Correlation Analysis between Optical Coherence Tomography Parameters of Neovascular Age-Related Macular Degeneration and Visual Prognosis at 2 Months after Anti-VEGF Therapy

Correlation Analysis between Optical Coherence Tomography Parameters of Neovascular Age-Related Macular Degeneration and Visual Prognosis at 2 Months after Anti-VEGF Therapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation Analysis between Optical Coherence Tomography Parameters of Neovascular Age-Related Macular Degeneration and Visual Prognosis at 2 Months after Anti-VEGF Therapy Wei Li, Junchen Zhang, Changyun He, Qibin Jiao, Yanlin Wu, Zhonghao Ji, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8383468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the correlation between baseline optical coherence tomography (OCT) parameters and 2-month visual prognosis after anti-vascular endothelial growth factor (anti-VEGF) therapy in patients with neovascular age-related macular degeneration (nAMD). Methods A retrospective analysis was performed on 100 nAMD patients treated with intravitreal anti-VEGF (bevacizumab, conbercept, or ranibizumab) at Puyang Eye Hospital (January 2020–June 2024). Based on 2-month best-corrected visual acuity (BCVA) changes, patients were classified into Good Prognosis (GP, n = 42) and Poor Prognosis (PP, n = 58) groups. OCT parameters (central macular thickness [CMT], subretinal fluid [SRF], hard exudates, etc.) were analyzed using chi-square test, Pearson correlation, ROC curve, and multivariate logistic regression. Results Baseline demographics showed no significant differences (P > 0.05). The PP group exhibited higher frequencies of retinal edema, hard exudates, cystic spaces, subretinal hyperreflective material (SHRM), hyperreflective foci, macular hemorrhage, SRF, RPE tear, and fibrosis (all P < 0.05). The GP group had higher baseline CMT (701.24 ± 556.13 µm vs. 474.34 ± 233.13 µm, P < 0.01). CMT correlated positively with postoperative logMAR BCVA (r = 0.496, P < 0.001) and showed moderate predictive value for prognosis (AUC = 0.707, cutoff = 538.5 µm, sensitivity = 76.2%, specificity = 70%). Multivariate analysis identified CMT as an independent protective factor (OR = 1.01, P < 0.001), while retinal edema, hard exudates, SHRM & ellipsoid zone (EZ) disruption, macular hemorrhage, and pigment epithelium detachment (PED) were risk factors. Conclusion Baseline OCT features are strongly associated with short-term visual outcomes in nAMD. CMT serves as an independent protective predictor, while edema, exudates, SHRM & EZ disruption, hemorrhage, and PED indicate poor prognosis. Neovascular age-related macular degeneration (nAMD) Anti-vascular endothelial growth factor (anti-VEGF) Optical coherence tomography (OCT) Visual prognosis Central macular thickness (CMT) Retinal pigment epithelium Subretinal fluid (SRF) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction According to data from the National Bureau of Statistics of China and relevant epidemiological surveys, with the acceleration of population aging in China, the prevalence of age-related macular degeneration (AMD) has been increasing year by year. Among these cases, neovascular AMD (nAMD) is one of the main causes of severe visual impairment and irreversible blindness in the elderly population 1 . The pathological features of nAMD include choroidal neovascularization, accompanied by exudation, hemorrhage, and scar formation, which severely damage the structure and function of the macular region and exert a significant impact on patients' quality of life 2 . In recent years, the advent of anti-vascular endothelial growth factor (anti-VEGF) agents has drastically transformed the treatment paradigm for nAMD, leading to a marked improvement in patients' visual prognosis 3 , 4 . However, there are significant differences in treatment responses among different patients, and some patients still experience limited visual recovery or disease recurrence 5 , 6 . How to accurately assess prognosis and predict treatment response before the initiation of therapy has become a key focus and challenge in clinical research. Optical coherence tomography (OCT), as a non-invasive, high-resolution imaging technique, can precisely visualize microstructural changes in the retinal macular region, providing objective evidence for the diagnosis, classification, efficacy evaluation, and follow-up of nAMD 7 , 8 . Existing studies have demonstrated that OCT parameters, such as central retinal thickness (CRT), subretinal fluid (SRF), pigment epithelium detachment (PED), and intraretinal fluid (IRF), are closely associated with visual recovery after anti-VEGF treatment 9 – 11 . Nevertheless, there remains a lack of systematic research on the correlation between characteristic OCT parameters and visual prognosis in nAMD patients following anti-VEGF therapy, and inconsistencies exist among the results of different studies 3 , 12 , 13 . Some researchers have found that the changes in best-corrected visual acuity (BCVA) and OCT parameters are most pronounced in the early stage of anti-VEGF treatment for nAMD patients, particularly at 3 months after treatment initiation 14 , 15 . However, in practical research, not all nAMD patients achieve favorable responses to anti-VEGF therapy, and visual prognosis is influenced by multiple factors 16 , 17 . In this study, we analyzed changes in OCT morphological indicators of nAMD patients undergoing anti-VEGF treatment, and further explored the factors affecting treatment efficacy and visual prognosis from both morphological parameters and visual acuity perspectives, aiming to identify valuable predictive indicators for visual prognosis in clinical practice. 2 Subjects and Methods 2.1 Study Subjects 2.1.1 General Information Retrospective analysis was conducted on clinical data of 100 nAMD patients (100 eyes) who received intravitreal anti-VEGF therapy (bevacizumab, conbercept, or ranibizumab) at Puyang Eye Hospital from January 2020 to June 2024. They were divided into two groups based on 2-month follow-up visual prognosis: Good Prognosis (GP) group (BCVA decrease < 0.1 logMAR) and Poor Prognosis (PP) group (BCVA decrease ≥ 0.1 logMAR). All patients provided written informed consent. The study was approved by the Science and Technology Ethics Committee (approval no.: xxx) and conducted per the Declaration of Helsinki. 2.1.2 Inclusion and Exclusion Criteria Inclusion Criteria: (1) nAMD confirmed by fundus examination, OCT, and FA; (2) newly diagnosed, first treated in this hospital, with 2-month treatment duration; (3) complete, clear OCT data.Exclusion Criteria: (1) Other ocular diseases (glaucoma, uveitis, etc.) or severe vitreous opacity affecting fundus observation; (2) major organ dysfunction (heart, liver, etc.); (3) retinal vein occlusion; (4) hematological/autoimmune diseases or malignant tumors; (5) vitreomacular traction; (6) follow-up < 2 months; (7) cognitive/language/psychiatric disorders; (8) significant refractive error (myopia ≥ − 6.00 D, hyperopia ≥ + 3.00 D, astigmatism ≥ 3.00 D); (9) ocular surgery history within 90 days before enrollment. 2.2 Study Methods Eligible patients received intravitreal anti-VEGF under sterile conditions. Comprehensive ophthalmic examinations included medical history collection, BCVA (Snellen converted to logMAR), OCT, and FA; ICGA was used only if diagnosis was unclear.OCT (Topcon DRIOCT Triton) was used for nAMD diagnosis and disease monitoring, with parameters: 20,000 A-scans/s, 6 µm axial resolution, 3D mode, 512×128-pixel range (optic disc centered). Tropicamide-phenylephrine drops were used for mydriasis if needed. Examinations were done 9:00–11:00 a.m. by the same ophthalmologist, with 3 measurements averaged.Recorded data: age, gender, surgical/ocular disease history, lens status, anti-VEGF injection times/type, and OCT parameters (macular hard exudates, cysts, etc.). Injections used 30-gauge needles: 3.5 mm posterior to limbus, 0.05 mL of the three agents (similar efficacy). 