Nomogram Model Predicts the Risk of Visual Impairment in Diabetic Retinopathy: a Retrospective Study

Overview

Journal BMC Ophthalmol

Publisher Biomed Central

Specialty Ophthalmology

Date 2022 Dec 9

PMID 36482340

Authors

Yuancheng Zhao

Rentao Yu

Chao Sun

Wei Fan

Huan Zou

Xiaofan Chen

Yanming Huang

Rongdi Yuan

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms.

Mao L, Yu Z, Lin L, Sharma M, Song H, Zhao H JMIR Aging. 2024; 7:e59810.

PMID: 39382570 PMC: 11481821. DOI: 10.2196/59810.

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