Risk prediction models of retinopathy in type 2 diabetes patients: a systematic review
10.3760/cma.j.cn115682-20230615-02403
- VernacularTitle:2型糖尿病患者发生视网膜病变风险预测模型的系统评价
- Author:
Xuhui DONG
1
;
Defeng CHEN
;
Bei LI
;
Wanlin PENG
Author Information
1. 广西中医药大学护理学院，南宁 530001
- Keywords:
Diabetes mellitus, type 2;
Diabetic retinopathy;
Predictive model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2024;30(5):644-650
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review the risk prediction model of retinopathy in type 2 diabetes patients.Methods:Research on the prediction model of retinopathy in patients with type 2 diabetes was retrieved in PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, WanFang Data, VIP, and China Biology Medicine. The search period was from database establishment to March 17, 2023. Two researchers independently screened the literature and extracted data, and used the prediction model risk of bias assessment tool to analyze the bias risk and applicability of the included literature.Results:A total of 18 articles were included, of which nine studies used Logistic regression, four studies used Cox proportional hazards regression, two studies used Lasso regression, one study used semi-parametric regression, and two studies used machine learning methods. Two studies simultaneously conducted internal and external validation, three studies conducted internal validation, and one study conducted external validation. A total of 12 studies mentioned that the area under the curve ( AUC) of predictive models ranged from 0.715 to 0.966, and all AUCs were greater than 0.7. Four studies mentioned the C index, which was 0.770 to 0.848, while two studies did not mention the predictive performance of the model. Age, course of type 2 diabetes, urinary protein, glycosylated hemoglobin, insulin use, hypertension, and time of hospital admissions were independent predictors of repeated reporting in the multivariate model. All studies had a certain risk of bias, but their applicability was good. Conclusions:The existing prediction models for retinopathy in type 2 diabetes patients have good prediction performance, but the overall risk of bias is high. Further validation of the performance of each model is needed, while further development of risk prediction models suitable for different populations (such as elderly patients, women, different races or regions) is still needed.