2.2.1 Poor Visual Prognosis Criteria 2-month outpatient follow-up (deadline: July 30, 2024): PP = BCVA decrease ≥ 0.1 logMAR; GP = visual improvement or BCVA decrease < 0.1 logMAR. 2.2.2 Statistical Methods Descriptive statistics were used: normally distributed data (mean ± SD), non-normal data (median [Q1, Q3]). Categorical data: chi-square test; inter-group comparison: Tukey’s test; BCVA-OCT correlation: Pearson analysis; OCT’s predictive value: ROC curves. p < 0.05 was significant. Sample size (100) ensured statistical power. 3 Results 3.1 Comparison of General Data between the Two Groups After 2 months of treatment, 42 patients were categorized into the good visual prognosis (GP) group, and 58 patients into the poor visual prognosis (PP) group. Variables including age, gender, anti-VEGF agent used, body mass index (BMI), and affected eye quadrants in the two groups are presented in Table 1 . The results showed no significant differences in general demographic and clinical data between the two groups (all P > 0.05). Table 1 Comparison of two groups of general data Characteristics Good prognosis (n = 42) Poor prognosis (n = 58) p t/x 2 Gender(%) 0 17 (40.5) 24 (41.4) 1 0.00 1 25 (59.5) 34 (58.6) 1 Age(mean(SD)) 70.98 (10.04) 69.50 (9.01) 0.443 0.76 Eye(%) 0 22 (52.4) 25 (43.1) 0.475 0.51 1 20 (47.6) 33 (56.9) Medication.Used(%) 1 11 (26.2) 12 (20.7) 0.801 0.44 2 21 (50.0) 32 (55.2) 3 10 (23.8) 14 (24.1) BMI(mean(SD)). 24.36 (2.51) 24.72 (2.44) 0.469 22.32 3.2 Comparison of OCT Parameters between Different Visual Outcome Groups The proportion of patients with abnormal OCT findings was higher in the poor visual prognosis (PP) group, with the following distributions: Retinal swelling: 36 cases (62.1%); Hard exudates: 25 cases (43.1%); Cystic spaces: 37 cases (63.8%); Changes in subretinal hyperreflective foci: 34 cases (58.6%); Hyperreflective dots: 35 cases (60.3%); Macular hemorrhage: 35 cases (60.3%); Subretinal fluid (SRF): 37 cases (63.8%); Pigment epithelium detachment (PED): 33 cases (56.9%); Retinal pigment epithelium (RPE) tear: 32 cases (55.2%); Fibrosis: 30 cases (51.7%). Among the OCT parameters, there was no significant difference in PED between the two groups (P > 0.05). In contrast, significant differences were observed in central macular thickness (CMT), retinal swelling, hard exudates, cystic spaces, subretinal hyperreflective material (SHRM), ellipsoid zone (EZ) integrity, hyperreflective foci (HF), macular hemorrhage, SRF, RPE tear, and fibrosis (all P < 0.05) (Table 2 ). Additionally, we found that the GP group had a significantly higher CMT value, and this difference was statistically significant (P < 0.01) (Fig. 1 ). Table 2 Statistical Baseline Table of OCT Indicators Among Groups with Different Postoperative Outcomes Characteristics Good prognosis (n = 42) Poor prognosis (n = 58) p t/x 2 CMT(mean(SD)) 701.24 (556.13) 474.34 (233.13) 0.006 97.95 Retinal Swelling 0 26 (61.9) 22 (37.9) 0.03 4.69 1 16 (38.1) 36 (62.1) Hard Exudates 0 39 (92.9) 33 (56.9) < 0.001 13.890 1 3 (7.1) 25 (43.1) Cystic Spaces 0 31 (73.8) 21 (36.2) < 0.001 12.33 1 11 (26.2) 37 (63.8) SHRM EZ Integrity 0 28 (66.7) 24 (41.4) 0.022 5.27 1 14 (33.3) 34 (58.6) Hyperreflective Foci(HF) 0 26 (61.9) 23 (39.7) 0.046 3.98 1 16 (38.1) 35 (60.3) Hemorrhages 0 32 (76.2) 30 (51.7) 0.023 5.79 1 10 (23.8) 28 (48.3) Subretinal fluid(SRF) 0 29 (69.0) 21 (36.2) 0.002 9.24 1 13 (31.0) 37 (63.8) Pigment epithelium detachmen(PED) 0 22 (52.4) 25 (43.1) 0.475 8.52 1 20 (47.6) 33 (56.9) RPE Tear 0 34 (81.0) 26 (44.8) 0.001 11.78 1 8 (19.0) 32 (55.2) Fibrosis 0 31 (73.8) 28 (48.3) 0.018 5.55 1 11 (26.2) 30 (51.7) 3.3 Correlation Analysis between OCT Assessment Results and Postoperative Visual Prognosis Poor postoperative visual prognosis in individuals with nAMD was positively correlated with central macular thickness (CMT), as shown in Table 3 and Fig. 2 . Table 3 Correlation between Central Macular Thickness (CMT) and LOGMAR Visual Acuity at 2 Months Postoperatively Characteristics LOGMAR visual acuity at 2 months postoperatively r p CMT 0.496 < 0.001 3.4 Evaluation value of OCT evaluation results on postoperative visual prognosis Taking poor visual prognosis as the dependent variable (yes = 1, no = 0), the pROC package was used to construct the ROC curve of the logistic regression model with CMT as the predictive variable. The roc() function was utilized to calculate the true positive rate (TPR) and false positive rate (FPR) under dynamic classification thresholds, thereby enabling the visual assessment of the model's predictive performance. The predictive performance indicators of CMT for poor visual prognosis were as follows: the area under the curve (AUC) reached 0.707, with a 95% confidence interval (95% CI) of 0.55–0.83, a sensitivity of 76.2%, and a specificity of 70%. These indicators reflect that CMT has a certain discriminative ability in the corresponding evaluation scenario and can serve as a reference basis for relevant judgments.These findings are detailed in Table 4 and Fig. 3 . Table 4 Predictive Performance of Central Macular Thickness (CMT) for Poor Visual Prognosis Assessed by ROC Curve Analysis Parameters Optimal AUC cutoff value AUC 95%CI Youden Index Sensiticity(%) Specificity(%) CMT 538.5 0.707 0.55–0.83 0.452 0.762 0.70 3.5 Multivariate Logistic Regression Analysis To further evaluate the correlation between OCT parameters and BCVA prognosis at 2 months after anti-VEGF therapy, all factors related to nAMD disease activity and progression were included in both univariate and multivariate logistic regression analysis models. Results of the multivariate logistic regression analysis confirmed that even after adjusting for confounding factors, central macular thickness (CMT) remained a significant influencing factor for post-treatment BCVA prognosis (odds ratio [OR] = 1.01, 95% confidence interval [95% CI]: 0.005–0.017, p < 0.001). Patients with higher CMT values generally achieved better visual recovery. Additionally, baseline retinal edema, hard exudates, subretinal hyperreflective material (SHRM) & ellipsoid zone (EZ) integrity, macular hemorrhages, and pigment epithelium detachment (PED) were identified as independent risk factors for poor visual prognosis (all p < 0.05) (Table 5 ). These factors all exhibited significantly negative coefficient estimates, indicating a significant negative correlation with the outcome. For example, regarding hard exudates, the results suggest that more severe hard exudation is associated with a higher likelihood of poor visual prognosis. (Fig. 4 ) Table 5 Multivariate Logistic Regression Analysis of OCT Parameters and BCVA Prognosis at 2 Months After Anti-VEGF Treatment Variable P OR(95%CI) CMT < 0.001 1.010(0.005, 0.017) Retinal Edema 0.011 0.059(-5.358, -0.903) Hard Exudates 0.001 0.011(-7.673, -2.086) Cystic Changes 0.051 0.197(-3.383, -0.047) SHRM & EZ Integrity 0.040 0.134(-3.972, -0.207) Hyperreflective Foci (HF) 0.075 0.198(-3.616, 0.037) Hemorrhages 0.017 0.069(-5.225, -0.725) Subretinal fluid (SRF) 0.065 0.197(-3.526, -0.003) Pigment epithelium detachment (PED) 0.007 0.060(-5.219, -1.005) RPE Tear 0.245 0.335(-3.071, 0.710) Fibrosis 0.087 0.208(-3.578, 0.119) 3.6 Correlation Analysis Among Various Optical Coherence Tomography (OCT) Features We also used Cramer's V coefficient and P-value to analyze the correlations among various optical coherence tomography (OCT) features. The results showed that different OCT features exhibited correlations to varying degrees. "Subretinal fluid (SRF)" and "subretinal hyperreflective material (SHRM) & ellipsoid zone (EZ) integrity", "cystic changes" and "hard exudates", and the intersections of "retinal pigment epithelium (RPE) tear" with "cystic changes" and "pigment epithelium detachment (PED)" showed relatively strong positive correlations, indicating statistically significant associations. 