Postoperative sepsis prediction in patients undergoing major cancer surgery.
Sood A, Abdollah F, Sammon JD, Arora N, Weeks M, Peabody JO, Menon M, and Trinh QD. Postoperative sepsis prediction in patients undergoing major cancer surgery. J Surg Res 2017; 209:60-69.
The Journal of surgical research
BACKGROUND: Cancer patients are at increased risk for postoperative sepsis. However, studies addressing the issue are lacking. We sought to identify preoperative and intraoperative predictors of 30-d sepsis after major cancer surgery (MCS) and derive a postoperative sepsis risk stratification tool.
METHODS: Patients undergoing one of nine MCSs (gastrointestinal, urological, gynecologic, or pulmonary) were identified within the American College of Surgeons National Surgical Quality Improvement Program (2005-2011, n = 69,169). Multivariable adjusted analyses (MVA) were performed to identify the predictors of postoperative sepsis. A composite sepsis risk score (CSRS) was constructed using the regression coefficients of predictors significant on MVA. The score was stratified into low, intermediate, and high risk, and its predictive accuracy for sepsis, septic shock, and mortality was assessed using the area under the curve analysis.
RESULTS: Overall, 4.3% (n = 2954) of patients developed postoperative sepsis. In MVA, Black race (odds ratio [OR] = 1.30, P = 0.002), preoperative hematocrit <30 >(OR = 1.40, P = 0.022), cardiopulmonary and cerebrovascular comorbidities (P < 0.010), American Society of Anesthesiologists score >3 (P < 0.05), operative time (OR = 1.002, P < 0.001), surgical approach (OR = 1.81, P < 0.001), and procedure type (P < 0.001) were significant predictors of postoperative sepsis. CSRS demonstrated favorable accuracy in predicting postoperative sepsis, septic shock, and mortality (area under the curve 0.72, 0.75, and 0.74, respectively). Furthermore, CSRS risk stratification demonstrated high concordance with sepsis rates, 1.3% in low-risk patients versus 9.7% in high-risk patients. Similarly, 30-d mortality rate varied from 0.5% to 5.5% (10-fold difference) in low-risk patients versus high-risk patients.
CONCLUSIONS: Our study identifies the major risk factors for 30-d sepsis after MCS. These risk factors have been converted into a simple, accurate bedside sepsis risk score. This tool might facilitate improved patient-physician interaction regarding the risk of postoperative sepsis and septic shock.
Medical Subject Headings
Aged; Female; Humans; Male; Middle Aged; Neoplasms; Postoperative Complications; Quality Improvement; Risk Assessment; Sepsis; United States