title: Description of a risk predictive model of 30-day postoperative mortality after elective abdominal aortic aneurysm repair.
abstract: OBJECTIVE
  Despite vast improvement in the field of vascular surgery, elective abdominal aortic aneurysm (AAA) repair still leads to perioperative death. Patients with asymptomatic AAAs, therefore, would benefit from an individual risk assessment to help with decisions regarding operative intervention. The purpose of this study was to describe such a 30-day postoperative (POD) risk prediction model using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) data.
  
  
  METHODS
  The NSQIP database (2005-2011) was queried for patients undergoing elective AAA repair using open or endovascular techniques. Clinical variables and known predictors of mortality were included in a full prediction model. These variables included procedure type, patient's age, functional dependence and comorbidities, and surgeon's specialty. Backward elimination with alpha-level of 0.2 was used to construct a parsimonious model. Model discrimination was evaluated in equally sized risk quintiles.
  
  
  RESULTS
  The overall mortality rate for 18,917 elective AAA patients was 1.7%. In this model, surgeon's specialty was not predictive of POD. The most significant factors affecting POD included open repair (odds ratio [OR], 2.712; 95% confidence interval [CI], 2.119-3.469; P < .001), age >70 (OR, 2.243; 95% CI, 1.695-3.033; P < .001), functional dependency (OR, 2.290; 95% CI, 1.442-3.637; P < .001), creatinine above 2.0 mg/dL (OR, 2.1; 95% CI, 1.403-3.142; P < .001) and low hematocrit levels (OR, 2.157; 95% CI, 1.365-3.408; P = .001).The discriminating ability of the NSQIP model was reasonable (C-statistic = 0.751) and corrected to 0.736 after internal validation. The NSQIP model performed well predicting mortality among risk-group quintiles.
  
  
  CONCLUSIONS
  The NSQIP risk prediction model is a robust vehicle to predict POD among patient undergoing elective AAA repair. This model can be used for risk stratification of patients undergoing elective AAA repair.
authors.affiliation: Division of Vascular Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pa. Electronic address: eslamimh@upmc.edu.
authors.forename: Mohammad H
authors.initials: MH
authors.lastname: Eslami
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, Mass.
authors.forename: Denis V
authors.initials: DV
authors.lastname: Rybin
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, Mass.
authors.forename: Gheorghe
authors.initials: G
authors.lastname: Doros
authors.affiliation: Division of Vascular and Endovascular Surgery, Department of Surgery, Boston University School of Medicine, Boston, Mass.
authors.forename: Alik
authors.initials: A
authors.lastname: Farber
mesh_terms: D000367:Age Factors; D000368:Aged; D017544:Aortic Aneurysm, Abdominal / Q000000981:diagnostic imaging / Q000401:mortality / Q000601:surgery*; D019540:Area Under Curve; D058070:Asymptomatic Diseases; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D003404:Creatinine / Q000097:blood; D016208:Databases, Factual; D003661:Decision Support Techniques; D017558:Elective Surgical Procedures; D057510:Endovascular Procedures / Q000009:adverse effects / Q000401:mortality*; D005260:Female; D006304:Health Status; D006400:Hematocrit; D006454:Hemoglobins / Q000032:analysis; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D016017:Odds Ratio; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States; D014656:Vascular Surgical Procedures / Q000009:adverse effects / Q000401:mortality*
chemical_list: D015415:Biomarkers; D006454:Hemoglobins; D003404:Creatinine