title: A practical mortality risk score for emergent colectomy.
abstract: BACKGROUND
  Critically ill patients requiring emergent colectomy have significant mortality risk.
  
  
  OBJECTIVE
  A national administrative database was used to compose a simple scoring scheme for predicting in-hospital mortality risk.
  
  
  METHODS
  The 2007 to 2009 Nationwide Inpatient Sample was queried to identify patients requiring nonelective colectomy. Multivariable binary logistic regression analysis was used to identify predictors that increased mortality. Each predictor was given a point value, based on the corresponding logit, the sum of which constituted a risk score. The scoring system was tested by using k-partitions cross-validation.
  
  
  METHODS
  This study is based on database analysis.
  
  
  METHODS
  A total of 338,348 cases were identified. Mean age was 64, and 53% of the patients were women.
  
  
  METHODS
  The primary outcomes measured were mortality and risk score development.
  
  
  RESULTS
  The overall mortality risk was 9%. Regression analysis identified the following risk factors and assigned points: acute renal failure (6), hemodialysis (6), age >65 (4), peripheral vascular disease (4), myocardial infarction (4), chronic obstructive pulmonary disease (2), cardiac arrhythmia (1), and congestive heart failure (1). The maximum score observed was 26 (of a possible 28), which corresponded to 100% mortality. Receiver operator characteristic analysis showed an area under the curve of 0.81.
  
  
  CONCLUSIONS
  This study was limited because of its retrospective nature, and because it used database data with variability in coding among participating institutions.
  
  
  CONCLUSIONS
  With the use of a simple 8-variable scoring system, inpatient mortality estimates can be made for patients requiring emergent colectomy. When used judiciously, it can be used as a tool when counseling patients and family both before and after surgery.
authors.affiliation: Department of Surgery, Steward St. Elizabeth's Medical Center, Boston, Massachusetts 02135, USA. reza.kermani@tufts.edu
authors.forename: Reza
authors.initials: R
authors.lastname: Kermani
authors.forename: Joseph J
authors.initials: JJ
authors.lastname: Coury
authors.forename: Haisar
authors.initials: H
authors.lastname: Dao
authors.forename: Justin H
authors.initials: JH
authors.lastname: Lee
authors.forename: Peter E
authors.initials: PE
authors.lastname: Miller
authors.forename: Darrick
authors.initials: D
authors.lastname: Yee
authors.forename: Charles
authors.initials: C
authors.lastname: Contant
authors.forename: Alan W
authors.initials: AW
authors.lastname: Hackford
mesh_terms: D058186:Acute Kidney Injury / Q000453:epidemiology; D000367:Age Factors; D001145:Arrhythmias, Cardiac / Q000453:epidemiology; D003082:Colectomy / Q000401:mortality*; D016208:Databases, Factual; D004630:Emergencies; D005260:Female; D006333:Heart Failure / Q000453:epidemiology; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000453:epidemiology; D016017:Odds Ratio; D016491:Peripheral Vascular Diseases / Q000453:epidemiology; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology; D012372:ROC Curve; D006435:Renal Dialysis; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States / Q000453:epidemiology