Establishment of Prediction Model for Postoperative Sore Throat

This observational study aims to identify the risk factors associated with postoperative sore throat (POST) and to develop a clinical prediction model for patients undergoing thyroid surgery under general anesthesia. Despite improvements in surgical and anesthetic techniques, POST remains one of the most common complications following thyroidectomy, with an incidence of up to 80%. POST can significantly impact patient comfort, swallowing function, nutritional intake, and recovery quality, leading to prolonged hospitalization and increased healthcare costs.

The etiology of POST is multifactorial, involving intubation-related factors (e.g., endotracheal tube size, cuff pressure), surgery-related factors (e.g., duration of surgery, neck hyperextension), and patient-related factors (e.g., age, sex, smoking history, pre-existing throat symptoms). However, large-scale, systematic studies in the Chinese population remain limited.

With the increasing adoption of Enhanced Recovery After Surgery (ERAS) principles, the accurate identification of modifiable risk factors and early prediction of POST risk are essential for targeted preventive strategies and improved perioperative management. Recent advancements in machine learning and statistical modeling allow for the development of personalized, multifactorial risk assessment tools.

In this study, clinical data from patients undergoing thyroid surgery at Nanjing First Hospital will be collected and analyzed. The primary goal is to identify key preoperative and intraoperative predictors of POST and to establish a multivariable nomogram-based prediction model. Internal validation using bootstrap methods will be performed to evaluate the model's discrimination, calibration, and clinical utility, ultimately supporting evidence-based interventions to improve postoperative outcomes and patient satisfaction.