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Renal Perfusion Index Reflects Cardiac Systolic Function in Chronic Cardio-Renal Syndrome

Arkadiusz Lubas, Robert Ryczek, Grzegorz Kade, Stanisław Niemczyk

Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw, Poland

Med Sci Monit 2015; 21:1089-1096

DOI: 10.12659/MSM.892630

Available online:

Published: 2015-04-17

Background: Cardiac dysfunction can modify renal perfusion, which is crucial to maintain sufficient kidney tissue oxygenation. Renal cortex perfusion assessed by dynamic ultrasound method is related both to renal function and cardiac hemodynamics. The aim of the study was to test the hypothesis that Renal Perfusion Index (RPI) can more closely reflect cardiac hemodynamics and differentiate etiology of chronic cardio-renal syndrome.
Material and Methods: Twenty-four patients with hypertension and chronic kidney disease (CKD) at 2–4 stage (12 with hypertensive nephropathy and 12 with CKD prior to hypertension) were enrolled in the study. Blood tests, 24-h ABPM, echocardiography, and ultrasonography with estimation of Total renal Cortical Perfusion intensity and Renal Perfusion Index (RPI) were performed.
Results: In the group of all patients, RPI correlated with left ventricular stoke volume (LVSV), and cardiac index, but not with markers of renal function. In multiple stepwise regression analysis CKD-EPI(Cys-Cr) (b=–0.360), LVSV (b=0.924) and MAP (b=0.376) together independently influenced RPI (R2=0.74; p<0.0001). RPI<0.567 allowed for the identification of patients with chronic cardio-renal syndrome with sensitivity of 41.7% and specificity of 83.3%.
Conclusions: Renal perfusion index relates more strongly to cardiac output than to renal function, and could be helpful in recognizing chronic cardio-renal syndrome. Applicability of RPI in diagnosing early abnormalities in the cardio-renal axis requires further investigation.

Keywords: Cardio-Renal Syndrome - urine, Antihypertensive Agents - therapeutic use, Chronic Disease, Heart Function Tests, Kidney - physiopathology, Linear Models, Perfusion, ROC Curve, Stroke Volume, Systole