4 Discussion This retrospective study systematically investigated the correlation between baseline optical coherence tomography (OCT) parameters and visual prognosis at 2 months after anti-vascular endothelial growth factor (anti-VEGF) therapy in 100 patients with neovascular age-related macular degeneration (nAMD). Several OCT features with predictive value for treatment outcomes were identified. These findings provide important clinical insights for optimizing individualized treatment strategies and improving the accuracy of prognostic assessment in nAMD management. From the research results, OCT parameters such as central macular thickness (CMT), subretinal hyperreflective material (SHRM), hyperreflective foci (HRF), retinal thickening, macular hemorrhage, hard exudates, macular cysts, and subretinal fluid (SRF) were found to be associated with post-anti-VEGF visual prognosis to varying degrees. Our core finding confirmed that CMT is an independent protective factor for visual prognosis after anti-VEGF therapy (OR = 1.01, 95% confidence interval: 0.005–0.017, p < 0.001). A higher baseline CMT value was associated with better visual recovery at the 2-month follow-up. This observation is consistent with the pathological mechanism: the early macular edema reflected by increased CMT is mainly caused by reversible fluid accumulation, which responds well to anti-VEGF therapy 18 , 19 . In contrast, long-term disease progression may lead to irreversible structural damage, such as photoreceptor atrophy, thereby impairing the treatment response 20 —a pattern consistent with previous descriptions of the pathophysiological progression of nAMD. Receiver operating characteristic (ROC) curve analysis further verified the predictive utility of CMT, with an area under the curve (AUC) of 0.707, indicating its moderate discriminative ability for poor visual prognosis and supporting its potential as a practical baseline prognostic indicator. Multivariate logistic regression analysis identified several baseline OCT features as independent risk factors for poor outcomes, including retinal edema, hard exudates, subretinal hyperreflective material (SHRM), impaired ellipsoid zone (EZ) integrity, macular hemorrhage, and pigment epithelial detachment (PED). Among these, the negative correlation between hard exudates and visual prognosis is particularly noteworthy. As a marker of chronic metabolic tissue damage and inflammatory activation, persistent hard exudates may indicate prolonged disease activity and irreversible disruption of the retinal microenvironment 21 , 22 , which is consistent with previous studies showing that hard exudates are associated with disease progression rather than transient treatment responses 23 . Similarly, SHRM and impaired EZ integrity directly reflect dysfunction of photoreceptors and retinal pigment epithelium (RPE)—the key structural basis for visual function 24 . Their predictive value for poor prognosis is consistent with the observations of Sari et al. 25 , who reported that EZ loss is associated with severe visual impairment. The correlation heatmap of OCT features showed strong positive correlations between subretinal fluid (SRF) and SHRM as well as EZ integrity, between cystic changes and hard exudates, and between RPE tears and cystic changes as well as PED. These interrelationships reflect the synergistic pathological processes in nAMD: vascular leakage caused by choroidal neovascularization (CNV) first leads to SRF accumulation, which in turn impairs RPE barrier function and induces cystic degeneration, ultimately promoting the deposition of hard exudates and fibrosis 26 – 28 . Notably, the lack of a significant correlation between PED and visual prognosis in our study contradicts some previous reports 29 , 30 . This discrepancy may be attributed to the heterogeneity of PED subtypes—our cohort may contain a higher proportion of serous PED that responds well to anti-VEGF therapy, while fibrovascular PED (which is associated with poor outcomes) accounts for a smaller proportion. Our findings further support the consensus that OCT-derived morphological parameters are valuable prognostic tools in nAMD treatment. Consistent with previous meta-analyses, OCT exhibits high sensitivity in assessing disease activity and treatment response, although its specificity varies among different parameters 31 . The predictive role of CMT observed in our study complements the research by Cristian, which emphasized that OCT parameters are key markers of active nAMD 7 , while our identification of SHRM and EZ integrity as risk factors extends the work of Maiko, who associated these features with treatment resistance. However, we also observed discrepancies with some studies. For example, Ebenezer Daniel reported that baseline hard exudates had no significant impact on visual outcomes 32 , whereas our data identified them as an independent risk factor. This inconsistency may stem from differences in follow-up duration—their 2-year study showed that hard exudates resolved over time, while our 2-month assessment captured the acute phase, during which persistent exudation indicates a poor response. In addition, our finding that PED was not associated with prognosis contrasts with studies describing PED as a trigger for visual loss in pro re nata (PRN) treatment regimens 33 , which may be due to differences in treatment frequency (fixed vs. PRN) and PED subtype distribution. The correlation between fibrosis and poor prognosis observed in our study is consistent with the view of Professor J. Zhang, who stated that fibrosis is a major driver of long-term visual decline in nAMD, with 60% of fibrotic progression occurring in the first year 34 . This underscores the importance of early intervention to inhibit disease activity and reduce the risk of fibrosis—there is evidence to support that adequate initial anti-VEGF therapy can alleviate fibrosis, making this strategy reasonable. This study offers actionable clinical insights. Baseline assessment of multiple OCT parameters enables patient prognostic stratification; those with multiple risk factors may need more intensive initial treatment. CMT (AUC = 0.707) has moderate predictive value and should be combined with other parameters, consistent with multimodal imaging improving prognostic accuracy. Intercorrelations among OCT features suggest targeting multiple pathological pathways may benefit complex phenotypes and address non-VEGF-dependent treatment resistance. Strengths include focusing on the 2-month critical treatment phase, comprehensive analysis of 10 OCT parameters, and strict confounding variable control via multivariate regression. Limitations are single-center retrospective design (selection bias), small sample (100 eyes, limited generalizability), unsubclassified key parameters, short 2-month follow-up (no long-term outcomes), and unassessed genetic/systemic factors. Future studies should use prospective multicenter designs, subclassify OCT features, extend follow-up (≥ 1 year), combine OCTA with structural OCT, explore molecular mechanisms, and develop machine learning models for personalized prediction. 