Model for Establishing Biological Variation in Nonhealthy Situations: Renal Posttransplantation Data

Abstract

Renal transplantation has come to be accepted as standard treatment for patients with terminal kidney failure. Because the physiological state of the renal transplant recipient is unstable, he or she is monitored with a well-defined protocol that is strictly followed by clinicians. Renal dysfunction is a common complication due to various problems, especially drug toxicity, and rejection is an ever-present danger (1). To detect episodes of rejection, clinicians use empirical criteria derived from experience that is mainly based on changes in creatinine concentrations. Posttransplantation monitoring, which includes frequent analysis of a number of constituents, generates large amounts of data. With this information, finding objective, early indicators that predict trends toward complications before the patient’s condition is seriously compromised would be beneficial, so that preventive actions could be taken. Data from biological variation (BV), the normal fluctuation around the homeostatic set point, has been used to evaluate the significance of changes in serial results (2). Therefore, it can provide clinicians with an indication of future patient status: A change between two consecutive observations higher than the established variation around the homeostatic set point could signal the beginning of a complication. However, the components of BV, sensitive and specific enough to characterize a “certain state of health” from the start of crisis, can be investigated only when a stable situation that denotes equilibrium has been demonstrated in the specific pathology. The aims of this work were to delineate the stable period after renal transplantation for six serum analytes expected to reflect instability/rejection, and to calculate within- and between-subject BV, indices of individuality (II), and critical differences (CDs) between serial results and compare outcome with published data in healthy subjects to determine whether BV data can predict crises in this population. We studied 19 patients (12 men and 7 women), 19 to 64 …