Predicting Adherent Perinephric Fat Using Preoperative Clinical and Radiological Factors in Patients Undergoing Partial Nephrectomy

Abstract Background The decision to perform a partial nephrectomy (PN) relies largely upon the complexity of the renal mass and its surrounding anatomy. The presence of adherent perinephric fat (APF) can increase surgical complexity and extend operative times. The accurate prediction of APF may improve surgical planning and aid in decision making for the surgical approach. Objective We sought to develop and externally validate a score that predicts APF based on preoperative clinical and radiological prognostic factors. Design, setting, and participants We retrospectively analyzed 495 consecutive patients who underwent open or minimally invasive PN. APF was defined as the presence of “dense,” “adherent,” or “sticky” perinephric fat at the time of dissection by the surgeon, and this did not require subcapsular dissection. Additionally, we analyzed an independent cohort of 285 patients for external validation. Outcome measurements and statistical analysis A score model was developed using multivariate logistic regression analysis. Calibration of the fitted model was assessed graphically with a plot of the predicted versus the actual probability of APF, and discrimination was assessed by calculating the area under the receiver operating characteristic curve. Results and limitations Of the 495 patients, 95 (19%) had APF. Patients with APF had longer operative (p = 0.02) and arterial clamp (p = 0.01) times than non-APF patients. On multivariate analyses, diabetes mellitus (p = 0.009), posterior perinephric fat thickness (p  Conclusions The APF score can accurately predict the presence of APF in patients with a small renal mass who are planning to undergo PN. This score could aid in pre- and intraoperative planning and impact the surgical approach. Patient summary The presence of “sticky” fat surrounding the kidney in patients undergoing partial nephrectomy has previously been linked to longer operative times, intraoperative complications, and surgical conversion. In our study, we found that this feature is more often presented in patients with diabetes mellitus, and thicker and more inflammatory fat on renal imaging. Based on these findings, we developed a risk score that can accurately predict this feature before surgery, in order to improve surgical planning and better counsel the patients.