5 Conclusion This retrospective study systematically explored the association between baseline OCT parameters and 2-month visual prognosis in 100 nAMD patients receiving anti-VEGF therapy, and clarified the predictive value of specific OCT features for treatment outcomes, providing valuable clinical evidence for nAMD management. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the Puyang Second People’s Hospital (Puyang Eye Hospital) (Approval No. 20240110). All procedures followed relevant guidelines and the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests Funding No funding was received. Authors' contributions 作者的贡献 Wei Li: Conceptualization, Methodology, Formal analysis, Resources, Writing—Original draft Preparation and Review and Editing，Final approval of the version to be published; Junchen Zhang: Acquisition of data, Methodology, Formal analysis, Resources, Writing—Review and Editing; Changyun He 3 : Conceptualization, Methodology, Formal analysis, Resources, Writing—Original draft Preparation and Review and Editing; Qibin Jiao: Methodology, Formal analysis; Yanlin Wu: Formal analysis, Investigation, Writing—Review and Editing. Xiaojie Li: Formal analysis, Investigation, Writing—Review and Editing. 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Moraru AD, Danielescu C, Iorga RE, Moraru RL, Zemba M, Branisteanu DC. Review of Guideline Recommendations for Optimal Anti-VEGF Therapy in Age-Related Macular Degeneration. Life (Basel). 2024;14(10). 10.3390/life14101220 . Azar G, Wolff B, De Bats F, et al. Morphological Predictive Features on Spectral-Domain Optical Coherence Tomography for Visual Outcomes in Neovascular Age-Related Macular Degeneration Treated with Ranibizumab. Biomed Res Int. 2018;2018:7438083. 10.1155/2018/7438083 . Namvar E, Ahmadieh H, Maleki A, Nowroozzadeh MH. Sensitivity and specificity of optical coherence tomography angiography for diagnosis and classification of diabetic retinopathy; a systematic review and meta-analysis. Eur J Ophthalmol. 2023;33(6):2068–78. 10.1177/11206721231167458 . Daniel E, Grunwald JE, Kim BJ, et al. Visual and Morphologic Outcomes in Eyes with Hard Exudate in the Comparison of Age-Related Macular Degeneration Treatments Trials. Ophthalmol Retina. 2017;1(1):25–33. 10.1016/j.oret.2016.09.001 . Schmidt-Erfurth U, Klimscha S, Waldstein SM, Bogunović H. A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration. Eye (Lond). 2017;31(1):26–44. 10.1038/eye.2016.227 . Zhang J, Sheng X, Ding Q, Wang Y, Zhao J, Zhang J. Subretinal fibrosis secondary to neovascular age-related macular degeneration: mechanisms and potential therapeutic targets. Neural Regen Res. 2025;20(2):378–93. 10.4103/nrr.Nrr-d-23-01642 . Additional Declarations No competing interests reported. Supplementary Files file.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8383468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591311990,"identity":"f6c9962f-3232-4e91-946e-f0860b430a99","order_by":0,"name":"Wei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACgwNA4oEBAwMbe2Pjww9Ea0kAaeE53GwsQbwWEEsivU2Ahygt588ek0goOCzHJ/mwjUGCwU5Ot4GAFvsD59IkEgwOG7NJJ7Y9KGBINjY7QMiWgz1mQC23E9ukE9sNJBgOJG4jqOUwD1hLfZvkwTYJHqK0HINoSWCTYCRWyxkeY4sEg/+GbTyJwEA2IMYv588Y3vjwJ01evv34w4cfKuzkCGpBN4E05aNgFIyCUTAKcAAANfFBlMM6qAQAAAAASUVORK5CYII=","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":591311991,"identity":"5a3c7106-ca78-494f-968c-936220d9aec0","order_by":1,"name":"Junchen Zhang","email":"","orcid":"","institution":"Puyang Second People’s Hospital (Puyang Eye Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Junchen","middleName":"","lastName":"Zhang","suffix":""},{"id":591311992,"identity":"c6cc42ab-7601-4b72-b2c7-b59b756029e0","order_by":2,"name":"Changyun He","email":"","orcid":"","institution":"Hangzhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Changyun","middleName":"","lastName":"He","suffix":""},{"id":591311993,"identity":"ab13f5f7-ebf5-4047-95a8-444e19d9b122","order_by":3,"name":"Qibin Jiao","email":"","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Qibin","middleName":"","lastName":"Jiao","suffix":""},{"id":591311994,"identity":"902bf9df-9162-47ce-a620-057081675308","order_by":4,"name":"Yanlin Wu","email":"","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Yanlin","middleName":"","lastName":"Wu","suffix":""},{"id":591311995,"identity":"303360e4-e15d-44d4-95c8-87a5241b080b","order_by":5,"name":"Zhonghao Ji","email":"","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Zhonghao","middleName":"","lastName":"Ji","suffix":""},{"id":591311996,"identity":"c7aba209-5672-4a73-9030-8e94df11eae5","order_by":6,"name":"Zhitao Zhu","email":"","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Zhitao","middleName":"","lastName":"Zhu","suffix":""},{"id":591311997,"identity":"0242918f-eb10-4dab-8ae7-689692e2a8a3","order_by":7,"name":"Yangyi Li","email":"","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Yangyi","middleName":"","lastName":"Li","suffix":""},{"id":591311998,"identity":"61e990eb-9a54-42d4-a349-51752a98357e","order_by":8,"name":"Xiaojie Li","email":"","orcid":"","institution":"Hangzhou Normal University (Affiliated Hospital of Hangzhou Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-12-17 08:39:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8383468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8383468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102752170,"identity":"200f0bb0-8ad4-4a2e-8b94-bc6aeedef185","added_by":"auto","created_at":"2026-02-16 09:30:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of central macular thickness (CMT) between the good prognosis group and the poor prognosis group.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/56d86f3cd4d1e3976d90f77d.png"},{"id":102752118,"identity":"b2f750ee-4963-4158-a73e-368d747b8416","added_by":"auto","created_at":"2026-02-16 09:30:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61662,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the correlation between central macular thickness (CMT) and 2-month postoperative LOGMAR visual acuity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/3cbca388dd058d59e4478ed5.png"},{"id":102752180,"identity":"334e3dae-9708-4bcc-86b7-7882d61b6a77","added_by":"auto","created_at":"2026-02-16 09:30:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curve for Predicting Poor Visual Prognosis Using Central Macular Thickness (CMT)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/47078f7ed4f9ceec66df1afa.png"},{"id":102752190,"identity":"88efbd54-929a-457b-b0e4-d72b34ffbbf6","added_by":"auto","created_at":"2026-02-16 09:30:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate Logistic Regression Analysis of OCT Parameters and BCVA Prognosis at 2 Months After Anti-VEGF Treatment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/6acce38b117d3a8473d768ee.png"},{"id":102753108,"identity":"c4af2c9d-0659-4795-9068-136eff4f77fd","added_by":"auto","created_at":"2026-02-16 09:33:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Heatmap of Optical Coherence Tomography (OCT) Features Using Cramer's V Coefficient and p-value\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/64b2421700d9980b9508ecd9.png"},{"id":105535143,"identity":"b0823c45-c642-4fee-aa97-bbac006ea688","added_by":"auto","created_at":"2026-03-27 06:57:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1708771,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/e1879e16-5308-440e-a39e-884a5766e023.pdf"},{"id":102752162,"identity":"2ef1d54d-27bc-4f90-ba44-3cb51ab2729f","added_by":"auto","created_at":"2026-02-16 09:30:12","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22555,"visible":true,"origin":"","legend":"","description":"","filename":"file.