolated heart and lung transplantation within United Network for Organ Sharing ( UNOS ) regions. Overall, 12 594 orthotopic heart transplantation ( OHT) patients across 135 centers and 12 300 orthotopic lung transplantation ( OLT) patients across 67 centers were included in the study. An algorithm was constructed that “closed” the lowest volume center in a region and referred its patients to the highest volume center. In the unadjusted analysis, referred patients were assigned the highest volume center's 1‐year mortality rate, and the difference in deaths per region before and after closure was computed. An adjusted analysis was performed using multivariable logistic regression using recipient and donor variables. The primary outcome was the potential number of lives saved at 1 year after transplant. In adjusted OHT analysis, 10 lives were saved (95% confidence interval [ CI] 9–11) after one center closure and 240 lives were saved (95% CI 209–272) after up to five center closures per region, with the latter resulting in 1624 total patient referrals (13.2% of OHT patients). For OLT , lives saved ranged from 29 (95% CI 26–32) after one center closure per region to 240 (95% CI 224–256) after up to five regional closures, but the latter resulted in 2999 referrals (24.4% of OLT patients). Increased referral distances would severely limit access to care for rural and resource‐limited populations.