A novel mathematical model to predict the severity of postoperative functional reduction before partial nephrectomy: The importance of calculating resected and ischemic volume

Tae Young Shin, Christos Komninos, Dong Wook Kim, Keum Sook So, Ki Seok Bang, Heon Jae Jeong, Woong Kyu Han, Sung Jun Hong, Byung Ha Jung, Sey Kiat Lim, Sang Kon Lee, Won Ki Lee, Koon Ho Rha

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Purpose Preoperatively predicting postoperative kidney function is an essential step to achieve improved renal function and prevent chronic kidney disease. We introduce a novel formula especially to calculate resected and ischemic volume before partial nephrectomy. We examined whether resected and ischemic volume would have value for predicting postoperative renal function. Materials and Methods We performed a retrospective cohort study in 210 patients who underwent robotic partial nephrectomy between September 2006 and October 2013 at a tertiary cancer care center. Based on abdominopelvic computerized tomography and magnetic resonance imaging we calculated resected and ischemic volume by the novel mathematical formula using integral calculus. We comparatively analyzed resected and ischemic volume, and current nephrometry systems to determine the degree of association and predictability regarding the severity of the postoperative functional reduction. Results On multivariable analysis resected and ischemic volume showed a superior association with the absolute change in estimated glomerular filtration rate/percent change in estimated glomerular filtration rate (B = 6.5, p = 0.005/B = 6.35, p = 0.009). The ROC AUC revealed accurate predictability of resected and ischemic volume on the stratified event of an absolute change in estimated glomerular filtration rate/event of percent change in estimated glomerular filtration rate compared to 3 representative nephrometry systems. The calibration plot of this model was excellent (close to the 45-degree line) within the whole range of predicted probabilities. Conclusions We report a method of preoperatively calculating resected and ischemic volume with a novel formula. This method has superior correlation with the absolute and percent change in estimated glomerular filtration rate compared to current nephrometry systems. The predictive model achieved a strong correlation for the absolute and percent change in estimated glomerular filtration rate.

Original languageEnglish
Pages (from-to)423-429
Number of pages7
JournalJournal of Urology
Volume193
Issue number2
DOIs
Publication statusPublished - 2015 Feb 1

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Nephrectomy
Glomerular Filtration Rate
Theoretical Models
Kidney
Calculi
Robotics
Chronic Renal Insufficiency
Tertiary Care Centers
Calibration
Area Under Curve
Cohort Studies
Retrospective Studies
Tomography
Magnetic Resonance Imaging
Neoplasms

All Science Journal Classification (ASJC) codes

  • Urology

Cite this

Shin, Tae Young ; Komninos, Christos ; Kim, Dong Wook ; So, Keum Sook ; Bang, Ki Seok ; Jeong, Heon Jae ; Han, Woong Kyu ; Hong, Sung Jun ; Jung, Byung Ha ; Lim, Sey Kiat ; Lee, Sang Kon ; Lee, Won Ki ; Rha, Koon Ho. / A novel mathematical model to predict the severity of postoperative functional reduction before partial nephrectomy : The importance of calculating resected and ischemic volume. In: Journal of Urology. 2015 ; Vol. 193, No. 2. pp. 423-429.
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abstract = "Purpose Preoperatively predicting postoperative kidney function is an essential step to achieve improved renal function and prevent chronic kidney disease. We introduce a novel formula especially to calculate resected and ischemic volume before partial nephrectomy. We examined whether resected and ischemic volume would have value for predicting postoperative renal function. Materials and Methods We performed a retrospective cohort study in 210 patients who underwent robotic partial nephrectomy between September 2006 and October 2013 at a tertiary cancer care center. Based on abdominopelvic computerized tomography and magnetic resonance imaging we calculated resected and ischemic volume by the novel mathematical formula using integral calculus. We comparatively analyzed resected and ischemic volume, and current nephrometry systems to determine the degree of association and predictability regarding the severity of the postoperative functional reduction. Results On multivariable analysis resected and ischemic volume showed a superior association with the absolute change in estimated glomerular filtration rate/percent change in estimated glomerular filtration rate (B = 6.5, p = 0.005/B = 6.35, p = 0.009). The ROC AUC revealed accurate predictability of resected and ischemic volume on the stratified event of an absolute change in estimated glomerular filtration rate/event of percent change in estimated glomerular filtration rate compared to 3 representative nephrometry systems. The calibration plot of this model was excellent (close to the 45-degree line) within the whole range of predicted probabilities. Conclusions We report a method of preoperatively calculating resected and ischemic volume with a novel formula. This method has superior correlation with the absolute and percent change in estimated glomerular filtration rate compared to current nephrometry systems. The predictive model achieved a strong correlation for the absolute and percent change in estimated glomerular filtration rate.",
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A novel mathematical model to predict the severity of postoperative functional reduction before partial nephrectomy : The importance of calculating resected and ischemic volume. / Shin, Tae Young; Komninos, Christos; Kim, Dong Wook; So, Keum Sook; Bang, Ki Seok; Jeong, Heon Jae; Han, Woong Kyu; Hong, Sung Jun; Jung, Byung Ha; Lim, Sey Kiat; Lee, Sang Kon; Lee, Won Ki; Rha, Koon Ho.

