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Assessing Renal Parenchymal Volume on Unenhanced CT as a Marker for Predicting Renal Function in Patients with Chronic Kidney Disease

By Supriya Gupta, Anand H. Singh, Amna Shabbir, Peter F. Hahn, Gordon Harris and Dushyant Sahani

Abstract

Objectives: To estimate renal volume in chronic kidney disease (CKD) Patients using a semiautomated software and compare them with split renal function estimates from radionuclide renogram (RR). We proposed that renal volume from unenhanced computed tomography (CT) scans may serve as surrogate marker for assessing renal function in CKD Patients. Materials and Methods: Unenhanced multidetector CT scans of 26 Patients with CKD (estimated glomerular filtration rate [eGFR] /kg/body surface area [BSA]) and 10 controls (eGFR \u3e60 mL/kg/BSA) were analyzed to calculate renal volumes using a semiautomated software (AMIRAV5.2.0). Volumes obtained were then correlated with corresponding eGFR and split renal function estimates from RR. Volumes were also compared with those obtained on enhanced scans in 10 cases (five disease group, five controls). Bland-Altman analysis was used to assess agreement between methods. Results: A moderately positive correlation was found between renal volume obtained on unenhanced CT and eGFR (r = 0.65, P \u3c .0001), whereas a significantly high correlation with split function estimates from RR (r = 0.95, P \u3c .001) was found. Bland-Altman analysis revealed a good agreement between renal volume from CT and renal function from RR (34/36 observations were within 95% CI and there were two outliers). Correlation between volumes obtained from unenhanced and enhanced CT scans was also significant (r = 0.96). Conclusion: In Patients with CKD, renal volume derived from unenhanced CT can possibly serve as a surrogate marker for assessing and monitoring renal function reserves to plan further management

Topics: Renal volumetry, split renal function, chronic kidney disease, Nephrology
Publisher: eCommons@AKU
Year: 2012
DOI identifier: 10.1016/j.acra.2012.02.006
OAI identifier: oai:ecommons.aku.edu:pakistan_fhs_mc_mc-1000
Provided by: eCommons@AKU
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