Mortality and Major Complications after Emergency Laparotomy: A Pilot Study of Risk Prediction Model Development by Preoperative Blood-Based Immune Parameters.

Background Emergency laparotomy is associated with high risk of postoperative complications and mortality. Preoperative identification of patients at high risk of adverse outcome is important. The immune response to conditions requiring emergency laparotomy is not understood in detail. The present study describes preoperative blood-based immune profiles and their potential value in surgical risk assessment. Method Patients (N = 100) referred for emergency laparotomy at Hvidovre Hospital were consecutively included from 3 June 2013-11 April 2014. All patients had blood samples collected before surgery and the immune parameters c-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-10 (IL-10), interferon-gamma induced protein 10 kDa (IP-10), tumor necrosis factor alpha (TNF-alpha) and soluble urokinase plasminogen receptor activator (suPAR) were determined. Patients were stratified according to major postoperative complications (including death), 30- and 180-day mortality. Using logistic regression models and receiver operating characteristics curves the predictive ability of the immune parameters were estimated. Results Major complications were recorded in 45 (45.0%) of the patients, whereas 30-day and 180-day mortalities were 17 (17.0%) and 25 (25.0%), respectively. Concentrations of suPAR and TNF-alpha were associated with major complications while CRP, IL-6, suPAR and TNF-alpha were associated with mortality. Adding the combined immune parameters to a regression model including age, sex, American Society of Anesthesiologists physical status and Eastern Cooperative Oncology Group Performance Status significantly improved the predictive ability for major complications, 30-day mortality and 180-day mortality. Conclusion In emergency laparotomy, preoperative blood-based immune parameters added predictive power to regression models and could be considered in risk prediction model development.