Objective: The objective of this study was to create a clinical prediction tool to differentiate women at risk for postoperative complications after benign gynecologic surgery.
Methods: We utilized the 2005 to 2009 American College of Surgeons National Surgical Quality Improvement Program participant use data files to perform a secondary data-set analysis of women older than 16 years who underwent benign gynecologic procedures. We then temporally divided women into 2 similar cohorts. Our derivation cohort included all women undergoing benign gynecologic procedures in 2005 to 2008. Our validation cohort included all women undergoing benign gynecologic procedures in 2009. The primary outcome, composite 30-day major postoperative complications, was analyzed as a dichotomous variable. A prediction tool was then constructed to predict the occurrence of postoperative complications built from the logistic regression model by rounding the value of each estimated β coefficient to the nearest integer. An individual’s risk score was then computed by summing the number of points based on her preoperative characteristics. This risk score was then used to categorize women into low-, medium-, and high-risk groups.
Results: A prediction tool for benign gynecologic procedures identified women at low (2.7% and 2.4%), medium (6.3% and 6.8%), and high (29.5% and 23.8%) risk of complications in the derivation and validation cohorts, respectively.
Conclusions: A prediction tool can differentiate women at risk for postoperative complications after benign gynecologic surgery.