title: Vascular Surgery Kidney Injury Predictive Score: A Historical Cohort Study.
abstract: OBJECTIVE
  To develop a risk-prediction model for acute kidney injury (AKI) in patients undergoing vascular surgery.
  
  
  METHODS
  A retrospective cohort study.
  
  
  METHODS
  A tertiary referral center.
  
  
  METHODS
  Participants included 845 adult patients who underwent vascular surgery between January 3, 2003, and May 29, 2008.
  
  
  METHODS
  None.
  
  
  RESULTS
  The median age of patients was 72 years (interquartile range 65-80 years), and 653 patients (77%) were male. AKI developed in 258 (30.5%) patients. Patients with AKI had lower estimated glomerular filtration rates (60±21 v 72±21, p<0.001), were older (73 [68-78] years v 71 [65-80] years, p = 0.01), had a higher prevalence of hypertension (81% v 73%, p = 0.02), and were more likely to undergo emergency surgery (5% v 2%, p = 0.02). Patients with AKI also received more diuretics (p<0.001) and β-blockers (p = 0.003) prior to surgery. The multivariate AKI risk-prediction model with preoperative variables (estimated glomerular filtration rate, previous vascular interventions, use of preoperative diuretics and β-blockers, and emergency surgery) showed an area under the receiver operating characteristic curve of 0.67 (95% confidence interval, 0.628-0.710); a model with additional intraoperative variables (procedure duration, fluid balance, and plasma and platelet transfusion) had an area under the receiver operating characteristic curve of 0.72 (95% confidence interval, 0.685-0.760).
  
  
  CONCLUSIONS
  As AKI is a very common complication after vascular surgery, a risk-prediction model was derived to assess the likelihood of postoperative AKI. If validated in an independent cohort, this model may be used to facilitate targeted interventions in vascular surgery patients at high risk for AKI.
authors.affiliation: Divisions of Nephrology and Hypertension; Division of Pulmonary and Critical Care, Department of Medicine. Electronic address: kashani.kianoush@mayo.edu.
authors.forename: Kianoush
authors.initials: K
authors.lastname: Kashani
authors.affiliation: Division of Pulmonary and Critical Care, Department of Medicine.
authors.forename: Jon H
authors.initials: JH
authors.lastname: Steuernagle
authors.affiliation: Department of Anesthesiology.
authors.forename: Abbasali
authors.initials: A
authors.lastname: Akhoundi
authors.affiliation: Division of Pulmonary and Critical Care, Department of Medicine.
authors.forename: Anas
authors.initials: A
authors.lastname: Alsara
authors.affiliation: Division of Biomedical Statistics & Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Hanson
authors.affiliation: Department of Anesthesiology.
authors.forename: Daryl J
authors.initials: DJ
authors.lastname: Kor
mesh_terms: D058186:Acute Kidney Injury / Q000175:diagnosis* / Q000453:epidemiology*; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications / Q000175:diagnosis* / Q000453:epidemiology*; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012307:Risk Factors; D014656:Vascular Surgical Procedures / Q000009:adverse effects*
keywords: acute kidney injury; fluid overload; predictive model; vascular surgery