Predicting Renal Function Outcomes After Partial and Radical Nephrectomy

Bimal Bhindi, Christine M. Lohse, Phillip Schulte, Ross J. Mason, John Cheville, Stephen A. Boorjian, Bradley Leibovich, R. Houston Thompson

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Background: Partial nephrectomy (PN) is generally favored for cT1 tumors over radical nephrectomy (RN) when technically feasible. However, it can be unclear whether the additional risks of PN are worth the magnitude of renal function benefit. Objective: To develop preoperative tools to predict long-term estimated glomerular filtration rate (eGFR) beyond 30 d following PN and RN, separately. Design, setting, and participants: In this retrospective cohort study, patients who underwent RN or PN for a single nonmetastatic renal tumor between 1997 and 2014 at our institution were identified. Exclusion criteria were venous tumor thrombus and preoperative eGFR <15 ml/min/1.73 m2. Intervention: RN and PN. Outcome measurements and statistical analysis: Hierarchical generalized linear mixed-effect models with backward selection of candidate preoperative features were used to predict long-term eGFR following RN and PN, separately. Predictive ability was summarized using marginal RGLMM 2, which ranges from 0 to 1, with higher values indicating increased predictive ability. Results and limitations: The analysis included 1152 patients (13 206 eGFR observations) who underwent RN and 1920 patients (18 652 eGFR observations) who underwent PN, with mean preoperative eGFRs of 66 ml/min/1.73 m2 (standard deviation [SD] = 18) and 72 ml/min/1.73 m2 (SD = 20), respectively. The model to predict eGFR after RN included age, diabetes, preoperative eGFR, preoperative proteinuria, tumor size, time from surgery, and an interaction between time from surgery and age (marginal RGLMM 2=0.41). The model to predict eGFR after PN included age, presence of a solitary kidney, diabetes, hypertension, preoperative eGFR, preoperative proteinuria, surgical approach, time from surgery, and interaction terms between time from surgery and age, diabetes, preoperative eGFR, and preoperative proteinuria (marginal RGLMM 2). Limitations include the lack of data on renal tumor complexity and the single-center design; generalizability needs to be confirmed in external cohorts. Conclusions: We developed preoperative tools to predict renal function outcomes following RN and PN. Pending validation, these tools should be helpful for patient counseling and clinical decision-making. Patient summary: We developed models to predict kidney function outcomes after partial and radical nephrectomy based on preoperative features. This should help clinicians during patient counseling and decision-making in the management of kidney tumors. We created prediction models for postoperative renal function after partial and radical nephrectomy based on preoperative features. These models may help clinicians in patient counseling and decision-making for the management of renal masses, particularly those with high anatomic complexity.

Original languageEnglish (US)
JournalEuropean Urology
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Nephrectomy
Kidney
Glomerular Filtration Rate
Proteinuria
Counseling
Neoplasms
Aptitude
Decision Making

Keywords

  • Chronic renal insufficiency
  • Glomerular filtration rate
  • Kidney neoplasms
  • Nephrectomy
  • Renal cell carcinoma

ASJC Scopus subject areas

  • Urology

Cite this

Bhindi, B., Lohse, C. M., Schulte, P., Mason, R. J., Cheville, J., Boorjian, S. A., ... Thompson, R. H. (Accepted/In press). Predicting Renal Function Outcomes After Partial and Radical Nephrectomy. European Urology. https://doi.org/10.1016/j.eururo.2018.11.021

Predicting Renal Function Outcomes After Partial and Radical Nephrectomy. / Bhindi, Bimal; Lohse, Christine M.; Schulte, Phillip; Mason, Ross J.; Cheville, John; Boorjian, Stephen A.; Leibovich, Bradley; Thompson, R. Houston.

In: European Urology, 01.01.2018.

