BACKGROUND
Laparoscopic hernia repair is a minimally invasive surgery, but patients may experience emergence agitation (EA) during the post-anesthesia recovery period, which can increase pain and lead to complications such as wound reopening and bleeding. There is limited research on the risk factors for this agitation, and few effective tools exist to predict it. Therefore, by integrating clinical data, we have developed nomograms and random forest predictive models to help clinicians predict and potentially prevent EA.
AIM
To establish a risk nomogram prediction model for EA in patients undergoing laparoscopic hernia surgery under total inhalation combined with sacral block anesthesia.
METHODS
Based on the clinical information of 300 patients who underwent laparoscopic hernia surgery in the Nanning Tenth People’s Hospital, Guangxi, from January 2020 to June 2023, the patients were divided into two groups according to their sedation-agitation scale score, i.e. , the EA group (≥ 5 points) and the non-EA group (≤ 4 points), during anesthesia recovery. Least absolute shrinkage and selection operator regression was used to select the key features that predict EA, and incorporating them into logistic regression analysis to obtain potential predictive factors and establish EA nomogram and random forest risk prediction models through R software.
RESULTS
Out of the 300 patients, 72 had agitation during anesthesia recovery, with an incidence of 24.0%. American Society of Anesthesiologists classification, preoperative anxiety, solid food fasting time, clear liquid fasting time, indwelling catheter, and pain level upon awakening are key predictors of EA in patients undergoing laparoscopic hernia surgery with total intravenous anesthesia and caudal block anesthesia. The nomogram predicts EA with an area under the receiver operating characteristic curve (AUC) of 0.947, a sensitivity of 0.917, and a specificity of 0.877, whereas the random forest model has an AUC of 0.923, a sensitivity of 0.912, and a specificity of 0.877. Delong’s test shows no significant difference in AUC between the two models. Clinical decision curve analysis indicates that both models have good net benefits in predicting EA, with the nomogram effective within the threshold of 0.02 to 0.96 and the random forest model within 0.03 to 0.90. In the external model validation of 50 cases of laparoscopic hernia surgery, both models predicted EA. The nomogram model had a sensitivity of 83.33%, specificity of 86.84%, and accuracy of 86.00%, while the random forest model had a sensitivity of 75.00%, specificity of 78.95%, and accuracy of 78.00%, suggesting that the nomogram model performs better in predicting EA.
CONCLUSION
Independent predictors of EA in patients undergoing laparoscopic hernia repair with total intravenous anesthesia combined with caudal block include American Society of Anesthesiologists classification, preoperative anxiety, duration of solid food fasting, duration of clear liquid fasting, presence of an indwelling catheter, and pain level upon waking. The nomogram and random forest models based on these factors can help tailor clinical decisions in the future.