In: Journal of Urology, Vol. 193, No. 2, 01.02.2015, p. 423-429.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A novel mathematical model to predict the severity of postoperative functional reduction before partial nephrectomy

T2 - The importance of calculating resected and ischemic volume

AU - Shin, Tae Young

AU - Komninos, Christos

AU - Kim, Dong Wook

AU - So, Keum Sook

AU - Bang, Ki Seok

AU - Jeong, Heon Jae

AU - Han, Woong Kyu

AU - Hong, Sung Jun

AU - Jung, Byung Ha

AU - Lim, Sey Kiat

AU - Lee, Sang Kon

AU - Lee, Won Ki

AU - Rha, Koon Ho

PY - 2015/2/1

Y1 - 2015/2/1

N2 - Purpose Preoperatively predicting postoperative kidney function is an essential step to achieve improved renal function and prevent chronic kidney disease. We introduce a novel formula especially to calculate resected and ischemic volume before partial nephrectomy. We examined whether resected and ischemic volume would have value for predicting postoperative renal function. Materials and Methods We performed a retrospective cohort study in 210 patients who underwent robotic partial nephrectomy between September 2006 and October 2013 at a tertiary cancer care center. Based on abdominopelvic computerized tomography and magnetic resonance imaging we calculated resected and ischemic volume by the novel mathematical formula using integral calculus. We comparatively analyzed resected and ischemic volume, and current nephrometry systems to determine the degree of association and predictability regarding the severity of the postoperative functional reduction. Results On multivariable analysis resected and ischemic volume showed a superior association with the absolute change in estimated glomerular filtration rate/percent change in estimated glomerular filtration rate (B = 6.5, p = 0.005/B = 6.35, p = 0.009). The ROC AUC revealed accurate predictability of resected and ischemic volume on the stratified event of an absolute change in estimated glomerular filtration rate/event of percent change in estimated glomerular filtration rate compared to 3 representative nephrometry systems. The calibration plot of this model was excellent (close to the 45-degree line) within the whole range of predicted probabilities. Conclusions We report a method of preoperatively calculating resected and ischemic volume with a novel formula. This method has superior correlation with the absolute and percent change in estimated glomerular filtration rate compared to current nephrometry systems. The predictive model achieved a strong correlation for the absolute and percent change in estimated glomerular filtration rate.

AB - Purpose Preoperatively predicting postoperative kidney function is an essential step to achieve improved renal function and prevent chronic kidney disease. We introduce a novel formula especially to calculate resected and ischemic volume before partial nephrectomy. We examined whether resected and ischemic volume would have value for predicting postoperative renal function. Materials and Methods We performed a retrospective cohort study in 210 patients who underwent robotic partial nephrectomy between September 2006 and October 2013 at a tertiary cancer care center. Based on abdominopelvic computerized tomography and magnetic resonance imaging we calculated resected and ischemic volume by the novel mathematical formula using integral calculus. We comparatively analyzed resected and ischemic volume, and current nephrometry systems to determine the degree of association and predictability regarding the severity of the postoperative functional reduction. Results On multivariable analysis resected and ischemic volume showed a superior association with the absolute change in estimated glomerular filtration rate/percent change in estimated glomerular filtration rate (B = 6.5, p = 0.005/B = 6.35, p = 0.009). The ROC AUC revealed accurate predictability of resected and ischemic volume on the stratified event of an absolute change in estimated glomerular filtration rate/event of percent change in estimated glomerular filtration rate compared to 3 representative nephrometry systems. The calibration plot of this model was excellent (close to the 45-degree line) within the whole range of predicted probabilities. Conclusions We report a method of preoperatively calculating resected and ischemic volume with a novel formula. This method has superior correlation with the absolute and percent change in estimated glomerular filtration rate compared to current nephrometry systems. The predictive model achieved a strong correlation for the absolute and percent change in estimated glomerular filtration rate.

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