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An Ex Vivo Tissue Culture Model for Fibrovascular Complications in Proliferative Diabetic Retinopathy

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Published on: January 25, 2019

Development and external validation of a predictive model for type 2 diabetic retinopathy.

Yongsheng Li¹, Bin Hu¹, Lian Lu²

¹Department of Preventive Medicine, Medical College, Tarim University, Alar, 843300, China.

Scientific Reports

|July 20, 2024

Summary

A new nomogram model accurately predicts diabetic retinopathy (DR) risk in type 2 diabetes patients. This tool aids early intervention, potentially reducing vision loss and blindness.

Keywords:

25(OH)D3 Diabetes retinopathy Nomogram Prediction model Type 2 diabetes mellitus (T2DM)

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Area of Science:

Ophthalmology
Endocrinology
Medical Informatics

Background:

Diabetic retinopathy (DR) is a leading cause of irreversible blindness in adults.
Early detection and intervention are crucial for managing DR and preserving vision.
Existing methods for DR risk prediction require enhancement for clinical utility.

Purpose of the Study:

To develop and externally validate a predictive nomogram for early diabetic retinopathy (DR) risk in type 2 diabetes mellitus (T2DM) patients.
To identify key clinical and biochemical predictors of DR.
To assess the nomogram's predictive performance and clinical applicability.

Main Methods:

Retrospective study of 2381 T2DM patients for model development and 962 T2DM patients for external validation.
Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression to identify independent predictors.
Nomogram performance evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA).

Main Results:

A nomogram was established using Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1).
The nomogram achieved high predictive accuracy with Areas Under the Curve (AUC) of 0.834 in the development group and 0.851 in the external validation group.
The nomogram demonstrated excellent calibration and clinical usefulness via DCA.

Conclusions:

The developed and validated nomogram is a reliable tool for predicting DR risk in T2DM patients.
Early identification of high-risk individuals enables timely clinical intervention.
This model holds significant potential for improving long-term visual health outcomes in diabetes patients.