Risk Stratification for Rejection and Infection after Kidney Transplantation

Abstract

Background and objectives Definition of individual risk profile is the first step to implement strategies to keep the delicate balance between under-and overimmunosuppression after kidney transplantation. Results Despite different temporal dynamics, rejections and severe infections had similar overall incidences in the first year after transplantation (23.4% and 25.5%, respectively), and infections were the principal cause of death (43.2% of all deaths). Recipient older age, deceased donor, higher number of HLA mismatches, and high risk for cytomegalovirus disease were associated with infection; deceased donor, higher number of HLA mismatches, and immunosuppressive therapy including cyclosporin A (compared with tacrolimus), with rejection. These factors were integrated into a two-dimensional risk stratification model, which defined four risk groups: low risk for infection and rejection (30.8%), isolated risk for rejection (36.1%), isolated risk for infection (7.0%), and high risk for infection and rejection (26.1%). In internal validation, this model significantly discriminated the subgroups in terms of composite end point (low risk for infection/rejection, 24.4%; isolated risk for rejection and isolated risk for infection, 31.3%; high risk for infection/rejection, 54.4%; P,0.001), rejection episodes (isolated risk for infection and low risk for infection/rejection, 13.0%; isolated risk for rejection and high risk for infection/ rejection, 24.2%; P=0.001), and infection episodes (low risk for infection/rejection and isolated risk for rejection, 12.0%; isolated risk for infection and high risk for infection/rejection, 37.6%; P,0.001). External validation confirmed the applicability of the model to an independent cohort.Conclusions We propose a two-dimensional risk stratification model able to disentangle the individual risk for rejection and infection in the first year after kidney transplantation. This concept can be applied to implement a personalized immunosuppressive and antimicrobial treatment approach.

Single-center analysis of infectious complications in older adults during the first year after kidney transplantation

“…The infection rate was very high, with 92.4% of patients experiencing at least one infectious complication. This is higher than the 67%−80% rate reported from comparable small series of similar aged patients[9,15] and substantially higher than in younger cohorts (25.5%)[16]. Despite this very high number of infectious complications during the first post-transplant year, the number of deaths, graft rejections, and graft losses was low compared to similar aged patients[9].…”

Clinical outcomes of kidney transplantation in older end‐stage renal disease patients: A nationwide cohort study

“…We investigated infection-related mortality, the most common cause of patient death during the early-to-intermediate post-transplant period in previous studies. 12,28,29 As a result, it was higher in the elderly patients in the overall and both subgroups. As aforementioned, elderly ESRD patients can show reduced immune response not only to alloantigen, but also to opportunistic infections.…”

“…Advanced age was also an independent risk factor for all‐cause mortality in the overall study group, which is consistent with previous studies. We investigated infection‐related mortality, the most common cause of patient death during the early‐to‐intermediate post‐transplant period in previous studies . As a result, it was higher in the elderly patients in the overall and both subgroups.…”

A simple score can identify kidney transplant recipients at high risk of severe infection over the following 2 years

“…These factors are important because the amount of immunosuppression and the relative risk for infection decrease over time and such large heterogeneity in post‐transplant follow‐up may influence the risk prediction models. Importantly, we did not simultaneously examine rates of rejection which would be necessary in any study looking to reduce immunosuppression . We showed that readily available clinical features and biomarkers at a single point in time can be used in a score to identify a subgroup of patients at very high risk of severe infection.…”

“…Importantly, we did not simultaneously examine rates of rejection which would be necessary in any study looking to reduce immunosuppression. 35 We showed that readily available clinical features and biomarkers at a single point in time can be used in a score to identify a subgroup of patients at very high risk of severe infection.…”