Evidence map›Paper›PMID 42363174›Full record

ArticleBMC ophthalmology2026

Association of early anti-VEGF Therapy anatomical response with renal function and OCT biomarkers in diabetic macular edema.

Huixiang Chen, Yu Shang, Peizhen Yang, Yanping Zhou, Yuanzhi Yuan

Abstract read
In one paragraph

Article in BMC ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Huixiang ChenDepartment of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yu ShangDepartment of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China.
Peizhen YangDepartment of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yanping ZhouDepartment of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yuanzhi YuanDepartment of Ophthalmology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. yuan.yuanzhi@zs-hospital.sh.cn.ORCID http://orcid.org/0000-0002-5240-3915

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo investigate the predictors of anatomical response in patients with diabetic macular edema (DME) following anti-vascular endothelial growth factor (anti-VEGF) therapy and establish a nomogram model for predicting the probability of anatomical response.

methodsThis study enrolled 200 DME patients treated with anti-VEGF regimen. Based on the reduction rate of central macular thickness (CMT) following treatment, patients were classified into an anatomical weak responder group (CMT reduction < 20%) and an anatomical responder group (CMT reduction ≥ 20%). Baseline clinical data and OCT biomarkers were analyzed with multivariate logistic regression. A nomogram model was constructed by using R software. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model.

resultsPredictors for anatomical response in DME patients are serum creatinine (Scr), CMT, photoreceptor outer segment length (PROSL), and cystoid macular edema (CME) presence as independent variables. The nomogram prediction model based on the above four predictors had good representativeness (Bootstrap method: precision: 0.820), differentiation [the area under curve (AUC) value: 0.819], and the DCA analysis showed that the prediction model, whose threshold probability was in the range of 0 to 1, had clinical practical value.

conclusionThe anatomical response to anti-VEGF treatment for DME is independently associated with baseline Scr, CMT, PROSL, and the presence of CME.

Indexed as

Angiogenesis InhibitorsBevacizumabDiabetic RetinopathyMacular EdemaRanibizumabAgedBiomarkersCreatinineFemaleHumansIntravitreal InjectionsMaleMiddle AgedNomogramsRetrospective StudiesROC CurveAngiogenesis InhibitorsBevacizumabBiomarkersCreatinineRanibizumabVascular Endothelial Growth Factor AAnatomical treatment responseDiabetic macular edemaOptical coherence tomographySerum creatinine

Identifiers

PMID42363174
PMCPMC13576092

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.