Andrew J Davidiuk1, Alexander S Parker2, Colleen S Thomas3, Bradley C Leibovich4, Erik P Castle5, Michael G Heckman3, Kaitlynn Custer2, David D Thiel6. 1. Department of Urology, Mayo Clinic, Jacksonville, FL, USA. 2. Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA. 3. Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL, USA. 4. Department of Urology, Mayo Clinic, Rochester, MN, USA. 5. Department of Urology, Mayo Clinic, Phoenix, AZ, USA. 6. Department of Urology, Mayo Clinic, Jacksonville, FL, USA. Electronic address: Thiel.David@mayo.edu.
Abstract
BACKGROUND: Image-based renal morphometry scoring systems are used to predict the potential difficulty of partial nephrectomy (PN), but they are centered entirely on tumor-specific factors and neglect other patient-specific factors that may complicate the technical aspects of PN. Adherent perinephric fat (APF) is one such factor known to make PN difficult. OBJECTIVE: To develop an accurate image-based nephrometry scoring system to predict the presence of APF encountered during robot-assisted partial nephrectomy (RAPN). DESIGN, SETTING, AND PARTICIPANTS: We prospectively analyzed 100 consecutive RAPNs performed by one surgeon and defined APF as the need for subcapsular renal dissection to isolate the renal tumor for RAPN. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The scoring algorithm to predict the presence of APF was developed with a multivariable logistic regression model using a forward selection approach with a focus on improvement in the area under the receiver operating characteristic curve. RESULTS AND LIMITATIONS: Thirty patients (30%; 95% confidence interval, 21-40) had APF. Single-variable analysis noted an increased likelihood of APF in male patients (p<0.001), higher body mass index (p=0.003), greater posterior perinephric fat thickness (p<0.001), greater lateral perinephric fat thickness (p<0.001), and those with perirenal fat stranding (p<0.001). Two of these variables, posterior perinephric fat thickness and stranding, were most highly predictive of APF in multivariable analysis and were therefore used to create a risk score, termed Mayo Adhesive Probability (MAP) and ranging from 0 to 5, to predict the presence of APF. We observed APF in 6% of patients with a MAP score of 0, 16% with a score of 1, 31% with a score of 2, 73% with a score of 3-4, and 100% of patients with a score of 5. CONCLUSIONS: MAP score accurately predicts the presence of APF in patients undergoing RAPN. Prospective validation of the MAP score is required. PATIENT SUMMARY: The Mayo Adhesive Probability score that we we developed is an accurate system that predicts whether or not adherent perinephric, or "sticky," fat is present around the kidney that would make partial nephrectomy difficult.
BACKGROUND: Image-based renal morphometry scoring systems are used to predict the potential difficulty of partial nephrectomy (PN), but they are centered entirely on tumor-specific factors and neglect other patient-specific factors that may complicate the technical aspects of PN. Adherent perinephric fat (APF) is one such factor known to make PN difficult. OBJECTIVE: To develop an accurate image-based nephrometry scoring system to predict the presence of APF encountered during robot-assisted partial nephrectomy (RAPN). DESIGN, SETTING, AND PARTICIPANTS: We prospectively analyzed 100 consecutive RAPNs performed by one surgeon and defined APF as the need for subcapsular renal dissection to isolate the renal tumor for RAPN. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The scoring algorithm to predict the presence of APF was developed with a multivariable logistic regression model using a forward selection approach with a focus on improvement in the area under the receiver operating characteristic curve. RESULTS AND LIMITATIONS: Thirty patients (30%; 95% confidence interval, 21-40) had APF. Single-variable analysis noted an increased likelihood of APF in male patients (p<0.001), higher body mass index (p=0.003), greater posterior perinephric fat thickness (p<0.001), greater lateral perinephric fat thickness (p<0.001), and those with perirenal fat stranding (p<0.001). Two of these variables, posterior perinephric fat thickness and stranding, were most highly predictive of APF in multivariable analysis and were therefore used to create a risk score, termed Mayo Adhesive Probability (MAP) and ranging from 0 to 5, to predict the presence of APF. We observed APF in 6% of patients with a MAP score of 0, 16% with a score of 1, 31% with a score of 2, 73% with a score of 3-4, and 100% of patients with a score of 5. CONCLUSIONS: MAP score accurately predicts the presence of APF in patients undergoing RAPN. Prospective validation of the MAP score is required. PATIENT SUMMARY: The Mayo Adhesive Probability score that we we developed is an accurate system that predicts whether or not adherent perinephric, or "sticky," fat is present around the kidney that would make partial nephrectomy difficult.