Objective To develop and validate a practical predictive tool to reliably forecast olfactory changes in patients with pituitary adenomas three months after surgery of endoscopic transnasal transsphenoidal pituitary tumor resection.Methods The data of 348 patients diagnosed with pituitary adenomas and underwent endoscopic transnasal tumor resection at the Affiliated Hospital of Xuzhou Medical University from January 2018 to January 2023 were analyzed.The data included demographic information,clinical details,imaging indicators and laboratory results.Patients were randomly divided into training and testing sets in a 7∶3 ratio.Clinical characteristics with a P<0.05 in univariate analysis were further analyzed by using multivariate analysis.A nomogram was established based on significant variables from the multivariate analysis,and model performance was assessed using receiver operating characteristic(ROC)curve and Calibration plot curve.Finally,clinical influence curves were plotted to visually demonstrate the model's clinical predictive benefits.Results Tumor recurrence,nasal septal mucosal flap elevation method,tumor size,and postoperative nasal irrigation were identified as independent risk factors affecting postoperative olfactory status in pituitary adenoma patients.The nomogram model based on these factors demonstrated good predictive ability and clinical utility.Conclusions This study successfully established a nomogram model that provides an effective and reliable predictive tool for postoperative olfactory changes in patients with pituitary adenomas.The model aids clinicians in preoperatively assessing the risk of olfactory dysfunction,guiding intraoperative strategies and reducing postoperative complications.Furthermore,this research provides new insights for future clinical studies,with the potential to enhance the postoperative quality of life for patients with pituitary adenomas.
pituitary adenomaprognostic modelnomogramlogistic regressiontransnasal nerve endoscopic resection of the pituitary tumor
国家科技期刊平台
NSTL
万方数据
