Preoperative Nomograms for Predicting Renal Function at 1 Year after Partial Nephrectomy

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Abstract

Introduction: Partial nephrectomy (PN) reduces the risk of postoperative chronic renal insufficiency (CRI). However, some patients still develop CRI after PN, and may eventually require dialysis. Being able to predict renal function before PN helps in counseling patients and managing expectations. We aimed to construct nomograms that predict estimated glomerular filtration rates (eGFRs), defined by the modification of diet in renal disease (MDRD) and the chronic kidney disease epidemiology collaboration (CKD-EPI) formulae, at 1 year after PN, using only preoperative covariates as predictors. Patients and Methods: We identified patients who underwent PN in our institution between 2004 and 2016, with known postoperative serum creatinine levels at 1 year. The preoperative covariates included patients' demographics, chronic comorbid conditions, tumor characteristics, and preoperative renal status. The endpoints were eGFRs at 1 year after PN, calculated using the MDRD and the CKD-EPI formulae. We first identified preoperative covariates with significant associations with the endpoints by Pearson correlation and independent samples t-test. Suitable covariates were then included in two multivariate linear regression models, for constructing and internally validating two nomograms. Results: 461 patients were eligible for analysis. The percentage of patients with eGFR below 60 mL/min/1.73 m2 increased from 25% before PN to 35% at 1 year after PN. We included age, gender, African American race, body mass index, preoperative creatinine level, ipsilateral renal volume, solitary kidney status, tumor diameter, hypertension, diabetes, ischemic heart disease, and previous stroke in the multivariate linear regression models for nomogram construction. Internal validation showed bootstrap-corrected coefficients of determination of 0.61 and 0.70, for predicting eGFRs defined by the MDRD and CKD-EPI formulae, respectively. Conclusions: We constructed and internally validated two nomograms to predict eGFRs at 1 year after PN, using only preoperative covariates as predictors.

Original languageEnglish (US)
Pages (from-to)711-718
Number of pages8
JournalJournal of Endourology
Volume31
Issue number7
DOIs
StatePublished - Jul 1 2017

Fingerprint

Nomograms
Nephrectomy
Kidney
Glomerular Filtration Rate
Chronic Renal Insufficiency
Diet Therapy
Linear Models
Epidemiology
Creatinine
African Americans
Myocardial Ischemia
Counseling
Dialysis
Neoplasms
Body Mass Index
Stroke
Demography
Hypertension

Keywords

  • Nomogram
  • partial nephrectomy
  • postoperative renal function

ASJC Scopus subject areas

  • Urology

Cite this

@article{6f24b5a568264fdcb881c27d2dc3d791,
title = "Preoperative Nomograms for Predicting Renal Function at 1 Year after Partial Nephrectomy",
abstract = "Introduction: Partial nephrectomy (PN) reduces the risk of postoperative chronic renal insufficiency (CRI). However, some patients still develop CRI after PN, and may eventually require dialysis. Being able to predict renal function before PN helps in counseling patients and managing expectations. We aimed to construct nomograms that predict estimated glomerular filtration rates (eGFRs), defined by the modification of diet in renal disease (MDRD) and the chronic kidney disease epidemiology collaboration (CKD-EPI) formulae, at 1 year after PN, using only preoperative covariates as predictors. Patients and Methods: We identified patients who underwent PN in our institution between 2004 and 2016, with known postoperative serum creatinine levels at 1 year. The preoperative covariates included patients' demographics, chronic comorbid conditions, tumor characteristics, and preoperative renal status. The endpoints were eGFRs at 1 year after PN, calculated using the MDRD and the CKD-EPI formulae. We first identified preoperative covariates with significant associations with the endpoints by Pearson correlation and independent samples t-test. Suitable covariates were then included in two multivariate linear regression models, for constructing and internally validating two nomograms. Results: 461 patients were eligible for analysis. The percentage of patients with eGFR below 60 mL/min/1.73 m2 increased from 25{\%} before PN to 35{\%} at 1 year after PN. We included age, gender, African American race, body mass index, preoperative creatinine level, ipsilateral renal volume, solitary kidney status, tumor diameter, hypertension, diabetes, ischemic heart disease, and previous stroke in the multivariate linear regression models for nomogram construction. Internal validation showed bootstrap-corrected coefficients of determination of 0.61 and 0.70, for predicting eGFRs defined by the MDRD and CKD-EPI formulae, respectively. Conclusions: We constructed and internally validated two nomograms to predict eGFRs at 1 year after PN, using only preoperative covariates as predictors.",
keywords = "Nomogram, partial nephrectomy, postoperative renal function",
author = "Shum, {Cheuk Fan} and Clinton Bahler and Cary, {K. Clinton} and Timothy Masterson and Boris, {Ronald S.} and Thomas Gardner and Hristos Kaimakliotis and Richard Foster and Richard Bihrle and Michael Koch and Slaven, {James E.} and Chandru Sundaram",
year = "2017",
month = "7",
day = "1",
doi = "10.1089/end.2017.0184",
language = "English (US)",
volume = "31",
pages = "711--718",
journal = "Journal of Endourology",
issn = "0892-7790",
publisher = "Mary Ann Liebert Inc.",
number = "7",

