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Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study

BACKGROUND: To develop a model for predicting the risk of visual impairment in diabetic retinopathy (DR) by a nomogram. METHODS: Patients with DR who underwent both optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) were retrospectively enrolled. FFA was conduct...

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Autores principales: Zhao, Yuancheng, Yu, Rentao, Sun, Chao, Fan, Wei, Zou, Huan, Chen, Xiaofan, Huang, Yanming, Yuan, Rongdi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733396/
https://www.ncbi.nlm.nih.gov/pubmed/36482340
http://dx.doi.org/10.1186/s12886-022-02710-6
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author Zhao, Yuancheng
Yu, Rentao
Sun, Chao
Fan, Wei
Zou, Huan
Chen, Xiaofan
Huang, Yanming
Yuan, Rongdi
author_facet Zhao, Yuancheng
Yu, Rentao
Sun, Chao
Fan, Wei
Zou, Huan
Chen, Xiaofan
Huang, Yanming
Yuan, Rongdi
author_sort Zhao, Yuancheng
collection PubMed
description BACKGROUND: To develop a model for predicting the risk of visual impairment in diabetic retinopathy (DR) by a nomogram. METHODS: Patients with DR who underwent both optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) were retrospectively enrolled. FFA was conducted for DR staging, swept-source optical coherence tomography (SS-OCT) of the macula and 3*3-mm blood flow imaging by OCTA to observe retinal structure and blood flow parameters. We defined a logarithm of the minimum angle of resolution visual acuity (LogMAR VA) ≥0.5 as visual impairment, and the characteristics correlated with VA were screened using binary logistic regression. The selected factors were then entered into a multivariate binary stepwise regression, and a nomogram was developed to predict visual impairment risk. Finally, the model was validated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 parameters were included in the analysis, and 13 characteristics were used to develop a nomogram model. Finally, diabetic macular ischaemia (DMI) grading, disorganization of the retinal inner layers (DRIL), outer layer disruption, and the vessel density of choriocapillaris layer inferior (SubVD) were found to be statistically significant (P < 0.05). The model was found to have good accuracy based on the ROC (AUC = 0.931) and calibration curves (C-index = 0.930). The DCA showed that risk threshold probabilities in the (3–91%) interval models can be used to guide clinical practice, and the proportion of people at risk at each threshold probability is illustrated by the CIC. CONCLUSION: The nomogram model for predicting visual impairment in DR patients demonstrated good accuracy and utility, and it can be used to guide clinical practice. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2200059835. Registered 12 May 2022, https://www.chictr.org.cn/edit.aspx?pid=169290&htm=4 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12886-022-02710-6.
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spelling pubmed-97333962022-12-10 Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study Zhao, Yuancheng Yu, Rentao Sun, Chao Fan, Wei Zou, Huan Chen, Xiaofan Huang, Yanming Yuan, Rongdi BMC Ophthalmol Research BACKGROUND: To develop a model for predicting the risk of visual impairment in diabetic retinopathy (DR) by a nomogram. METHODS: Patients with DR who underwent both optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) were retrospectively enrolled. FFA was conducted for DR staging, swept-source optical coherence tomography (SS-OCT) of the macula and 3*3-mm blood flow imaging by OCTA to observe retinal structure and blood flow parameters. We defined a logarithm of the minimum angle of resolution visual acuity (LogMAR VA) ≥0.5 as visual impairment, and the characteristics correlated with VA were screened using binary logistic regression. The selected factors were then entered into a multivariate binary stepwise regression, and a nomogram was developed to predict visual impairment risk. Finally, the model was validated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 parameters were included in the analysis, and 13 characteristics were used to develop a nomogram model. Finally, diabetic macular ischaemia (DMI) grading, disorganization of the retinal inner layers (DRIL), outer layer disruption, and the vessel density of choriocapillaris layer inferior (SubVD) were found to be statistically significant (P < 0.05). The model was found to have good accuracy based on the ROC (AUC = 0.931) and calibration curves (C-index = 0.930). The DCA showed that risk threshold probabilities in the (3–91%) interval models can be used to guide clinical practice, and the proportion of people at risk at each threshold probability is illustrated by the CIC. CONCLUSION: The nomogram model for predicting visual impairment in DR patients demonstrated good accuracy and utility, and it can be used to guide clinical practice. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2200059835. Registered 12 May 2022, https://www.chictr.org.cn/edit.aspx?pid=169290&htm=4 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12886-022-02710-6. BioMed Central 2022-12-08 /pmc/articles/PMC9733396/ /pubmed/36482340 http://dx.doi.org/10.1186/s12886-022-02710-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhao, Yuancheng
Yu, Rentao
Sun, Chao
Fan, Wei
Zou, Huan
Chen, Xiaofan
Huang, Yanming
Yuan, Rongdi
Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
title Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
title_full Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
title_fullStr Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
title_full_unstemmed Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
title_short Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
title_sort nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733396/
https://www.ncbi.nlm.nih.gov/pubmed/36482340
http://dx.doi.org/10.1186/s12886-022-02710-6
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