Background and Objectives:Sigmoid resection still bears a considerable risk of complications. The primary aim was to evaluate and incorporate influencing factors of adverse perioperative outcomes following sigmoid resection into a nomogram-based prediction model.Materials and Methods:Patients from a prospectively maintained database (2004–2022) who underwent either elective or emergency sigmoidectomy for diverticular disease were enrolled. A multivariate logistic regression model was constructed to identify patient-specific, disease-related, or surgical factors and preoperative laboratory results that may predict postoperative outcome.Results:Overall morbidity and mortality rates were 41.3% and 3.55%, respectively, in 282 included patients. Logistic regression analysis revealed preoperative hemoglobin levels (p= 0.042), ASA classification (p= 0.040), type of surgical access (p= 0.014), and operative time (p= 0.049) as significant predictors of an eventful postoperative course and enabled the establishment of a dynamic nomogram. Postoperative length of hospital stay was influenced by low preoperative hemoglobin (p= 0.018), ASA class 4 (p= 0.002), immunosuppression (p= 0.010), emergency intervention (p= 0.024), and operative time (p= 0.010).Conclusions:A nomogram-based scoring tool will help stratify risk and reduce preventable complications.