World Journal of Surgery

, Volume 38, Issue 8, pp 1954–1960 | Cite as

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

  • Jamie E. AndersonEmail author
  • John Rose
  • Abraham Noorbakhsh
  • Mark A. Talamini
  • Samuel R. G. Finlayson
  • Stephen W. Bickler
  • David C. Chang



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.


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).


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).


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.


Ventral Hernia Repair Wound Classification Adequate Risk Adjustment Inpatient Adverse Event Predict Inpatient Mortality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The ACS NSQIP and its participating hospitals are the source of data used in this research; they have not verified, and are not responsible for, the statistical validity of the data analysis or conclusions of the authors.


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Copyright information

© Société Internationale de Chirurgie 2014

Authors and Affiliations

  • Jamie E. Anderson
    • 1
    Email author
  • John Rose
    • 1
  • Abraham Noorbakhsh
    • 1
  • Mark A. Talamini
    • 2
  • Samuel R. G. Finlayson
    • 3
  • Stephen W. Bickler
    • 1
  • David C. Chang
    • 1
  1. 1.Department of SurgeryUniversity of CaliforniaSan DiegoUSA
  2. 2.Department of SurgeryState University of New York at Stony BrookStony BrookUSA
  3. 3.Department of SurgeryUniversity of UtahSalt Lake CityUSA

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