title: Does nephrometry scoring of renal tumors predict outcomes in patients selected for robot-assisted partial nephrectomy?
abstract: OBJECTIVE
  Robot-assisted partial nephrectomy has emerged as a viable surgical treatment for patients with certain renal tumors. We hypothesized that extirpation of more complex tumors, as graded with the nephrometry score, would result in worse operative and postoperative outcomes when compared with tumors with lower nephrometry scores. We report whether nephrometry-graded tumor complexity impacted operative or postoperative outcomes.
  
  
  METHODS
  A single experienced surgeon at our tertiary-care institution performed more than 100 robot-assisted partial nephrectomies. Istitutional Review Board-approved data collection was available for 95 patients, and nephrometry scores were available for 92 patients. Cases were divided into tertiles, based on their nephrometry score of low, medium, or high. We compared preoperative, operative, and postoperative data to evaluate any differences between the three tertiles. Statistical analysis was performed using JMP 8 software.
  
  
  RESULTS
  There were 66, 22, and 4 patients in the low, medium, and high nephrometry score tertiles, respectively. There were no statistically significant differences between the tertiles regarding warm ischemia time, estimated blood loss, operative time, length of stay, change in glomerular filtration rate, Clavien-graded complication rates, or any other metric. Mean follow-up for each tertile was also similar.
  
  
  CONCLUSIONS
  We have routinely been using the nephrometry scoring system to anatomically describe renal masses before robot-assisted partial nephrectomy. Our findings demonstrate that nephrometry-graded tumor complexity was not related to any differences in outcomes for patients with renal tumors who were selected at our institution to undergo robot-assisted partial nephrectomy. The nephrometry system remains a reproducible standardized classification of renal tumor anatomy, but it remains to be seen if this can be used to predict surgical outcomes.
authors.affiliation: Department of Urology, Wake Forest University Baptist Medical Center, Winston-Salem, NC 27104, USA.
authors.forename: Patrick W
authors.initials: PW
authors.lastname: Mufarrij
authors.forename: L Spencer
authors.initials: LS
authors.lastname: Krane
authors.forename: Srinivas
authors.initials: S
authors.lastname: Rajamahanty
authors.forename: Ashok K
authors.initials: AK
authors.lastname: Hemal
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D009392:Nephrectomy / Q000379:methods*; D019990:Perioperative Care; D012189:Retrospective Studies; D012371:Robotics; D016896:Treatment Outcome