W. Q. Zhang1, A. Rana2, B. V. Murthy2 1Baylor College Of Medicine,Houston, TX, USA 2Baylor College Of Medicine,Division Of Abdominal Transplantation,Houston, TX, USA
Introduction: Length of stay is a surrogate for determining use of healthcare resources and costs to both patients and hospitals. Currently, there is not a comprehensive review of risk factors for prolonged length of stay (PLOS; >14 days) at a hospital after kidney transplant. Insight into factors that increase the probability for PLOS will provide a basis for future clinical applications.
Methods: Univariate and multivariate analyses (p<0.05) were performed on 98,322 adult recipients of deceased donor kidneys between August 2008 and March 2018 using the United Network for Organ Sharing (UNOS) database to identify donor, recipient, and perioperative risk factors for PLOS.
Results: Univariate analysis identified 32 factors, in addition to Estimated Post Transplant Survival (EPTS) score and the Kidney Donor Profile Index (KDPI), as possible predictors of PLOS. Including EPTS and KDPI, 18 total factors remained significant after multivariate analysis. Factors increasing the probability of PLOS include longer cold ischemia times (CITs), admission to the intensive care unit (ICU) at time of transplant, lower functional status, African American ethnicity, male donor, body mass index (BMI) under 18.5 or over 35, longer time on dialysis, and national procurement. Factors protective against PLOS include shorter time on waitlist, shorter time on dialysis, and BMI of 25 up to 30.
Conclusion: Overall, admission to the ICU (Odds Ratio (OR) = 13.61) had the largest effect on PLOS, but other interactions were also revealed. Of note, groups with CITs of 7 hours up to 18 hours (OR = 1.65), 18 hours up to 32 hours (OR = 1.97), and over 32 hours (OR = 2.42) all had significantly increased risk of PLOS compared to the reference group of CIT under 7 hours, with the effect on PLOS increasing with increasing CIT. This emphasizes the need to minimize CIT. Other factors will require further analysis to interpret. A next step for this project will be to create a predictive index for PLOS.