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8383468/v1/b59b24542c85ef3448dcb5ce.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Analysis between Optical Coherence Tomography Parameters of Neovascular Age-Related Macular Degeneration and Visual Prognosis at 2 Months after Anti-VEGF Therapy","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAccording to data from the National Bureau of Statistics of China and relevant epidemiological surveys, with the acceleration of population aging in China, the prevalence of age-related macular degeneration (AMD) has been increasing year by year. Among these cases, neovascular AMD (nAMD) is one of the main causes of severe visual impairment and irreversible blindness in the elderly population \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The pathological features of nAMD include choroidal neovascularization, accompanied by exudation, hemorrhage, and scar formation, which severely damage the structure and function of the macular region and exert a significant impact on patients' quality of life \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, the advent of anti-vascular endothelial growth factor (anti-VEGF) agents has drastically transformed the treatment paradigm for nAMD, leading to a marked improvement in patients' visual prognosis \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, there are significant differences in treatment responses among different patients, and some patients still experience limited visual recovery or disease recurrence \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. How to accurately assess prognosis and predict treatment response before the initiation of therapy has become a key focus and challenge in clinical research.\u003c/p\u003e \u003cp\u003eOptical coherence tomography (OCT), as a non-invasive, high-resolution imaging technique, can precisely visualize microstructural changes in the retinal macular region, providing objective evidence for the diagnosis, classification, efficacy evaluation, and follow-up of nAMD \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Existing studies have demonstrated that OCT parameters, such as central retinal thickness (CRT), subretinal fluid (SRF), pigment epithelium detachment (PED), and intraretinal fluid (IRF), are closely associated with visual recovery after anti-VEGF treatment \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Nevertheless, there remains a lack of systematic research on the correlation between characteristic OCT parameters and visual prognosis in nAMD patients following anti-VEGF therapy, and inconsistencies exist among the results of different studies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSome researchers have found that the changes in best-corrected visual acuity (BCVA) and OCT parameters are most pronounced in the early stage of anti-VEGF treatment for nAMD patients, particularly at 3 months after treatment initiation \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, in practical research, not all nAMD patients achieve favorable responses to anti-VEGF therapy, and visual prognosis is influenced by multiple factors \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this study, we analyzed changes in OCT morphological indicators of nAMD patients undergoing anti-VEGF treatment, and further explored the factors affecting treatment efficacy and visual prognosis from both morphological parameters and visual acuity perspectives, aiming to identify valuable predictive indicators for visual prognosis in clinical practice.\u003c/p\u003e"},{"header":"2 Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Subjects\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 General Information\u003c/h2\u003e \u003cp\u003eRetrospective analysis was conducted on clinical data of 100 nAMD patients (100 eyes) who received intravitreal anti-VEGF therapy (bevacizumab, conbercept, or ranibizumab) at Puyang Eye Hospital from January 2020 to June 2024. They were divided into two groups based on 2-month follow-up visual prognosis: Good Prognosis (GP) group (BCVA decrease\u0026thinsp;\u0026lt;\u0026thinsp;0.1 logMAR) and Poor Prognosis (PP) group (BCVA decrease\u0026thinsp;\u0026ge;\u0026thinsp;0.1 logMAR). All patients provided written informed consent. The study was approved by the Science and Technology Ethics Committee (approval no.: xxx) and conducted per the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eInclusion Criteria: (1) nAMD confirmed by fundus examination, OCT, and FA; (2) newly diagnosed, first treated in this hospital, with 2-month treatment duration; (3) complete, clear OCT data.Exclusion Criteria: (1) Other ocular diseases (glaucoma, uveitis, etc.) or severe vitreous opacity affecting fundus observation; (2) major organ dysfunction (heart, liver, etc.); (3) retinal vein occlusion; (4) hematological/autoimmune diseases or malignant tumors; (5) vitreomacular traction; (6) follow-up \u0026lt;\u0026thinsp;2 months; (7) cognitive/language/psychiatric disorders; (8) significant refractive error (myopia\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;6.00 D, hyperopia\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;3.00 D, astigmatism\u0026thinsp;\u0026ge;\u0026thinsp;3.00 D); (9) ocular surgery history within 90 days before enrollment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Methods\u003c/h2\u003e \u003cp\u003eEligible patients received intravitreal anti-VEGF under sterile conditions. Comprehensive ophthalmic examinations included medical history collection, BCVA (Snellen converted to logMAR), OCT, and FA; ICGA was used only if diagnosis was unclear.OCT (Topcon DRIOCT Triton) was used for nAMD diagnosis and disease monitoring, with parameters: 20,000 A-scans/s, 6 \u0026micro;m axial resolution, 3D mode, 512\u0026times;128-pixel range (optic disc centered). Tropicamide-phenylephrine drops were used for mydriasis if needed. Examinations were done 9:00\u0026ndash;11:00 a.m. by the same ophthalmologist, with 3 measurements averaged.Recorded data: age, gender, surgical/ocular disease history, lens status, anti-VEGF injection times/type, and OCT parameters (macular hard exudates, cysts, etc.). Injections used 30-gauge needles: 3.5 mm posterior to limbus, 0.05 mL of the three agents (similar efficacy).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Poor Visual Prognosis Criteria\u003c/h2\u003e \u003cp\u003e2-month outpatient follow-up (deadline: July 30, 2024): PP\u0026thinsp;=\u0026thinsp;BCVA decrease\u0026thinsp;\u0026ge;\u0026thinsp;0.1 logMAR; GP\u0026thinsp;=\u0026thinsp;visual improvement or BCVA decrease\u0026thinsp;\u0026lt;\u0026thinsp;0.1 logMAR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Statistical Methods\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used: normally distributed data (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), non-normal data (median [Q1, Q3]). Categorical data: chi-square test; inter-group comparison: Tukey\u0026rsquo;s test; BCVA-OCT correlation: Pearson analysis; OCT\u0026rsquo;s predictive value: ROC curves. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was significant. Sample size (100) ensured statistical power.