Incidence, predictors, and outcomes of early hospital readmissions after kidney transplantation: Systemic review and meta-analysis

Abstract

BackgroundEarly hospital readmission (EHR) within 30 days after kidney transplantation is a significant quality indicator of transplant centers and patient care. This meta-analysis aims to evaluate the incidence, predictors, and outcomes of EHR after kidney transplantation.MethodsWe comprehensively searched the databases, including PubMed, Cochrane CENTRAL, and Embase, from inception until December 2021 to identify studies that assessed incidence, risk factors, and outcome of EHR. The outcomes included death-censored graft failure and mortality. Data from each study were combined using the random effect to calculate the pooled incidence, mean difference (MD), odds ratio (OR), and hazard ratio (HR) with 95% confidence interval (CI).ResultsA total of 17 studies were included. The pooled EHR incidence after kidney transplant was 24.4% (95% CI 21.7–27.3). Meta-analysis showed that recipient characteristics, including older recipient age (MD 2.05; 95% CI 0.90–3.20), Black race (OR 1.31; 95% CI 1.11, 1.55), diabetes (OR 1.32; 95% CI 1.22–1.43), and longer dialysis duration (MD 0.85; 95% CI 0.41, 1.29), donor characteristics, including older donor age (MD 2.02; 95% CI 0.93–3.11), and transplant characteristics, including delayed graft function (OR 1.75; 95% CI 1.42–2.16) and longer length of hospital stay during transplantation (MD 1.93; 95% CI 0.59–3.27), were significantly associated with the increased risk of EHR. EHR was significantly associated with the increased risk of death-censored graft failure (HR 1.70; 95% CI 1.43–2.02) and mortality (HR 1.46; 95% CI 1.27–1.67) within the first year after transplantation.ConclusionAlmost one-fourth of kidney transplant recipients had EHR within 30 days after transplant, and they had worse post-transplant outcomes. Several risk factors for EHR were identified. This calls for future research to develop and implement for management strategies to reduce EHR in high-risk patients.