To develop and validate a nomogram for predicting the 28-day survival rate of patients undergoing emergency surgery.

Clinical data were collected from 1 956 adult patients who underwent emergency surgery at the Sir Run Run Shaw Hospital, Zhejiang University School of Medicine between January 2020 and June 2024. The cohort was randomly divided into a training set (n = 1 369) and a validation set (n = 587). According to the 28-day postoperative survival status, patients in the training set were classified into a survival group (n = 1 188) and a non-survival group (n = 181). The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection, followed by multivariable logistic regression to establish a prediction model, which was subsequently visualized as a nomogram. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by bootstrap resampling with 1 000 iterations, and decision curve analysis (DCA) was performed to evaluate the clinical utility of the model.

Compared with the survival group, patients in the non-survival group had significantly higher Charlson comorbidity index (CCI), white blood cell count, neutrophil count, lymphocyte count, serum creatinine, blood urea nitrogen (BUN), uric acid, intraoperative blood loss, and incidence of postoperative complications (all P < 0.05). In contrast, the proportion of male patients, diastolic blood pressure, fibrinogen (FIB), C-reactive protein, and serum potassium were significantly lower in the non-survival group than in the survival group (all P < 0.05). Multivariable logistic regression identified seven independent predictors of 28-day postoperative mortality: male, CCI, neutrophil count, lymphocyte count, FIB, BUN, and uric acid (all P < 0.05). A nomogram integrating these seven predictors was constructed to estimate the risk of 28-day postoperative mortality in patients undergoing emergency surgery. Receiver operating characteristic curve analysis demonstrated that the nomogram achieved AUCs of 0.759 [95% confidence interval (CI) (0.721, 0.798), P < 0.001] in the training set and 0.725 [95%CI (0.701, 0.820), P < 0.001] in the validation set, indicating its good discriminatory performance. Calibration curves showed good agreement between the predicted probabilities and the observed outcomes. DCA further demonstrated that the nomogram provided substantial net clinical benefit.

The nomogram developed in this study can effectively predict the 28-day survival rate of patients undergoing emergency surgery.