Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery...

Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving outcomes. This study developed a machine learning model to predict these factors and optimize anesthesia management in obstetric surgery.

This prospective study included 763 obstetric patients who underwent elective cesarean delivery between December 2023 and May 2024. A machine-learning model for postoperative analgesia and adverse reactions (MLPIAAR) was constructed using 42 variables categorized into preoperative (18), intraoperative (12), and postoperative (12) factors. Ten algorithms were applied for model development. Model performance was optimized through 10-fold cross-validation and GridSearchCV. Evaluation metrics included receiver operating characteristic, area under the curve, accuracy, and precision. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). Regression analysis explored PCA compression frequency, consumption, and satisfaction of PCA factors. Validation was performed using predicted values and scatter plots.

Our study found 24.25% of patients experienced inadequate postoperative analgesia within 24 h. Postoperative nausea also occurred in 23.20% of patients. Simultaneously using dexamethasone and flurbiprofen axetil reduced both risks. However, pregnancy-induced hypertension and intraoperative shivering increased nausea risk. Postoperative vomiting occurred in 11.80% of patients, primarily associated with intravenous PCA type. Hydromorphone PCA showed a higher vomiting incidence than nalbuphine PCA. Intraoperative nausea and vomiting increased postoperative vomiting risk. Patients receiving diclofenac sodium and those with longer surgeries exhibited higher PCA compression frequencies and consumption. Higher preoperative Edinburgh Postnatal Depression Scale scores correlated with increased PCA consumption.

The MLPIAAR model predicted high-risk factors for inadequate analgesia, nausea, and vomiting in obstetric patients. Dexamethasone and flurbiprofen axetil reduced these risks, while hydromorphone PCA increased vomiting risk compared to nalbuphine PCA. Machine learning and SHAP are valuable for optimizing anesthesia and analgesia management in obstetrics.