The Predictive Value of Clinical and Systemic Inflammatory Biomarkers in Emergency Colic Cancer Surgery: A Retrospective Study.

: Emergency surgery for complicated colon cancer carries high morbidity and mortality, largely driven by systemic inflammation and organ dysfunction. This study aims to investigate the predictive value of preoperative inflammatory biomarkers for postoperative outcomes. We retrospectively analyzed 219 patients undergoing emergency surgery for complicated colon cancer. Patients were classified as uncomplicated ( = 164) or complicated (Clavien-Dindo ≥ IIIA; = 55). Preoperative clinical data, comorbidity indices, laboratory values, and inflammatory markers: C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were assessed. Logistic regression and ROC (Receiver Operating Characteristic) curves analyses identified predictors of Clavien Dindo complications graded as IIIA or higher, anastomotic leak (AL), and in-hospital mortality. Most patients included in the study were males (75.02%), with a mean age of 69.63 (±11.54) years. Patients included in the complicated group had higher comorbidity burden, ASA (American Society of Anesthesiologists) grade, rates of diabetes, organ failure, and systemic inflammatory response. All inflammatory biomarkers were significantly elevated in the complicated group ( < 0.001). CRP (>62.8 mg/dL), NLR (>6.89), and PLR (>334.2) showed good discrimination for Clavien Dindo complications graded as IIIA or higher, with AUC (area under curve) ranging from 0.726 to 0.799. A multivariable model including Charlson Comorbidity Index (CCI), CRP, PLR, and diabetes predicted Clavien-Dindo ≥ IIIA complications with excellent accuracy (AUC 0.870). PLR, creatinine, and diabetes independently predicted AL (AUC 0.834). Mortality (20.5%) was strongly associated with peritonitis, CRP, and NLR (AUC 0.891). Preoperative inflammatory biomarkers, combined with comorbidity and renal function, reliably predict adverse outcomes after emergency colon cancer surgery. Multivariate models may be useful for early risk stratification and support individualized perioperative management.