Screening Criteria for Multiple Organ Dysfunction after Abdominal Surgery (Clinical Research)

Abstract

Objective: to determine the feasibility of using C-reactive protein (CRP) and cholesterol levels as biochemical screening markers for multiple organ dysfunction syndrome (MODS) in patients after abdominal surgery.Materials and methods. A prospective case-control study was performed in 192 patients who receivedtreatment at the Intensive Care Unit (ICU) after abdominal surgery. Patients were classified into two groups: Group 1 (n=95) of patients without MODS and Group 2 (n=97) of patients with MODS. The signs of MODS were identified based on 2001 SCCM/ACCP consensus conference criteria. During the first three post-operative days, total cholesterol and CRP levels were measured, and severity was assessed using prognostic scoring systems (SOFA and Apache III). Logistic regression analysis was used to evaluate five MODS prediction models based on total cholesterol levels, CRP levels, a combination of cholesterol and CRP levels as well as SOFA and Apache III scores.Results. Cholesterol levels in Group 2 were found to be significantly lower than those in Group 1 (3.13 (2.6–3.74) mmol/l vs 4.09 (3.26–5.01) mmol/l; P0.05). Significantly increased CRP levels in Group 2 compared to Group 1 were found (168.7 (90.2–247.2) mg/l vs 85.9 (35.6–172.6) mg/l; P0.05). AUC, sensitivity, and specificity values were determined for the study models and scales based on total cholesterol levels (AUC 0.679; 95% confidence interval (CI) 0.625–0.732), CRP levels (AUC 0.67; 95% CI 0.6–0.74), a combination of cholesterol and CRP levels (AUC 0.819; 95% CI 0.721–0.917), SOFA score (AUC 0.786; 95% CI 0.744–0.829), and Apache III score (AUC 0.631; 95% CI 0.582–0.68). The optimal threshold was 3.4 mmol/l and 96.5 mg/l for cholesterol and CRP levels, respectively.Conclusion. Total cholesterol and CRP monitoring revealed them as screening biomarkers informative for predicting MODS within the first three days after abdominal surgery. Using all these models, the probability of MODS development in a patient can be calculated as a function of the numerical value of the biomarker.