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparison of General Data between the Two Groups\u003c/h2\u003e \u003cp\u003eAfter 2 months of treatment, 42 patients were categorized into the good visual prognosis (GP) group, and 58 patients into the poor visual prognosis (PP) group. Variables including age, gender, anti-VEGF agent used, body mass index (BMI), and affected eye quadrants in the two groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results showed no significant differences in general demographic and clinical data between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of two groups of general data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et/x\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(mean(SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.98 (10.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.50 (9.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEye(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication.Used(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(mean(SD)).\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.36 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.72 (2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of OCT Parameters between Different Visual Outcome Groups\u003c/h2\u003e \u003cp\u003eThe proportion of patients with abnormal OCT findings was higher in the poor visual prognosis (PP) group, with the following distributions: Retinal swelling: 36 cases (62.1%); Hard exudates: 25 cases (43.1%); Cystic spaces: 37 cases (63.8%); Changes in subretinal hyperreflective foci: 34 cases (58.6%); Hyperreflective dots: 35 cases (60.3%); Macular hemorrhage: 35 cases (60.3%); Subretinal fluid (SRF): 37 cases (63.8%); Pigment epithelium detachment (PED): 33 cases (56.9%); Retinal pigment epithelium (RPE) tear: 32 cases (55.2%); Fibrosis: 30 cases (51.7%). Among the OCT parameters, there was no significant difference in PED between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, significant differences were observed in central macular thickness (CMT), retinal swelling, hard exudates, cystic spaces, subretinal hyperreflective material (SHRM), ellipsoid zone (EZ) integrity, hyperreflective foci (HF), macular hemorrhage, SRF, RPE tear, and fibrosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, we found that the GP group had a significantly higher CMT value, and this difference was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Baseline Table of OCT Indicators Among Groups with Different Postoperative Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et/x\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMT(mean(SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e701.24 (556.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e474.34 (233.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRetinal Swelling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHard Exudates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCystic Spaces\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSHRM EZ Integrity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperreflective Foci(HF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemorrhages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubretinal fluid(SRF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePigment epithelium detachmen(PED)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRPE Tear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFibrosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation Analysis between OCT Assessment Results and Postoperative Visual Prognosis\u003c/h2\u003e \u003cp\u003ePoor postoperative visual prognosis in individuals with nAMD was positively correlated with central macular thickness (CMT), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between Central Macular Thickness (CMT) and LOGMAR Visual Acuity at 2 Months Postoperatively\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOGMAR visual acuity at 2 months postoperatively\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Evaluation value of OCT evaluation results on postoperative visual prognosis\u003c/h2\u003e \u003cp\u003eTaking poor visual prognosis as the dependent variable (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0), the pROC package was used to construct the ROC curve of the logistic regression model with CMT as the predictive variable. The roc() function was utilized to calculate the true positive rate (TPR) and false positive rate (FPR) under dynamic classification thresholds, thereby enabling the visual assessment of the model's predictive performance. The predictive performance indicators of CMT for poor visual prognosis were as follows: the area under the curve (AUC) reached 0.707, with a 95% confidence interval (95% CI) of 0.55\u0026ndash;0.83, a sensitivity of 76.2%, and a specificity of 70%. These indicators reflect that CMT has a certain discriminative ability in the corresponding evaluation scenario and can serve as a reference basis for relevant judgments.These findings are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive Performance of Central Macular Thickness (CMT) for Poor Visual Prognosis Assessed by ROC Curve Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimal AUC cutoff value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensiticity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e538.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Multivariate Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eTo further evaluate the correlation between OCT parameters and BCVA prognosis at 2 months after anti-VEGF therapy, all factors related to nAMD disease activity and progression were included in both univariate and multivariate logistic regression analysis models. Results of the multivariate logistic regression analysis confirmed that even after adjusting for confounding factors, central macular thickness (CMT) remained a significant influencing factor for post-treatment BCVA prognosis (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.01, 95% confidence interval [95% CI]: 0.005\u0026ndash;0.017, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with higher CMT values generally achieved better visual recovery. Additionally, baseline retinal edema, hard exudates, subretinal hyperreflective material (SHRM) \u0026amp; ellipsoid zone (EZ) integrity, macular hemorrhages, and pigment epithelium detachment (PED) were identified as independent risk factors for poor visual prognosis (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These factors all exhibited significantly negative coefficient estimates, indicating a significant negative correlation with the outcome. For example, regarding hard exudates, the results suggest that more severe hard exudation is associated with a higher likelihood of poor visual prognosis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Analysis of OCT Parameters and BCVA Prognosis at 2 Months After Anti-VEGF Treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.010(0.005, 0.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRetinal Edema\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059(-5.358, -0.903)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHard Exudates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011(-7.673, -2.086)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCystic Changes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.197(-3.383, -0.047)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSHRM \u0026amp; EZ Integrity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134(-3.972, -0.207)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperreflective Foci (HF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.198(-3.616, 0.037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemorrhages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069(-5.225, -0.725)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubretinal fluid (SRF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.197(-3.526, -0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePigment epithelium detachment (PED)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060(-5.219, -1.