title: Understanding criteria for surveillance of patients with a small renal mass.
abstract: OBJECTIVE
  To better delineate which factors influence the decision to undergo active surveillance of small renal masses.
  
  
  METHODS
  We identified 204 consecutive patients at our institution with clinical Stage T1 renal masses from June 2009 through June 2010. A variety of demographic and clinical characteristics were measured. Based on our previous work, the "ideal" criteria for active surveillance included tumor size ≤ 4 cm, Charlson comorbidity index of ≥ 2, Eastern Cooperative Oncology Group (ECOG) performance status (PS) of ≥ 2, and estimated glomerular filtration rate <60 mL/min. We performed sensitivity analyses to identify the characteristics associated with choice of active surveillance and compared these with our "ideal" criteria.
  
  
  RESULTS
  Of the 204 patients, 73 (36%) and 131 (64%) underwent active surveillance and treatment, respectively. The patients undergoing active surveillance versus treatment differed with respect to distance from hospital >60 miles (P = .04), ECOG PS of ≥ 2 (P < .01), tumor size (P < .01), multifocality (P = .03), endophytic nature of lesion (P = .04), and whether the patient's surgeon generally used a robotic, laparoscopic, or open approach (P = .01). Neither the baseline estimated glomerular filtration rate (P = .91) nor the Charlson comorbidity index (P = .69) were significant factors. The combination of tumor size <3 cm, ECOG PS of ≥ 2, and an endophytic lesion were most predictive of active surveillance.
  
  
  CONCLUSIONS
  Patient, tumor, and surgeon characteristics all influence the choice of active surveillance. From the sensitivity analyses, active surveillance was driven by a tumor size <3 cm, poor PS (ie, ECOG PS of ≥ 2), and an endophytic lesion.
authors.affiliation: Department of Urology, Division of Oncology, University of Michigan Health System, Ann Arbor, Michigan 48109-2800, USA. brucejac@med.umich.edu
authors.forename: Bruce L
authors.initials: BL
authors.lastname: Jacobs
authors.forename: Hung-Jui
authors.initials: HJ
authors.lastname: Tan
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Montgomery
authors.forename: Alon Z
authors.initials: AZ
authors.lastname: Weizer
authors.forename: David P
authors.initials: DP
authors.lastname: Wood
authors.forename: David C
authors.initials: DC
authors.lastname: Miller
authors.forename: J Stuart
authors.initials: JS
authors.lastname: Wolf
authors.forename: Khaled S
authors.initials: KS
authors.lastname: Hafez
mesh_terms: D000368:Aged; D016009:Chi-Square Distribution; D005260:Female; D005919:Glomerular Filtration Rate; D006297:Health Services Accessibility; D006801:Humans; D007680:Kidney Neoplasms / Q000473:pathology* / Q000503:physiopathology* / Q000601:surgery; D010535:Laparoscopy; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009392:Nephrectomy; D010818:Practice Patterns, Physicians'; D012189:Retrospective Studies; D012371:Robotics; D047368:Tumor Burden; D057832:Watchful Waiting / Q000706:statistics & numerical data*