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ABSTRACT: Background
Prolonged warm ischemia time (WIT) is associated with graft failure and mortality, however less is known about factors associated with prolonged WIT.Methods
In a cohort of United States deceased donor kidney transplant recipients identified using the Scientific Registry of Transplant Recipients (Jan 2005-Dec 2013), we identified factors associated with prolonged WIT (defined as ≥ 30 minutes versus 10-30 minutes) using hierarchical multilevel models adjusting for center effect, and WIT as a continuous variable using multiple linear regression of log-transformed data.Results
Among 55 829 patients, potentially modifiable risk factors associated with prolonged WIT included increased recipient body mass index (BMI) (odds ratio [OR], 1.57; 95% confidence interval [CI], 1.44-1.72 for BMI > 35), right donor kidney (OR, 1.14; 95% CI, 1.08-1.19), and a prolonged cold ischemic time (OR, 1.23; 95% CI, 1.13-1.33 for cold ischemia time > 24 hours). Transplanting a right kidney into an obese recipient further prolonged WIT (OR, 1.75; 95% CI, 1.55-1.98; for BMI > 35), increasing overall WIT by 11.0%. There was no correlation between median WIT for a given center and annual center transplant rate (pairwise correlation coefficient, 0.0898).Conclusions
In conclusion, several modifiable factors are associated with prolonged WIT and may represent strategies to improve WIT and subsequent posttransplant outcomes.
SUBMITTER: Vinson AJ
PROVIDER: S-EPMC5959340 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature

Transplantation direct 20180418 5
<h4>Background</h4>Prolonged warm ischemia time (WIT) is associated with graft failure and mortality, however less is known about factors associated with prolonged WIT.<h4>Methods</h4>In a cohort of United States deceased donor kidney transplant recipients identified using the Scientific Registry of Transplant Recipients (Jan 2005-Dec 2013), we identified factors associated with prolonged WIT (defined as ≥ 30 minutes versus 10-30 minutes) using hierarchical multilevel models adjusting for center ...[more]