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRPE Tear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.335(-3.071, 0.710)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFibrosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.208(-3.578, 0.119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation Analysis Among Various Optical Coherence Tomography (OCT) Features\u003c/h2\u003e \u003cp\u003eWe also used Cramer's V coefficient and P-value to analyze the correlations among various optical coherence tomography (OCT) features. The results showed that different OCT features exhibited correlations to varying degrees. \"Subretinal fluid (SRF)\" and \"subretinal hyperreflective material (SHRM) \u0026amp; ellipsoid zone (EZ) integrity\", \"cystic changes\" and \"hard exudates\", and the intersections of \"retinal pigment epithelium (RPE) tear\" with \"cystic changes\" and \"pigment epithelium detachment (PED)\" showed relatively strong positive correlations, indicating statistically significant associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis retrospective study systematically investigated the correlation between baseline optical coherence tomography (OCT) parameters and visual prognosis at 2 months after anti-vascular endothelial growth factor (anti-VEGF) therapy in 100 patients with neovascular age-related macular degeneration (nAMD). Several OCT features with predictive value for treatment outcomes were identified. These findings provide important clinical insights for optimizing individualized treatment strategies and improving the accuracy of prognostic assessment in nAMD management.\u003c/p\u003e \u003cp\u003eFrom the research results, OCT parameters such as central macular thickness (CMT), subretinal hyperreflective material (SHRM), hyperreflective foci (HRF), retinal thickening, macular hemorrhage, hard exudates, macular cysts, and subretinal fluid (SRF) were found to be associated with post-anti-VEGF visual prognosis to varying degrees. Our core finding confirmed that CMT is an independent protective factor for visual prognosis after anti-VEGF therapy (OR\u0026thinsp;=\u0026thinsp;1.01, 95% confidence interval: 0.005\u0026ndash;0.017, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A higher baseline CMT value was associated with better visual recovery at the 2-month follow-up. This observation is consistent with the pathological mechanism: the early macular edema reflected by increased CMT is mainly caused by reversible fluid accumulation, which responds well to anti-VEGF therapy \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In contrast, long-term disease progression may lead to irreversible structural damage, such as photoreceptor atrophy, thereby impairing the treatment response\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u0026mdash;a pattern consistent with previous descriptions of the pathophysiological progression of nAMD. Receiver operating characteristic (ROC) curve analysis further verified the predictive utility of CMT, with an area under the curve (AUC) of 0.707, indicating its moderate discriminative ability for poor visual prognosis and supporting its potential as a practical baseline prognostic indicator.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis identified several baseline OCT features as independent risk factors for poor outcomes, including retinal edema, hard exudates, subretinal hyperreflective material (SHRM), impaired ellipsoid zone (EZ) integrity, macular hemorrhage, and pigment epithelial detachment (PED). Among these, the negative correlation between hard exudates and visual prognosis is particularly noteworthy. As a marker of chronic metabolic tissue damage and inflammatory activation, persistent hard exudates may indicate prolonged disease activity and irreversible disruption of the retinal microenvironment \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, which is consistent with previous studies showing that hard exudates are associated with disease progression rather than transient treatment responses\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Similarly, SHRM and impaired EZ integrity directly reflect dysfunction of photoreceptors and retinal pigment epithelium (RPE)\u0026mdash;the key structural basis for visual function \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Their predictive value for poor prognosis is consistent with the observations of Sari et al. \u003csup\u003e25\u003c/sup\u003e, who reported that EZ loss is associated with severe visual impairment.\u003c/p\u003e \u003cp\u003eThe correlation heatmap of OCT features showed strong positive correlations between subretinal fluid (SRF) and SHRM as well as EZ integrity, between cystic changes and hard exudates, and between RPE tears and cystic changes as well as PED. These interrelationships reflect the synergistic pathological processes in nAMD: vascular leakage caused by choroidal neovascularization (CNV) first leads to SRF accumulation, which in turn impairs RPE barrier function and induces cystic degeneration, ultimately promoting the deposition of hard exudates and fibrosis \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Notably, the lack of a significant correlation between PED and visual prognosis in our study contradicts some previous reports \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This discrepancy may be attributed to the heterogeneity of PED subtypes\u0026mdash;our cohort may contain a higher proportion of serous PED that responds well to anti-VEGF therapy, while fibrovascular PED (which is associated with poor outcomes) accounts for a smaller proportion.\u003c/p\u003e \u003cp\u003eOur findings further support the consensus that OCT-derived morphological parameters are valuable prognostic tools in nAMD treatment. Consistent with previous meta-analyses, OCT exhibits high sensitivity in assessing disease activity and treatment response, although its specificity varies among different parameters \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The predictive role of CMT observed in our study complements the research by Cristian, which emphasized that OCT parameters are key markers of active nAMD \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, while our identification of SHRM and EZ integrity as risk factors extends the work of Maiko, who associated these features with treatment resistance.\u003c/p\u003e \u003cp\u003eHowever, we also observed discrepancies with some studies. For example, Ebenezer Daniel reported that baseline hard exudates had no significant impact on visual outcomes \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, whereas our data identified them as an independent risk factor. This inconsistency may stem from differences in follow-up duration\u0026mdash;their 2-year study showed that hard exudates resolved over time, while our 2-month assessment captured the acute phase, during which persistent exudation indicates a poor response. In addition, our finding that PED was not associated with prognosis contrasts with studies describing PED as a trigger for visual loss in pro re nata (PRN) treatment regimens \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which may be due to differences in treatment frequency (fixed vs. PRN) and PED subtype distribution.