Development and Validation of a Scoring System to Predict 30‐Day Mortality in Patients Undergoing Emergency Laparotomy

ABSTRACT Background Emergency laparotomy is associated with high postoperative morbidity and mortality. Accurate early risk stratification is essential for guiding clinical decision‐making and resource allocation. This study aimed to develop and internally validate a robust predictive scoring system for 30‐day mortality using routinely available preoperative variables. Methods A retrospective cohort study was conducted among 402 adult patients who underwent emergency laparotomy. The dataset was randomly split into training (70%) and testing (30%) subsets. An elastic net logistic regression model ( α = 0.5) was developed on the training set with 10‐fold cross‐validation to optimize model performance and select predictors. Model discrimination was assessed using cross‐validated area under the receiver operating characteristic curve (AUROC) and bootstrap‐based ROC analysis. Calibration was evaluated using the calibration belt method. Results Twelve preoperative predictors were retained in the final model, including ASA grade, cardiovascular disease, serum creatinine, preoperative sepsis, and follow‐up surgery. The cross‐validated AUROC was 0.7922 (95% CI: 0.7278–0.8440), and the bootstrap AUROC was 0.7895 (95% CI: 0.7365–0.8394), indicating good discriminative ability. The model demonstrated statistically significant fit (LR χ 2 = 86.57, p < 0.001) with a pseudo R 2 of 0.1991. A nomogram was constructed to facilitate bedside risk prediction. Conclusion The developed scoring system demonstrated good predictive performance in estimating 30‐day mortality following emergency laparotomy. Incorporating routine clinical and laboratory parameters, the tool is readily applicable in resource‐limited settings. External validation is warranted to assess generalizability and potential for integration into surgical risk assessment workflows.