title: Predicting risk of death in general surgery patients on the basis of preoperative variables using American College of Surgeons National Surgical Quality Improvement Program data.
abstract: OBJECTIVE
  To use the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database to develop an accurate and clinically meaningful preoperative mortality predictor (PMP) for general surgery on the basis of objective information easily obtainable at the patient's bedside and to compare it with the preexisting NSQIP mortality predictor (NMP).
  
  
  METHODS
  Data were obtained from the ACS NSQIP Participant Use Data File (2005 to 2008) for current procedural terminology codes that included open pancreas surgery and open/laparoscopic colorectal, hernia (ventral, umbilical, or inguinal), and gallbladder surgery. Chi-square analysis was conducted to determine which preoperative variables were significantly associated with death. Logistic regression followed by frequency analysis was conducted to assign weight to these variables. PMP score was calculated by adding the scores for contributing variables and was applied to 2009 data for validation. The accuracy of PMP score was tested with correlation, logistic regression, and receiver operating characteristic analysis.
  
  
  RESULTS
  PMP score was based on 16 variables that were statistically reliable in distinguishing between surviving and dead patients (p < 0.05). Statistically significant variables predicting death were inpatient status, sepsis, poor functional status, do-not-resuscitate directive, disseminated cancer, age, comorbidities (cardiac, renal, pulmonary, liver, and coagulopathy), steroid use, and weight loss. The model correctly classified 98.6% of patients as surviving or dead (p < 0.05). Spearman correlation of the NMP and PMP was 86.9%.
  
  
  CONCLUSIONS
  PMP score is an accurate and simple tool for predicting operative survival or death using only preoperative variables that are readily available at the bedside. This can serve as a performance assessment tool between hospitals and individual surgeons.
authors.affiliation: York Hospital, Pennsylvania, USA. drvaid@yahoo.com
authors.forename: Sachin
authors.initials: S
authors.lastname: Vaid
authors.forename: Ted
authors.initials: T
authors.lastname: Bell
authors.forename: Rod
authors.initials: R
authors.lastname: Grim
authors.forename: Vanita
authors.initials: V
authors.lastname: Ahuja
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D013502:General Surgery / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications / Q000401:mortality*; D017410:Practice Guidelines as Topic / Q000592:standards*; D057234:Preoperative Period; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012955:Societies, Medical; D016896:Treatment Outcome; D014481:United States; D055815:Young Adult