title: The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery.
abstract: OBJECTIVE
  To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the "point-of-care," and that can be used by surgeons and hospitals to internally audit their quality of care.
  
  
  BACKGROUND
  Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery.
  
  
  METHODS
  Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database.
  
  
  RESULTS
  The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set.
  
  
  CONCLUSIONS
  Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk-reduction strategies, and guiding quality improvement efforts.
authors.affiliation: Department of Anesthesiology, University of Rochester School of Medicine, Rochester, NY 14642, USA. Laurent_Glance@urmc.rochester.edu
authors.forename: Laurent G
authors.initials: LG
authors.lastname: Glance
authors.forename: Stewart J
authors.initials: SJ
authors.lastname: Lustik
authors.forename: Edward L
authors.initials: EL
authors.lastname: Hannan
authors.forename: Turner M
authors.initials: TM
authors.lastname: Osler
authors.forename: Dana B
authors.initials: DB
authors.lastname: Mukamel
authors.forename: Feng
authors.initials: F
authors.lastname: Qian
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Dick
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008485:Medical Audit; D008875:Middle Aged; D058996:Quality Improvement; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D012307:Risk Factors; D013514:Surgical Procedures, Operative / Q000401:mortality* / Q000592:standards