Development of an American College of Surgeons National Surgery Quality Improvement Program: morbidity and mortality risk calculator for colorectal surgery

J Am Coll Surg. 2009 Jun;208(6):1009-16. doi: 10.1016/j.jamcollsurg.2009.01.043. Epub 2009 Apr 17.


Background: Surgical decision-making and informed patient consent both benefit from having accurate information about risk. But currently available risk estimating systems have one or more limitations associated with lack of specificity to operation type, size of sample (reliability), range of outcomes predicted, and appreciation of hospital effects.

Study design: Data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) patients who underwent colorectal surgery in 2006 to 2007 were used to generate logistic prediction models for 30-day morbidity, serious morbidity, and mortality. Results for these three models were then used to construct a universal multivariable model to predict risk for all three outcomes. Model performance was externally validated against 2005 data.

Results: For 2006 to 2007, 28,863 patients were identified who underwent major colorectal operations at 182 hospitals. A single 15-variable predictor model exhibited discrimination (c-statistic) close to that observed for the separate models on all three outcomes. Similar discrimination was found when the 2006 to 2007 universal model was applied to 3,037 operations conducted in 2005 at 37 hospitals.

Conclusions: The ACS NSQIP colorectal risk calculator allows surgeons to preoperatively provide patients with detailed information about their personal risks of overall morbidity, serious morbidity, and mortality. Because ACS NSQIP can also categorize hospitals as performing better or worse than expected (or as expected), surgeons have the opportunity to adjust risk probabilities for patients at their institutions accordingly.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Colectomy / mortality
  • Colectomy / standards*
  • Colectomy / statistics & numerical data*
  • Databases as Topic
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Morbidity
  • Quality Assurance, Health Care*
  • Risk Assessment
  • Societies, Medical
  • Treatment Outcome
  • United States