Research output: Contribution to journalArticle

Bhindi, B, Lohse, CM, Schulte, P, Mason, RJ, Cheville, J, Boorjian, SA, Leibovich, B & Thompson, RH 2018, 'Predicting Renal Function Outcomes After Partial and Radical Nephrectomy', European Urology. https://doi.org/10.1016/j.eururo.2018.11.021
Bhindi, Bimal ; Lohse, Christine M. ; Schulte, Phillip ; Mason, Ross J. ; Cheville, John ; Boorjian, Stephen A. ; Leibovich, Bradley ; Thompson, R. Houston. / Predicting Renal Function Outcomes After Partial and Radical Nephrectomy. In: European Urology. 2018.
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abstract = "Background: Partial nephrectomy (PN) is generally favored for cT1 tumors over radical nephrectomy (RN) when technically feasible. However, it can be unclear whether the additional risks of PN are worth the magnitude of renal function benefit. Objective: To develop preoperative tools to predict long-term estimated glomerular filtration rate (eGFR) beyond 30 d following PN and RN, separately. Design, setting, and participants: In this retrospective cohort study, patients who underwent RN or PN for a single nonmetastatic renal tumor between 1997 and 2014 at our institution were identified. Exclusion criteria were venous tumor thrombus and preoperative eGFR <15 ml/min/1.73 m2. Intervention: RN and PN. Outcome measurements and statistical analysis: Hierarchical generalized linear mixed-effect models with backward selection of candidate preoperative features were used to predict long-term eGFR following RN and PN, separately. Predictive ability was summarized using marginal RGLMM 2, which ranges from 0 to 1, with higher values indicating increased predictive ability. Results and limitations: The analysis included 1152 patients (13 206 eGFR observations) who underwent RN and 1920 patients (18 652 eGFR observations) who underwent PN, with mean preoperative eGFRs of 66 ml/min/1.73 m2 (standard deviation [SD] = 18) and 72 ml/min/1.73 m2 (SD = 20), respectively. The model to predict eGFR after RN included age, diabetes, preoperative eGFR, preoperative proteinuria, tumor size, time from surgery, and an interaction between time from surgery and age (marginal RGLMM 2=0.41). The model to predict eGFR after PN included age, presence of a solitary kidney, diabetes, hypertension, preoperative eGFR, preoperative proteinuria, surgical approach, time from surgery, and interaction terms between time from surgery and age, diabetes, preoperative eGFR, and preoperative proteinuria (marginal RGLMM 2). Limitations include the lack of data on renal tumor complexity and the single-center design; generalizability needs to be confirmed in external cohorts. Conclusions: We developed preoperative tools to predict renal function outcomes following RN and PN. Pending validation, these tools should be helpful for patient counseling and clinical decision-making. Patient summary: We developed models to predict kidney function outcomes after partial and radical nephrectomy based on preoperative features. This should help clinicians during patient counseling and decision-making in the management of kidney tumors. We created prediction models for postoperative renal function after partial and radical nephrectomy based on preoperative features. These models may help clinicians in patient counseling and decision-making for the management of renal masses, particularly those with high anatomic complexity.",
keywords = "Chronic renal insufficiency, Glomerular filtration rate, Kidney neoplasms, Nephrectomy, Renal cell carcinoma",
author = "Bimal Bhindi and Lohse, {Christine M.} and Phillip Schulte and Mason, {Ross J.} and John Cheville and Boorjian, {Stephen A.} and Bradley Leibovich and Thompson, {R. Houston}",
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T1 - Predicting Renal Function Outcomes After Partial and Radical Nephrectomy

AU - Bhindi, Bimal

AU - Lohse, Christine M.

AU - Schulte, Phillip

AU - Mason, Ross J.

AU - Cheville, John

AU - Boorjian, Stephen A.

