A New Nomogram-Based Prediction Model for Postoperative Outcome after Sigmoid Resection for Diverticular Disease.

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. 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. Overall morbidity and mortality rates were 41.3% and 3.55%, respectively, in 282 included patients. Logistic regression analysis revealed preoperative hemoglobin levels ( = 0.042), ASA classification ( = 0.040), type of surgical access ( = 0.014), and operative time ( = 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 ( = 0.018), ASA class 4 ( = 0.002), immunosuppression ( = 0.010), emergency intervention ( = 0.024), and operative time ( = 0.010). A nomogram-based scoring tool will help stratify risk and reduce preventable complications.