Nomogram to Predict the Risk of External Auditory Canal Stenosis After Endoscopic Surgery: A Retrospective Study.

Journal: Ear, Nose, & Throat Journal
Published:
Abstract

Objective: To identify risk factors and develop a predictive model for the onset of external auditory canal stenosis (EACS) after endoscopic surgery.

Methods: A retrospective analysis was conducted in 362 patients who underwent endoscopic surgery from January 2021 to September 2023. The patients were categorized into a training set (n = 217) and a test set (n = 145). A single-factor regression analysis was used to identify significant differences between the EACS and non-EACS groups within the training set. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression were employed to screen and develop predictive models, visualized in a nomogram. The predictive accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC).

Results: Univariate regression analysis of the training set revealed that the history of EACS, the history of ear surgery, the operative time, the levels of triglycerides (TG), the systemic immune-inflammation ratio (SIRI), and the albumin-to-creatinine score (AISI) were significant factors between the 2 groups (P < .05). Subsequently, these variables were included in the LASSO regression analysis, which identified 4 high-risk factors: history of ear surgery, operative time, TG levels, and SIRI. The model exhibited strong predictive capacity, with an area under the ROC curve of 0.89 (95% CI 0.82-0.95) in the training set and 0.88 (95% CI 0.72-1.00) in the validation set. Calibration curves, DCA, and CIC analyses further demonstrated the model's excellent predictive value and clinical utility.

Conclusions: The developed nomogram is a significant tool for predicting postoperative EACS in patients undergoing endoscopic surgery. It offers a valuable reference for the early identification of high-risk patients, facilitating timely clinical intervention and promoting personalized and precise treatment strategies.

Authors
Zhongxuan Yao, Shao Yudi, Peng Yaxin, He Jiadi, Wei Li