AU - Leibovich, Bradley

AU - Thompson, R. Houston

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Background: Partial nephrectomy (PN) is generally favored for cT1 tumors over radical nephrectomy (RN) when technically feasible. However, it can be unclear whether the additional risks of PN are worth the magnitude of renal function benefit. Objective: To develop preoperative tools to predict long-term estimated glomerular filtration rate (eGFR) beyond 30 d following PN and RN, separately. Design, setting, and participants: In this retrospective cohort study, patients who underwent RN or PN for a single nonmetastatic renal tumor between 1997 and 2014 at our institution were identified. Exclusion criteria were venous tumor thrombus and preoperative eGFR <15 ml/min/1.73 m2. Intervention: RN and PN. Outcome measurements and statistical analysis: Hierarchical generalized linear mixed-effect models with backward selection of candidate preoperative features were used to predict long-term eGFR following RN and PN, separately. Predictive ability was summarized using marginal RGLMM 2, which ranges from 0 to 1, with higher values indicating increased predictive ability. Results and limitations: The analysis included 1152 patients (13 206 eGFR observations) who underwent RN and 1920 patients (18 652 eGFR observations) who underwent PN, with mean preoperative eGFRs of 66 ml/min/1.73 m2 (standard deviation [SD] = 18) and 72 ml/min/1.73 m2 (SD = 20), respectively. The model to predict eGFR after RN included age, diabetes, preoperative eGFR, preoperative proteinuria, tumor size, time from surgery, and an interaction between time from surgery and age (marginal RGLMM 2=0.41). The model to predict eGFR after PN included age, presence of a solitary kidney, diabetes, hypertension, preoperative eGFR, preoperative proteinuria, surgical approach, time from surgery, and interaction terms between time from surgery and age, diabetes, preoperative eGFR, and preoperative proteinuria (marginal RGLMM 2). Limitations include the lack of data on renal tumor complexity and the single-center design; generalizability needs to be confirmed in external cohorts. Conclusions: We developed preoperative tools to predict renal function outcomes following RN and PN. Pending validation, these tools should be helpful for patient counseling and clinical decision-making. Patient summary: We developed models to predict kidney function outcomes after partial and radical nephrectomy based on preoperative features. This should help clinicians during patient counseling and decision-making in the management of kidney tumors. We created prediction models for postoperative renal function after partial and radical nephrectomy based on preoperative features. These models may help clinicians in patient counseling and decision-making for the management of renal masses, particularly those with high anatomic complexity.

AB - Background: Partial nephrectomy (PN) is generally favored for cT1 tumors over radical nephrectomy (RN) when technically feasible. However, it can be unclear whether the additional risks of PN are worth the magnitude of renal function benefit. Objective: To develop preoperative tools to predict long-term estimated glomerular filtration rate (eGFR) beyond 30 d following PN and RN, separately. Design, setting, and participants: In this retrospective cohort study, patients who underwent RN or PN for a single nonmetastatic renal tumor between 1997 and 2014 at our institution were identified. Exclusion criteria were venous tumor thrombus and preoperative eGFR <15 ml/min/1.73 m2. Intervention: RN and PN. Outcome measurements and statistical analysis: Hierarchical generalized linear mixed-effect models with backward selection of candidate preoperative features were used to predict long-term eGFR following RN and PN, separately. Predictive ability was summarized using marginal RGLMM 2, which ranges from 0 to 1, with higher values indicating increased predictive ability. Results and limitations: The analysis included 1152 patients (13 206 eGFR observations) who underwent RN and 1920 patients (18 652 eGFR observations) who underwent PN, with mean preoperative eGFRs of 66 ml/min/1.73 m2 (standard deviation [SD] = 18) and 72 ml/min/1.73 m2 (SD = 20), respectively. The model to predict eGFR after RN included age, diabetes, preoperative eGFR, preoperative proteinuria, tumor size, time from surgery, and an interaction between time from surgery and age (marginal RGLMM 2=0.41). The model to predict eGFR after PN included age, presence of a solitary kidney, diabetes, hypertension, preoperative eGFR, preoperative proteinuria, surgical approach, time from surgery, and interaction terms between time from surgery and age, diabetes, preoperative eGFR, and preoperative proteinuria (marginal RGLMM 2). Limitations include the lack of data on renal tumor complexity and the single-center design; generalizability needs to be confirmed in external cohorts. Conclusions: We developed preoperative tools to predict renal function outcomes following RN and PN. Pending validation, these tools should be helpful for patient counseling and clinical decision-making. Patient summary: We developed models to predict kidney function outcomes after partial and radical nephrectomy based on preoperative features. This should help clinicians during patient counseling and decision-making in the management of kidney tumors. We created prediction models for postoperative renal function after partial and radical nephrectomy based on preoperative features. These models may help clinicians in patient counseling and decision-making for the management of renal masses, particularly those with high anatomic complexity.

KW - Chronic renal insufficiency

KW - Glomerular filtration rate

KW - Kidney neoplasms

KW - Nephrectomy

KW - Renal cell carcinoma

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