}

TY - JOUR

T1 - Preoperative Nomograms for Predicting Renal Function at 1 Year after Partial Nephrectomy

AU - Shum, Cheuk Fan

AU - Bahler, Clinton

AU - Cary, K. Clinton

AU - Masterson, Timothy

AU - Boris, Ronald S.

AU - Gardner, Thomas

AU - Kaimakliotis, Hristos

AU - Foster, Richard

AU - Bihrle, Richard

AU - Koch, Michael

AU - Slaven, James E.

AU - Sundaram, Chandru

PY - 2017/7/1

Y1 - 2017/7/1

N2 - Introduction: Partial nephrectomy (PN) reduces the risk of postoperative chronic renal insufficiency (CRI). However, some patients still develop CRI after PN, and may eventually require dialysis. Being able to predict renal function before PN helps in counseling patients and managing expectations. We aimed to construct nomograms that predict estimated glomerular filtration rates (eGFRs), defined by the modification of diet in renal disease (MDRD) and the chronic kidney disease epidemiology collaboration (CKD-EPI) formulae, at 1 year after PN, using only preoperative covariates as predictors. Patients and Methods: We identified patients who underwent PN in our institution between 2004 and 2016, with known postoperative serum creatinine levels at 1 year. The preoperative covariates included patients' demographics, chronic comorbid conditions, tumor characteristics, and preoperative renal status. The endpoints were eGFRs at 1 year after PN, calculated using the MDRD and the CKD-EPI formulae. We first identified preoperative covariates with significant associations with the endpoints by Pearson correlation and independent samples t-test. Suitable covariates were then included in two multivariate linear regression models, for constructing and internally validating two nomograms. Results: 461 patients were eligible for analysis. The percentage of patients with eGFR below 60 mL/min/1.73 m2 increased from 25% before PN to 35% at 1 year after PN. We included age, gender, African American race, body mass index, preoperative creatinine level, ipsilateral renal volume, solitary kidney status, tumor diameter, hypertension, diabetes, ischemic heart disease, and previous stroke in the multivariate linear regression models for nomogram construction. Internal validation showed bootstrap-corrected coefficients of determination of 0.61 and 0.70, for predicting eGFRs defined by the MDRD and CKD-EPI formulae, respectively. Conclusions: We constructed and internally validated two nomograms to predict eGFRs at 1 year after PN, using only preoperative covariates as predictors.

AB - Introduction: Partial nephrectomy (PN) reduces the risk of postoperative chronic renal insufficiency (CRI). However, some patients still develop CRI after PN, and may eventually require dialysis. Being able to predict renal function before PN helps in counseling patients and managing expectations. We aimed to construct nomograms that predict estimated glomerular filtration rates (eGFRs), defined by the modification of diet in renal disease (MDRD) and the chronic kidney disease epidemiology collaboration (CKD-EPI) formulae, at 1 year after PN, using only preoperative covariates as predictors. Patients and Methods: We identified patients who underwent PN in our institution between 2004 and 2016, with known postoperative serum creatinine levels at 1 year. The preoperative covariates included patients' demographics, chronic comorbid conditions, tumor characteristics, and preoperative renal status. The endpoints were eGFRs at 1 year after PN, calculated using the MDRD and the CKD-EPI formulae. We first identified preoperative covariates with significant associations with the endpoints by Pearson correlation and independent samples t-test. Suitable covariates were then included in two multivariate linear regression models, for constructing and internally validating two nomograms. Results: 461 patients were eligible for analysis. The percentage of patients with eGFR below 60 mL/min/1.73 m2 increased from 25% before PN to 35% at 1 year after PN. We included age, gender, African American race, body mass index, preoperative creatinine level, ipsilateral renal volume, solitary kidney status, tumor diameter, hypertension, diabetes, ischemic heart disease, and previous stroke in the multivariate linear regression models for nomogram construction. Internal validation showed bootstrap-corrected coefficients of determination of 0.61 and 0.70, for predicting eGFRs defined by the MDRD and CKD-EPI formulae, respectively. Conclusions: We constructed and internally validated two nomograms to predict eGFRs at 1 year after PN, using only preoperative covariates as predictors.

KW - Nomogram

KW - partial nephrectomy

KW - postoperative renal function

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U2 - 10.1089/end.2017.0184

DO - 10.1089/end.2017.0184

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