\u003c/p\u003e \u003cp\u003eThe correlation between fibrosis and poor prognosis observed in our study is consistent with the view of Professor J. Zhang, who stated that fibrosis is a major driver of long-term visual decline in nAMD, with 60% of fibrotic progression occurring in the first year \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This underscores the importance of early intervention to inhibit disease activity and reduce the risk of fibrosis\u0026mdash;there is evidence to support that adequate initial anti-VEGF therapy can alleviate fibrosis, making this strategy reasonable.\u003c/p\u003e \u003cp\u003eThis study offers actionable clinical insights. Baseline assessment of multiple OCT parameters enables patient prognostic stratification; those with multiple risk factors may need more intensive initial treatment. CMT (AUC\u0026thinsp;=\u0026thinsp;0.707) has moderate predictive value and should be combined with other parameters, consistent with multimodal imaging improving prognostic accuracy. Intercorrelations among OCT features suggest targeting multiple pathological pathways may benefit complex phenotypes and address non-VEGF-dependent treatment resistance.\u003c/p\u003e \u003cp\u003eStrengths include focusing on the 2-month critical treatment phase, comprehensive analysis of 10 OCT parameters, and strict confounding variable control via multivariate regression.\u003c/p\u003e \u003cp\u003eLimitations are single-center retrospective design (selection bias), small sample (100 eyes, limited generalizability), unsubclassified key parameters, short 2-month follow-up (no long-term outcomes), and unassessed genetic/systemic factors.\u003c/p\u003e \u003cp\u003eFuture studies should use prospective multicenter designs, subclassify OCT features, extend follow-up (\u0026ge;\u0026thinsp;1 year), combine OCTA with structural OCT, explore molecular mechanisms, and develop machine learning models for personalized prediction.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis retrospective study systematically explored the association between baseline OCT parameters and 2-month visual prognosis in 100 nAMD patients receiving anti-VEGF therapy, and clarified the predictive value of specific OCT features for treatment outcomes, providing valuable clinical evidence for nAMD management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Puyang Second People\u0026rsquo;s Hospital (Puyang Eye Hospital) (Approval No. 20240110). All procedures followed relevant guidelines and the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e 作者的贡献\u003c/p\u003e\n\u003cp\u003eWei Li: Conceptualization, Methodology, Formal analysis, Resources, Writing\u0026mdash;Original draft Preparation and Review and Editing，Final approval of the version to be published; Junchen Zhang: Acquisition of data, Methodology, Formal analysis, Resources, Writing\u0026mdash;Review and Editing;\u0026nbsp;Changyun He\u003csup\u003e3\u003c/sup\u003e: Conceptualization, Methodology, Formal analysis, Resources, Writing\u0026mdash;Original draft Preparation and Review and Editing;\u0026nbsp;Qibin Jiao: Methodology, Formal analysis; Yanlin Wu: Formal analysis, Investigation, Writing\u0026mdash;Review and Editing. Xiaojie Li: Formal analysis, Investigation, Writing\u0026mdash;Review and Editing.\u0026nbsp;Zhonghao Ji: Formal analysis, Investigation, Writing\u0026mdash;Review and Editing. all authors agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHenriques C, da Ana R, Krambeck K, et al. Monoclonal Antibodies for the Treatment of Ocular Diseases. J Clin Med. 2024;13(19). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm13195815\u003c/span\u003e\u003cspan address=\"10.3390/jcm13195815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubin A-M, Korva-Gurung I, Ohtonen P, Hautala N. 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Neural Regen Res. 2025;20(2):378\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/nrr.Nrr-d-23-01642\u003c/span\u003e\u003cspan address=\"10.4103/nrr.Nrr-d-23-01642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neovascular age-related macular degeneration (nAMD), Anti-vascular endothelial growth factor (anti-VEGF), Optical coherence tomography (OCT), Visual prognosis, Central macular thickness (CMT), Retinal pigment epithelium, Subretinal fluid (SRF)","lastPublishedDoi":"10.21203/rs.3.rs-8383468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8383468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the correlation between baseline optical coherence tomography (OCT) parameters and 2-month visual prognosis after anti-vascular endothelial growth factor (anti-VEGF) therapy in patients with neovascular age-related macular degeneration (nAMD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was performed on 100 nAMD patients treated with intravitreal anti-VEGF (bevacizumab, conbercept, or ranibizumab) at Puyang Eye Hospital (January 2020\u0026ndash;June 2024). Based on 2-month best-corrected visual acuity (BCVA) changes, patients were classified into Good Prognosis (GP, n\u0026thinsp;=\u0026thinsp;42) and Poor Prognosis (PP, n\u0026thinsp;=\u0026thinsp;58) groups. OCT parameters (central macular thickness [CMT], subretinal fluid [SRF], hard exudates, etc.) were analyzed using chi-square test, Pearson correlation, ROC curve, and multivariate logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBaseline demographics showed no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The PP group exhibited higher frequencies of retinal edema, hard exudates, cystic spaces, subretinal hyperreflective material (SHRM), hyperreflective foci, macular hemorrhage, SRF, RPE tear, and fibrosis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The GP group had higher baseline CMT (701.24\u0026thinsp;\u0026plusmn;\u0026thinsp;556.13 \u0026micro;m vs. 474.34\u0026thinsp;\u0026plusmn;\u0026thinsp;233.13 \u0026micro;m, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). CMT correlated positively with postoperative logMAR BCVA (r\u0026thinsp;=\u0026thinsp;0.496, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and showed moderate predictive value for prognosis (AUC\u0026thinsp;=\u0026thinsp;0.707, cutoff\u0026thinsp;=\u0026thinsp;538.5 \u0026micro;m, sensitivity\u0026thinsp;=\u0026thinsp;76.2%, specificity\u0026thinsp;=\u0026thinsp;70%). Multivariate analysis identified CMT as an independent protective factor (OR\u0026thinsp;=\u0026thinsp;1.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while retinal edema, hard exudates, SHRM \u0026amp; ellipsoid zone (EZ) disruption, macular hemorrhage, and pigment epithelium detachment (PED) were risk factors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBaseline OCT features are strongly associated with short-term visual outcomes in nAMD. CMT serves as an independent protective predictor, while edema, exudates, SHRM \u0026amp; EZ disruption, hemorrhage, and PED indicate poor prognosis.\u003c/p\u003e","manuscriptTitle":"Correlation Analysis between Optical Coherence Tomography Parameters of Neovascular Age-Related Macular Degeneration and Visual Prognosis at 2 Months after Anti-VEGF Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 08:46:16","doi":"10.21203/rs.3.rs-8383468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cee17c78-d0bb-4538-94eb-7b718e61548d","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T06:56:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 08:46:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8383468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8383468","identity":"rs-8383468","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}