RESUMO
OBJECTIVE: Free flap reconstruction for head and neck defects is currently a common procedure. This study aimed to create and validate a predictive model for identifying patients at risk of delayed recovery from anesthesia after free flap reconstruction for head and neck defect. METHODS: Electronic medical records from all patients were retrospectively collected. The primary outcome variable was delayed recovery from anesthesia. The least absolute shrinkage and selection operator regression model was employed to identify the most relevant features, followed by the construction of a nomogram model using multivariable logistic regression analysis. The discriminatory power, calibration, and clinical utility of the nomogram model were assessed using receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis, respectively. RESULTS: This novel nomogram model incorporated 4 predictors for delayed recovery from anesthesia: preoperative albumin, intraoperative fresh frozen plasma infusion, preoperative platelet-to-lymphocyte ratio, and duration of intraoperative hypotension. The area under the receiver operating characteristic curve (area under the curve) for the nomogram model was determined to be 0.821 (95% CI: 0.803-0.836). After internal validation, the corrected area under the curve was found to be 0.768 (95% CI: 0.639-0.812). In addition, the model exhibited well-fitted calibration curves and demonstrated favorable clinical usability as indicated by the calibration curve and decision curve analysis curve. CONCLUSION: The authors created and validated a novel predictive model utilizing a limited number of 4 predictors, yet exhibiting commendable predictive performance. This innovative tool holds the potential to mitigate delayed recovery from anesthesia and enhance the efficient allocation of medical resources.
