• Joshua S Richman, Patrick W Hosokawa, Sung-Joon Min, Majed G Tomeh, Leigh Neumayer, Darrell A Campbell, William G Henderson, and Mary T Hawn.
• C-SMART, Birmingham Veterans Affairs Medical Center, Birmingham, Alabama 35222, USA. jrichman@uab.edu
• Am Surg. 2012 Jul 1; 78 (7): 755-60.

AbstractThe purpose of this study was to explore the feasibility of prospectively identifying patients at high risk for surgical complications using automatable methods focused on patient characteristics. We used data from the Michigan Surgical Quality Collaborative (60,411 elective surgeries) performed between 2003 and 2008. Regression models for postoperative mortality, overall morbidity, cardiac, thromboembolic, pulmonary, renal, and surgical site infection complications were developed using preoperative patient and planned procedure data. Risk was categorized by quartiles of predicted probability: "low" risk corresponding to the bottom quartile, "average" to the middle two quartiles, and "high" to the top quartile. C-indices were calculated to measure discrimination; model validity was assessed by cross-validation. Models were repeated using only patient characteristics. Risk category was closely related to event rates; 80 to 90 per cent of mortality and cardiac, renal, and pulmonary complications occurred among the 25 per cent of "high-risk" patients. Although thromboembolisms and surgical site infections were less predictable, 60 to 70 per cent of events occurred among high-risk patients. Cross-validation results were consistent and only slightly attenuated when predictors were restricted to patient characteristics alone. Adverse postoperative events are concentrated among patients identifiable preoperatively as high risk. Preoperative risk assessment could allow for efficient interventions targeted to high-risk patients.

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