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      Risk Factors for Chronic Renal Dysfunction in Cardiac Allograft Recipients

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          Renal dysfunction is one of the most common and threatening complications in heart transplant recipients. Even if ciclosporin seems to play a central role in inducing renal damage, other factors may concur or predispose to renal injury. In order to identify factors responsible for renal dysfunction, we retrospectively studied a cohort of 114 cardiac transplant recipients during a follow-up period of at least 3 years. The patients had a normal renal function before and 0.5 months after heart transplantation. Doubling of baseline serum creatinine or attainment of serum creatinine steadily above 176.8 µmol/l (2.0 mg/dl) was used as criterion to define the end-point renal dysfunction. A series of clinical and laboratory variables were obtained from the patients’ charts at different time intervals, and their prognostic value for the occurrence of renal dysfunction was calculated by Cox proportional hazards models. 23 out of 114 patients reached the end point after a median time period of 21 months. High serum triglyceride, alanine aminotransferase, alkaline phosphatase, ciclosporin, urea, glucose, and hemoglobin levels were shown to be associated with the development of renal dysfunction. Four variables, i.e., triglyceride, ciclosporin, urea, and alkaline phosphatase, had an independent prognostic value. Our results confirm a role for ciclosporin in inducing renal dysfunction and identify hyperlipidemia and an increased plasma urea level as risk factors for renal dysfunction in heart transplant recipients.

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          Explained variation in survival analysis.

           F STARE,  M Schemper (1996)
          Several measures of explained variation have been suggested for the Cox proportional hazards regression model. We have categorized these measures into three classes which correspond to three different definitions of multiple R2 of the general linear model. In an empirical study we compared the performance of these measures and classified them by their adherence to a set of criteria which we think should be met by a measure of explained variation for survival data. We suggest that currently there is no uniformly superior measure, particularly as the concepts of either uncensored or censored populations may lead to different choices. For uncensored populations, a measure by Kent and O'Quigley and the squared rank correlation between survival time and the predictor from a Cox regression model appear recommendable choices. For the latter, censored survival times are terminated using a very recent data augmentation algorithm for multiple imputation under proportional hazards. With censored populations, Schemper's measure, V2, could be considered. We give an introductory example, discuss aspects of application and stress the desirability of routinely evaluating explained variation in studies of survival.

            Author and article information

            S. Karger AG
            January 2000
            19 January 2000
            : 84
            : 1
            : 21-28
            aDepartment of Internal Medicine and Nephrology, bDivision of Cardiology, cDivision of Cardiac Surgery, and dScientific Administration, IRCCS Policlinico San Matteo, University of Pavia, Italy
            45534 Nephron 2000;84:21–28
            © 2000 S. Karger AG, Basel

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            Figures: 3, Tables: 1, References: 41, Pages: 8
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