Abstract / Summary
Intraocular pressure (IOP) spikes are a common postoperative complication following gonioscopy-assisted transluminal trabeculotomy (GATT). Previous studies have identified postoperative IOP spikes as a significant risk factor contributing to surgical failure. This study aims to identify the risk factors associated with IOP spikes and to develop a prediction model for early identification of high-risk patients. We retrospectively analyzed 149 patients with primary open-angle glaucoma (POAG) who underwent GATT at Chengdu First People’s Hospital between September 2019 and May 2025. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) regression were employed to identify relevant predictive variables. Based on these variables, a multivariate logistic regression model was constructed and presented as a nomogram. The model’s performance was evaluated using receiver operating characteristic (ROC) curve analysis, the Hosmer–Lemeshow goodness-of-fit test, Spiegelhalter’s Z-test, calibration curve analysis, and decision curve analysis (DCA). Internal validation was performed using bootstrap resampling with 500 iterations. Among the 149 patients, 52 (34.9%) experienced an IOP spikes. This study identified age, preoperative IOP, body mass index (BMI), activated partial thromboplastin time (APTT), and total protein (TP) as predictive variables for IOP spikes after GATT, and a nomogram was constructed based on these variables. The area under the ROC curve (AUC) was 0.898 (95% CI = 0.849–0.947), demonstrating good performance. Calibration curve, the Hosmer-Lemeshow test, Spiegelhalter’s Z-test and DCA confirmed the model’s excellent clinical utility. The developed predictive model demonstrated good performance in identifying high-risk individuals for IOP spikes after GATT. Pending external validation, it may assist clinicians in enhancing IOP monitoring in these patients, making more accurate clinical decisions, and providing better preoperative education.