Nomogram for predicting short-term response to anti-vascular endothelial growth factor treatment in neovascular...

Anti-vascular endothelial growth factor (anti-VEGF) therapy is critical for managing neovascular age-related macular degeneration (nAMD), but understanding factors influencing treatment efficacy is essential for optimizing patient outcomes.

To identify the risk factors affecting anti-VEGF treatment efficacy in nAMD and develop a predictive model for short-term response.

In this study, 65 eyes of exudative AMD patients after anti-VEGF treatment for ≥ 1 mo were observed using optical coherence tomography angiography. Patients were classified into non-responders (n = 22) and responders (n = 43). Logistic regression was used to determine independent risk factors for treatment response. A predictive model was created using the Akaike Information Criterion, and its performance was assessed with the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) with 500 bootstrap re-samples.

Multivariable logistic regression analysis identified the number of junction voxels [odds ratio = 0.997, 95% confidence interval (CI): 0.993-0.999, P = 0.010] as an independent predictor of positive anti-VEGF treatment outcomes. The predictive model incorporating the fractal dimension, number of junction voxels, and longest shortest path, achieved an area under the curve of 0.753 (95%CI: 0.622-0.873). Calibration curves confirmed a high agreement between predicted and actual outcomes, and DCA validated the model's clinical utility.

The predictive model effectively forecasts 1-mo therapeutic outcomes for nAMD patients undergoing anti-VEGF therapy, enhancing personalized treatment planning.