title: An objective method for detecting time-dependent effects in graft survival.
abstract: The proportional hazards model has become increasingly important in the analysis of censored survival data after transplantation. Neverthless, in clinical transplantation it is still undefined how the influence of covariates changes over time. The additive regression model is an alternative (or extension) to the Cox model. It results in plots (Aalen plots) that may give information on the effect of covariates over time by way of the cumulative regression function plots. A total of 386 primary cadaveric kidney transplants performed between 1984 and 1996 were included in our analysis. The follow-up period ranged from 24 to 156 months. According to Aalen, an additive regression model was used and plots for the detection of time-dependent effects of covariates were determined. Patients dying with functioning grafts were classed as graft failures. Factors potentially affecting graft outcome, such as sex, donor and recipient's age, HLA A-B match, cold ischaemia time (CIT), delayed graft function (DGF), serum creatinine at 1 month (Cr1), rejection episodes within 3 months (R3), and type of brain death (BD), were considered. The slopes of the plots by donor age, DGF, HLA A-B match, R3, Cr1 and BD appear to have a significant influence throughout the observation period, with different time-dependent effects on graft survival. Slopes for DGF, Cr1, and age of donor are positive (increased hazard), while slopes for HLA match and BD are negative (decreased hazard). Estimated regression functions for DGF, donor age and Cr1 show a prompt slope (within 3 months); the covariate R3 has a clear influence for about 5 years, and then seems to disappear; and BD appears to have a consistent effect over the entire period. The additive regression model with Aalen plots represents a useful technique in the analysis of survival data after kidney transplantation. Some covariates, such as R3, may often lose their effects on graft survival, with a relevant clinical impact. Others have a clear and additive influence over the entire period (BD), while the effects of donor age, DGF and CR1 each appear to have a prompt effect in the outcome.
authors: Centro Trapianti Renali, Ospedale Civile Maggiore, Verona, Italy. bos@iol.it
authors: L
authors: L
authors: Boschiero
authors: R
authors: R
authors: Foroni
authors: M
authors: M
authors: Tonini
authors: G
authors: G
authors: Ancona
mesh_terms: D000328:Adult; D000367:Age Factors; D001926:Brain Death; D002102:Cadaver; D003404:Creatinine / Q000097:blood; D005260:Female; D006084:Graft Rejection / Q000453:epidemiology; D006085:Graft Survival; D006650:Histocompatibility Testing; D006801:Humans; D016030:Kidney Transplantation / Q000276:immunology / Q000502:physiology*; D008297:Male; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012189:Retrospective Studies; D013997:Time Factors; D014019:Tissue Donors; D017211:Treatment Failure; D016896:Treatment Outcome
chemical_list: D003404:Creatinine