Steffan H Kim1, Grayson L Baird2, George Bayliss3, Basma Merhi3, Adena Osband3, Reginald Gohh3, Paul E Morrissey3. 1. Warren Alpert Medical School of Brown University, Providence, Rhode Island. 2. Lifespan Biostatistics Core, Rhode Island Hospital, Providence, Rhode Island. 3. Division of Organ Transplantation, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
Abstract
BACKGROUND: Thirty-day readmission rates (early hospital readmission, EHR) are an important benchmark for quality improvement. Nationally, patients undergoing renal transplantation incur a 31% EHR rate. While national databases provide useful data, the impact of EHR on individual centers has received little attention. We proposed that an institutional review of EHR after renal transplantation may provide a benchmark for individual transplant programs and identify modifiable program-specific issues to reduce EHR. METHODS: We reviewed 269 consecutive kidney transplant recipients over a five-year period (2012-2016). Early hospital readmission was modeled using generalized linear modeling assuming a binary distribution. RESULTS: About 21% of patients were readmitted within 30 days. Deceased kidney donation (DD), delayed graft functioning (DGF), anti-thymocyte globulin (ATG) induction, diabetes, public insurance, weekend discharge, and low glomerular filtration rate (eGFR) at discharge were all identified as risk factors for readmission. Early hospital readmission was not correlated with risk of death (5.4% at 44 months: HR 2.2 (95% CI [0.7, 6.6]; P = 0.1473) or graft loss. CONCLUSIONS: EHR after renal transplantation is common. Certain factors may predict an increased risk for EHR. A multi-disciplinary approach to discharge planning may limit some EHR, but most complications and adverse events are unpredictable and require hospital-level of care.
BACKGROUND: Thirty-day readmission rates (early hospital readmission, EHR) are an important benchmark for quality improvement. Nationally, patients undergoing renal transplantation incur a 31% EHR rate. While national databases provide useful data, the impact of EHR on individual centers has received little attention. We proposed that an institutional review of EHR after renal transplantation may provide a benchmark for individual transplant programs and identify modifiable program-specific issues to reduce EHR. METHODS: We reviewed 269 consecutive kidney transplant recipients over a five-year period (2012-2016). Early hospital readmission was modeled using generalized linear modeling assuming a binary distribution. RESULTS: About 21% of patients were readmitted within 30 days. Deceased kidney donation (DD), delayed graft functioning (DGF), anti-thymocyte globulin (ATG) induction, diabetes, public insurance, weekend discharge, and low glomerular filtration rate (eGFR) at discharge were all identified as risk factors for readmission. Early hospital readmission was not correlated with risk of death (5.4% at 44 months: HR 2.2 (95% CI [0.7, 6.6]; P = 0.1473) or graft loss. CONCLUSIONS: EHR after renal transplantation is common. Certain factors may predict an increased risk for EHR. A multi-disciplinary approach to discharge planning may limit some EHR, but most complications and adverse events are unpredictable and require hospital-level of care.
Keywords:
care delivery; hospital readmission; kidney (allograft) function / dysfunction; kidney disease; kidney transplantation: living donor; quality of care
Authors: Michelle C Nguyen; Christina L Avila; Guy N Brock; Jason A Benedict; Iyore James; Ashraf El-Hinnawi; Amer Rajab; Elmahdi Elkhammas; Ronald P Pelletier; Mitchell Henry; Ginny L Bumgardner Journal: Clin Transplant Date: 2020-03-02 Impact factor: 3.456
Authors: Kyla L Naylor; Gregory A Knoll; Justin Slater; Eric McArthur; Amit X Garg; Ngan N Lam; Britney Le; Alvin H Li; Megan K McCallum; Marlee Vinegar; S Joseph Kim Journal: Can J Kidney Health Dis Date: 2021-11-29