MP61-06 USING CYSTATIN C TO PREDICT RENAL FUNCTION POST-NEPHRECTOMY
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance III (MP61)1 Sep 2021MP61-06 USING CYSTATIN C TO PREDICT RENAL FUNCTION POST-NEPHRECTOMY Ben Petrinec, Ian Cooke, Eric Midenburg, Kenneth Ogan, and Viraj Master Ben PetrinecBen Petrinec More articles by this author , Ian CookeIan Cooke More articles by this author , Eric MidenburgEric Midenburg More articles by this author , Kenneth OganKenneth Ogan More articles by this author , and Viraj MasterViraj Master More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002101.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Patients undergoing nephrectomy experience loss of renal volume and function. Lower pre-operative estimated glomerular filtration rate (eGFR) is a predictor of post-operative chronic kidney disease. Some studies suggest cystatin c (Cys C) may be a more precise and accurate measure with which to calculate eGFR than serum creatinine (SCr). The purpose of this study is to determine whether Cys C and eGFR utilizing Cys C will correlate more strongly with post-operative renal function (presence of proteinuria and renal parenchymal volume) when compared to SCr and creatinine-based eGFR. METHODS: Prospective patients undergoing partial (PN) or radical nephrectomy (RN) had SCr, Cys C, and SCr and Cys C-based GFR measured pre-operatively (within 3 months of surgery), on post-operative day 1 (POD1), and 3-6 months after surgery. Preoperative and post-operative abdomen CT or MRI studies were obtained. Slice-O-Matic software (TomoVision, Magog, Canada) was used to outline kidney parenchyma and renal masses (shown in figure 1) and construct 3-D measurements estimating renal volume. RESULTS: Of our 53 patients, 35 patients (66%) underwent RN and 18 (34%) underwent PN. Cys C measured pre-operatively (r= -.35, p=.015) and on POD1 (r=-.40, p=.004) inversely correlated to post-operative kidney volume. SCr measured preoperatively (r=.-34, p=.016) and on POD1 (r=.-31, p=.028) demonstrated inverse correlation with post-operative kidney volume. POD1 Cys C (r=.33, p=.023) and SCr (r=.33, p=.026) correlated with presence of proteinuria at 3-6 months post-nephrectomy. eGFR at pre-operative visit and on POD1 utilizing Cys C-based (r=.32, p=.026; r=.45,p=.001, respectively) and creatinine-based CKD-EPI (r=.50, p=.000; r=.47, p=.001, respectively) formulas demonstrated a direct relationship to post-operative kidney volume. eGFR calculated from creatinine based CKD-EPI demonstrated an inverse correlation to presence of proteinuria at 3-6 months post-operatively (r=-.33, p =.027). CONCLUSIONS: Cystatin C as well as Cys C-based eGFR measured preoperatively and on POD1 may have some utility in estimating post-surgical renal volume and function. Further studies are needed to determine the utility of Cys C levels in predicting renal function and risk of renal dysfunction following nephrectomy. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1085-e1085 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ben Petrinec More articles by this author Ian Cooke More articles by this author Eric Midenburg More articles by this author Kenneth Ogan More articles by this author Viraj Master More articles by this author Expand All Advertisement Loading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".