Development and validation of a predictive model for emergence agitation after general anesthesia in total knee arthroplasty patients

建立和验证全膝关节置换术患者全身麻醉后苏醒期躁动预测模型

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Abstract

This study aims to identify independent risk factors for postoperative agitation during the postoperative recovery period in patients undergoing total knee arthroplasty (TKA) under general anesthesia and construct and validate a predictive nomogram model. We conducted a retrospective analysis of clinical data from 957 patients who underwent primary unilateral TKA under general anesthesia between January 2021 and December 2024. Independent risk factors were identified using univariate and multivariate logistic regression analysis. A predictive nomogram was constructed based on these variables. Model performance was evaluated using receiver operating characteristic curve area under the curve, calibration curves, and decision curves. Internal validation was performed using bootstrap resampling and 10-fold cross-validation. Among the 957 patients, 100 (10.45%) experienced postoperative agitation during the postoperative recovery period. Five variables were identified as independent risk factors: age > 75 years (OR = 2.507, 95% CI: 1.387-4.694), anesthesia duration > 3 hours (OR = 2.937, 95% CI: 1.224-5.470), intraoperative hypothermia (OR = 1.945, 95% CI: 1.098-4.365), American Society of Anesthesiologists classification > II (OR = 2.864, 95% CI: 1.197-4.766), and first-time general anesthesia (OR = 2.576, 95% CI: 1.378-4.295). The nomogram demonstrated good discriminative ability, with area under the curve values of 0.830 and 0.817 for the training and validation sets, respectively. Calibration curves and decision curves indicated that the model fits well and has clinical applicability. The developed nomogram effectively predicts the risk of postoperative agitation during the recovery period in TKA patients. It can assist clinicians in identifying high-risk individuals preoperatively and implementing targeted interventions to enhance patient safety and optimize postoperative recovery outcomes.

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