An Efficient Risk Adjustment Model to Predict Inpatient Adverse Events after Surgery

Background Risk adjustment is an important component of surgical outcomes and quality analyses. Current models include numerous preoperative variables; however, the relative contribution of these variables may be limited. This research seeks to identify a model with the fewest number of variables necessary to perform an adequate risk adjustment to predict any inpatient adverse event for use in resource‐limited settings. Methods All patients from the National Surgical Quality Improvement Program (NSQIP) database from 2005 to 2010 were included. Outcomes were inpatient mortality or any surgical complication captured by NSQIP. Models were built by sequential addition of preoperative risk variables selected by their area under the receiver operator characteristic curve (AUC). Results Among 863,349 patients, the single variable with the highest AUC was American Society of Anesthesiologists (ASA) classification (AUC = 0.7127). AUC values reached 0.7923 with five variables (ASA classification, wound classification, functional status prior to surgery, albumin, and age) and 0.7945 with six variables. The sixth variable was one of the following: alkaline phosphatase, weight loss, principal anesthesia technique, gender, or emergency status. The model with the highest discrimination that did not require laboratories included ASA classification, functional status prior to surgery, wound classification, and age (AUC = 0.7810). Including all 66 preoperative variables produced little additional gain (AUC = 0.8006). Conclusions Six variables are sufficient to develop a risk adjustment tool for inpatient surgical mortality and morbidity. This research has important implications for the field of surgical outcomes research by improving efficiency of data collection. This limited model can aid the expansion of risk‐adjusted analyses to resource‐limited settings worldwide.