Predicting postoperative recurrence of auricular pseudocyst: key factors and risk models.

Objective: To investigate the factors influencing postoperative recurrence of auricular pseudocysts and to develop recurrence risk prediction models using logistic regression and Cox regression analyses.

Methods: This retrospective study analyzed clinical data from 215 patients who underwent surgical treatment for auricular pseudocysts between January 2015 and December 2022. Univariate analysis identified factors associated with recurrence, which were further assessed using multivariate logistic regression and Cox regression. Recurrence prediction models were constructed, and their predictive performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.

Results: Univariate analysis identified age, cyst size, surgical approach, and postoperative adjuvant therapy as significant factors associated with postoperative recurrence (P<0.05). Multivariate logistic regression and Cox regression identified age <53.5 years, cyst size <2.5 cm, fenestration surgery, and absence of postoperative adjuvant therapy as protective factors against recurrence (P<0.05). The constructed models showed stable AUC values for 90-day and 120-day predictions (AUC = 0.718). No significant difference in predictive performance was observed between logistic regression and Cox regression models for 6-month recurrence risk (P = 0.934).

Conclusion: Age, cyst size, surgical approach, and postoperative adjuvant therapy are critical factors influencing postoperative recurrence of auricular pseudocysts. The recurrence prediction models based on logistic regression and Cox regression demonstrate high efficiency in predicting short-term recurrence and can guide postoperative management strategies.