Development and Internal Validation of a Risk Prediction Model for Visual Impairment Progression in Elderly Patients with Diabetic Retinopathy: A Single-Center Retrospective Study.
- 1Department of Ophthalmology, Guangxi Hospital, The First Affiliated Hospital of Sun Yat-sen University, Nanning, People's Republic of China.
- 2Department of Ophthalmology, The First People's Hospital of Nanning, Nanning, People's Republic of China.
Abstract
Objective
To identify predictors of visual impairment progression in elderly patients with diabetic retinopathy (DR) and to establish a risk prediction model.Methods
This single-center retrospective study included 443 elderly patients with DR treated between January 2022 and January 2025. According to the occurrence of visual impairment within 12 months after discharge, patients were divided into progression and non-progression groups. Multivariate logistic regression analysis was used to identify predictors, and a nomogram-based risk prediction model was constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate model performance.Results
Multivariate Logistic regression analysis demonstrated that duration of diabetes, educational level, duration of DR, presence of other underlying diseases, DR stage, baseline glycated hemoglobin (HbA1c), and vascular endothelial growth factor A (VEGFA) level were independently associated with visual impairment progression and were included as predictors in the final model. ROC curve analysis showed that the area under the curve (AUC) was 0.932 in the training cohort and 0.925 in the validation cohort, indicating good discriminative performance. Bootstrap validation demonstrated good agreement between the calibration curve and the ideal curve, and no significant difference was observed between the predicted and observed values in either cohort (χ²=7.355 and 2.960, P=0.499 and 0.937, respectively), suggesting favorable calibration accuracy. DCA showed positive net benefit across threshold probabilities of 0.03-0.96 in both cohorts.Conclusion
Duration of diabetes, educational level, duration of DR, presence of other underlying diseases, DR stage, baseline HbA1c, and VEGFA levels were associated with visual impairment progression in elderly patients with DR. The nomogram demonstrated good discrimination and calibration in this single-center cohort; however, its performance may be optimistic because the validation cohort was derived from the same institution, and independent external validation is required before broader clinical application.
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BibTeX
@article{liang2026development,
title = {{Development and Internal Validation of a Risk Prediction Model for Visual Impairment Progression in Elderly Patients with Diabetic Retinopathy: A Single-Center Retrospective Study.}},
author = {Haiming Liang and Pinzhang Hu and Liu Qin and Qi Tang},
journal = {International journal of general medicine},
year = {2026},
month = oct,
doi = {10.2147/ijgm.s641537},
url = {https://europepmc.org/article/MED/42903758},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Development and Internal Validation of a Risk Prediction Model for Visual Impairment Progression in Elderly Patients with Diabetic Retinopathy: A Single-Center Retrospective Study.
AU - Haiming Liang
AU - Pinzhang Hu
AU - Liu Qin
AU - Qi Tang
PY - 2026
DA - 2026/10/16
JO - International journal of general medicine
DO - 10.2147/ijgm.s641537
UR - https://europepmc.org/article/MED/42903758
AB - Objective To identify predictors of visual impairment progression in elderly patients with diabetic retinopathy (DR) and to establish a risk prediction model.Methods This single-center retrospective study included 443 elderly patients with DR treated between January 2022 and January 2025. According to the occurrence of visual impairment within 12 months after discharge, patients were divided into progression and non-progression groups. Multivariate logistic regression analysis was used to identify predictors, and a nomogram-based risk prediction model was constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate model performance.Results Multivariate Logistic regression analysis demonstrated that duration of diabetes, educational level, duration of DR, presence of other underlying diseases, DR stage, baseline glycated hemoglobin (HbA1c), and vascular endothelial growth factor A (VEGFA) level were independently associated with visual impairment progression and were included as predictors in the final model. ROC curve analysis showed that the area under the curve (AUC) was 0.932 in the training cohort and 0.925 in the validation cohort, indicating good discriminative performance. Bootstrap validation demonstrated good agreement between the calibration curve and the ideal curve, and no significant difference was observed between the predicted and observed values in either cohort (χ²=7.355 and 2.960, P=0.499 and 0.937, respectively), suggesting favorable calibration accuracy. DCA showed positive net benefit across threshold probabilities of 0.03-0.96 in both cohorts.Conclusion Duration of diabetes, educational level, duration of DR, presence of other underlying diseases, DR stage, baseline HbA1c, and VEGFA levels were associated with visual impairment progression in elderly patients with DR. The nomogram demonstrated good discrimination and calibration in this single-center cohort; however, its performance may be optimistic because the validation cohort was derived from the same institution, and independent external validation is required before broader clinical application.
SN - 1178-7074
ER -