Analysis of influencing factors and clinical application of a predictive model for emergence agitation from general anesthesia after abdominal surgery.

OBJECTIVES

To identify factors influencing emergence agitation (EA) in abdominal surgery patients and develop a predictive model for early clinical intervention.

METHODS

We retrospectively analyzed data from 794 patients who underwent abdominal surgery between June 2022 and June 2024. Independent risk factors for EA were identified using multivariate logistic regression, which informed the construction of a nomogram model. The dataset was split into a training set (67%) and a validation set (33%), with an additional 119 patients serving as an external validation set. Data analysis was performed using SPSS 26.0 and R 4.3.3, and model performance was assessed using Receiver Operating Characteristic (ROC) and calibration curves.

RESULTS

Multivariate analysis revealed nine independent risk factors for EA: age, ASA classification, type of surgery, duration of surgery, intraoperative fluid volume, use of analgesic pumps, catheter usage, postoperative pain, and smoking history. The model's area under the curve (AUC) was 0.787 in the training set, 0.623 in the validation set, and 0.666 in the external validation set, indicating good predictive performance. Calibration curves demonstrated a strong agreement between predicted and observed outcomes, confirming the model's accuracy and consistency.

CONCLUSION

The developed nomogram integrates multiple risk factors to predict EA risk in abdominal surgery patients. It demonstrates high stability and applicability across different datasets, facilitating early identification of high-risk patients and supporting individualized postoperative management.