Frontiers in Endocrinology (Nov 2022)

Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model

  • Qian Wang,
  • Ni Zeng,
  • Hongbo Tang,
  • Xiaoxia Yang,
  • Qu Yao,
  • Lin Zhang,
  • Han Zhang,
  • Ying Zhang,
  • Xiaomei Nie,
  • Xin Liao,
  • Feng Jiang

DOI
https://doi.org/10.3389/fendo.2022.993423
Journal volume & issue
Vol. 13

Abstract

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BackgroundThis study aims to develop a diabetic retinopathy (DR) hazard nomogram for a Chinese population of patients with type 2 diabetes mellitus (T2DM).MethodsWe constructed a nomogram model by including data from 213 patients with T2DM between January 2019 and May 2021 in the Affiliated Hospital of Zunyi Medical University. We used basic statistics and biochemical indicator tests to assess the risk of DR in patients with T2DM. The patient data were used to evaluate the DR risk using R software and a least absolute shrinkage and selection operator (LASSO) predictive model. Using multivariable Cox regression, we examined the risk factors of DR to reduce the LASSO penalty. The validation model, decision curve analysis, and C-index were tested on the calibration plot. The bootstrapping methodology was used to internally validate the accuracy of the nomogram.ResultsThe LASSO algorithm identified the following eight predictive variables from the 16 independent variables: disease duration, body mass index (BMI), fasting blood glucose (FPG), glycated hemoglobin (HbA1c), homeostatic model assessment-insulin resistance (HOMA-IR), triglyceride (TG), total cholesterol (TC), and vitamin D (VitD)-T3. The C-index was 0.848 (95% CI: 0.798–0.898), indicating the accuracy of the model. In the interval validation, high scores (0.816) are possible from an analysis of a DR nomogram’s decision curve to predict DR.ConclusionWe developed a non-parametric technique to predict the risk of DR based on disease duration, BMI, FPG, HbA1c, HOMA-IR, TG, TC, and VitD.

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