MODELING MORTALITY RISK IN HEMODIALYSIS-PATIENTS USING LABORATORY VALUES AS TIME-DEPENDENT COVARIATES

Proportional hazards analyses assume that the magnitude of mortality r isk for a predictor variable remains proportional over time. In a time -dependent model, the explanatory variable violates this assumption, a nd repeat observations are required to accommodate the change in risk that occurs over time. Using a retrospective cohort design, we tested the following laboratory values for a time-by-covariate interaction: h ematocrit (HCT), serum albumin (ALB), and serum creatinine (CR). A ran dom sample of 4,083 hemodialysis patients whose specimens were analyze d in a central laboratory over a 3-year period served as the study gro up. Using the baseline observation, we discovered significant probabil ity values far the interaction terms TIME star CR (P = 0.013) and TIME star ALB (P < 0.01). The beta coefficient for TIME star HCT was not s ignificant, indicating that the proportional hazard assumption was not violated by this covariate. Based on these results, we fitted a multi variate regression model containing two time-dependent covariates (CR and ALB) using a data structure that incorporated repeat observations of these laboratory values. Patients with high ALB levels experienced the lowest mortality risk, Similarly, serum CR levels were higher in l ong-term survivors. This analysis verifies the clinical importance of monitoring ALB and CR levels over time and the hazard of using a singl e laboratory observation to make long-term projections. Additionally t he predictive values of the time-dependent covariates ALB and CR affir m the significance of nutritional approaches directed toward maintaini ng visceral and somatic protein content throughout renal replacement t herapy. (C) 1996 by the National Kidney Foundation, Inc.