Prognostic impact of renal arterial resistance index upon renal allograft survival: the time point matters

Abstract

Background: The renal arterial resistance index (RI) is reported to be a significant predictive parameter for renal allograft failure or death. The influence of the time point after renal transplantation on its predictive power has not been sufficiently evaluated. We performed a retrospective analysis of RI and its power to predict renal allograft failure or death with special emphasis on the time point of RI measurement.

Methods: The present analysis is based on ultrasonographically recorded intrarenal arterial RI measurements, routinely obtained in our outpatient department, over a period of 13 years. Altogether, 88 patients with an RI measurement 0-3, 3-6 and 12-18 months after transplantation were included and retrospectively stratified into two groups according to the RI: those with an index >0.75 and those with an index of ≤0.75.

Results: Twenty patients (23%) reached the combined end point, i.e. allograft failure with a return to dialysis or death. The RI measured early after transplantation (0-3 and 3-6 months) did not predict the end point, whereas the RI obtained between 12 and 18 months showed a significant predictive value for renal transplant failure or death in a univariate approach [Wald test, P = 0.0013, hazard ratio (HR) 4.787, 95% confidence interval (CI) 1.846-12.411]. At 12-18 months after transplantation, 14% (12 patients) of the study population had an RI >0.75. Seven (58%) of these patients reached the end point versus 13 of 76 patients (17%) with an RI ≤0.75. In a multivariate Cox model, the RI measured between 12 and 18 months after transplantation exhibited the highest hazard ratio (HR 6.191, 95% CI 2.288-16.756, P = 0.0003) for transplant failure or death.

Conclusion: In our hands, the RI obtained during the first 6 months after transplantation failed to predict renal allograft failure or death, whereas the RI measured 12-18 months after transplantation appeared useful to predict long-term allograft outcomes.