You have accessJournal of UrologyKidney Cancer: Localized I1 Apr 2014PD16-05 COMPUTER ASSISTED RENAL VOLUMETRIC ASSESSMENT TO PREDICT POSTOPERATIVE RENAL FUNCTION PRIOR TO EXTIRPATIVE RENAL SURGERY Michael Liss, Dominique Caovan, Robert Deconde, Michael Gabe, Kerrin Palazzi, Nishant Patel, Ramzi Jabaji, Hak Lee, David Karow, Giovanna Casola, and Ithaar Derweesh Michael LissMichael Liss More articles by this author , Dominique CaovanDominique Caovan More articles by this author , Robert DecondeRobert Deconde More articles by this author , Michael GabeMichael Gabe More articles by this author , Kerrin PalazziKerrin Palazzi More articles by this author , Nishant PatelNishant Patel More articles by this author , Ramzi JabajiRamzi Jabaji More articles by this author , Hak LeeHak Lee More articles by this author , David KarowDavid Karow More articles by this author , Giovanna CasolaGiovanna Casola More articles by this author , and Ithaar DerweeshIthaar Derweesh More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1150AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Introduction and Objectives To incorporate a computer-assisted volumetric assessement of potential spared paranchyma from preoperative CT scans to predict chronic kidney disease (CKD) at 6 months from extirpative renal surgery. Methods We performed a retrospecitve analysis of radical (RN) or partial nephrectomy (PN) patients with compatable CT scans from our institution. We used Vitrea v.6.3 computer software program (Vital Images, Minnetonka, MN) to create a 3D volume of the tumor, 1 cm margin, ipsilateral kidney (Figure), and contralateral kidney (cm3). Primary outcome analyzed was development of postoperative CKD (estimated glomerular filtration rate<60 mL/min/1.73 m2 by MDRD equation). We performed linear regression using preoperative GFR, total RENAL nephrometry score, and volumes (excluding tumor) to predict 6-month GFR and tested with 5-fold cross validation. The GFR generated by our analysis was compared to postoperative eGFR for prediction of CKD to calculate test characteristics [sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV)] and area under the curve (AUC). Results We included 130 patients (79 PN/51 RN) from our database from 3/2000 to 2/2013 and a median eGFR follow-up of 6.1 (4.2-36) months. Median age was 58 (IQR: 49-67), 83 (61%) men, and 53 (57%) Caucasian. Median tumor volume was 24.6 (IQR: 7–82) cm3. The most signifant corrlates of post operative renal function were preoperative eGFR (p<0.001), Isipateral volume (p<0.001), and estimated margin volume (p<0.001). RENAL nephromery score (p=0.285) and contralateral renal volume (p=0.418) were non-signficant. In multivariate linear regression, the predicted GFR correlated with postoperative GFR at 6 months (R2=0.518, p<0.001). Using the model, prediction of postoperative CKD noted an AUC of 0.752 (95% CI 0.662-0.842; p=<0.001) with accompanying sensitivity (86.7%), specificity (63.6%), positive predicitve value (76.5%), and negative predictive value (77.8%). Conclusions Preoperative GFR and computer assisted predicted renal volume spared are able to predict 6-month postoperative occurance of CKD. While prospective validation is requisite, this technique may provide valuable information regarding risk of postoperative CKD in clincial decision-making regarding partial or radical nephrectomy and post-operative expectations. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e488 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Michael Liss More articles by this author Dominique Caovan More articles by this author Robert Deconde More articles by this author Michael Gabe More articles by this author Kerrin Palazzi More articles by this author Nishant Patel More articles by this author Ramzi Jabaji More articles by this author Hak Lee More articles by this author David Karow More articles by this author Giovanna Casola More articles by this author Ithaar Derweesh More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...