Seasonal variation in surgical outcomes as measured by the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP)

Ann Surg. 2007 Sep;246(3):456-62; discussion 463-5. doi: 10.1097/SLA.0b013e31814855f2.


Objective: We hypothesize that the systems of care within academic medical centers are sufficiently disrupted with the beginning of a new academic year to affect patient outcomes.

Methods: This observational multiinstitutional cohort study was conducted by analysis of the National Surgical Quality Improvement Program-Patient Safety in Surgery Study database. The 30-day morbidity and mortality rates were compared between 2 periods of care: (early group: July 1 to August 30) and late group (April 15 to June 15). Patient baseline characteristics were first compared between the early and late periods. A prediction model was then constructed, via stepwise logistic regression model with a significance level for entry and a significance level for selection of 0.05.

Results: There was 18% higher risk of postoperative morbidity in the early (n = 9941) versus the late group (n = 10313) (OR 1.18, 95%, CI 1.07-1.29, P = 0.0005, c-index 0.794). There was a 41% higher risk for mortality in the early group compared with the late group (OR 1.41, CI 1.11-1.80, P = 0.005, c-index 0.938). No significant trends in patient risk over time were noted.

Conclusion: Our data suggests higher rates of postsurgical morbidity and mortality related to the time of the year. Further study is needed to fully describe the etiologies of the seasonal variation in outcomes.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chi-Square Distribution
  • Efficiency, Organizational
  • Female
  • General Surgery / standards*
  • Humans
  • Male
  • Middle Aged
  • Operating Rooms / organization & administration
  • Outcome Assessment, Health Care*
  • Postoperative Complications / epidemiology*
  • Postoperative Complications / mortality
  • Quality Assurance, Health Care*
  • Regression Analysis
  • Seasons*
  • Societies, Medical
  • United States / epidemiology