Huang ZH, Tu XZ, Lin Q, Tu M, Lin GC, Zhang KP. Nomogram for predicting short-term response to anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration: An observational study. World J Radiol 2024; 16(9): 418-428 [PMID: 39355396 DOI: 10.4329/wjr.v16.i9.418]
Corresponding Author of This Article
Zhen-Huan Huang, MD, Associate Chief Physician, Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105 North 91 Road, Xinluo District, Longyan 364000, Fujian Province, China. tuxuezhao@163.com
Research Domain of This Article
Ophthalmology
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Radiol. Sep 28, 2024; 16(9): 418-428 Published online Sep 28, 2024. doi: 10.4329/wjr.v16.i9.418
Nomogram for predicting short-term response to anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration: An observational study
Zhen-Huan Huang, Qi Lin, Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian Province, China
Xue-Zhao Tu, Department of Orthopedics, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian Province, China
Mei Tu, Department of Endocrinology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian Province, China
Guo-Cai Lin, Kai-Ping Zhang, Department of Ophthalmology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian Province, China
Author contributions: Huang ZH and Lin GC conceived and designed the study; Huang ZH, Zhang KP, and Tu XZ collected and assembled the data; Huang ZH, Tu XZ, Lin Q, and Tu M analyzed and interpreted the data. All authors have read and approved the final manuscript.
Supported byFujian Province Natural Science Foundation of China, No. 2020J011321.
Institutional review board statement: The study was reviewed and approved by the Longyan First Affiliated Hospital of Fujian Medical University (approval No. 202014).
Informed consent statement: As this retrospective observational study used anonymous clinical data from patients who have already consented to treatment, no additional consent was needed. Patients were informed of potential research use at the time of treatment consent.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhen-Huan Huang, MD, Associate Chief Physician, Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No. 105 North 91 Road, Xinluo District, Longyan 364000, Fujian Province, China. tuxuezhao@163.com
Received: May 7, 2024 Revised: August 12, 2024 Accepted: August 14, 2024 Published online: September 28, 2024 Processing time: 142 Days and 12.5 Hours
Abstract
BACKGROUND
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.
AIM
To identify the risk factors affecting anti-VEGF treatment efficacy in nAMD and develop a predictive model for short-term response.
METHODS
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.
RESULTS
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.
CONCLUSION
The predictive model effectively forecasts 1-mo therapeutic outcomes for nAMD patients undergoing anti-VEGF therapy, enhancing personalized treatment planning.
Core Tip: This study developed a predictive model using optical coherence tomography angiography to identify factors affecting the effectiveness of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration (nAMD). The number of junction voxels emerged as an independent predictor of positive treatment outcomes. Integrating several parameters, the predictive model demonstrated strong performance in forecasting 1-mo therapeutic outcomes, providing a valuable tool for personalized treatment planning in nAMD patients.