Prediction of retinopathy risk: A prospective cohort study in China

IF 3.4 Q1 ENDOCRINOLOGY & METABOLISM
Xiaohan Xu , Duolao Wang , Uazman Alam , Shanhu Qiu , Yuzhi Ding , Zilin Sun , Anupam Garrib
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引用次数: 0

Abstract

Aim

To identify risk factors for retinopathy and to develop a nomogram for individualised risk prediction in a multi-ethnic Chinese cohort.

Methods

Data were derived from the SENSIBLE-Cohort, excluding participants with retinopathy at baseline. Two nomograms were constructed: one using baseline data only (Baseline), and one incorporating baseline and follow-up data (Combination). Predictor selection involved Cox regression, Boruta, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE). Model performance was evaluated using Harrell's C-index, confusion matrix, and Brier Score. The receiver operating characteristic (ROC) curves, the area under the ROC curve (AUC), the DeLong test, and the decision curve analysis (DCA) were used for comparative assessment.

Results

A total of 2,447 participants were included (mean age: 53.0 ± 8.6 years; 66.1 % female; BMI: 25.4 ± 3.5 kg/m2), including 1,380 with normal glucose tolerance, 762 with prediabetes, and 305 with diabetes. During follow-up, 144 (5.9 %) people developed retinopathy. Key predictors included BMI, waist-to-hip ratio, triglycerides, systolic and diastolic blood pressure, hypertension history, and ethnicity.
The Combination nomogram showed superior discrimination compared to the Baseline nomogram (AUC: 0.75 vs. 0.64, P < 0.001) and demonstrated balanced sensitivity and specificity. DCA demonstrated greater clinical utility of the Combination nomogram across a range of risk thresholds.

Conclusion

The Combination nomogram enables early retinopathy risk stratification using accessible clinical data. It may support personalised screening and introduces the broader concept of metabolic retinopathy.
视网膜病变风险预测:中国的一项前瞻性队列研究
目的确定中国多民族人群视网膜病变的危险因素,并建立个体化风险预测的nomogram。方法数据来源于sensible队列,排除基线时患有视网膜病变的参与者。构建了两个nomogram:一个仅使用基线数据(baseline),另一个包含基线和随访数据(Combination)。预测器选择包括Cox回归、Boruta、最小绝对收缩和选择算子(LASSO)和递归特征消除(RFE)。采用Harrell’s c指数、混淆矩阵和Brier评分对模型性能进行评估。采用受试者工作特征(ROC)曲线、ROC曲线下面积(AUC)、DeLong检验和决策曲线分析(DCA)进行比较评价。结果共纳入2447例受试者(平均年龄:53.0±8.6岁;女性占66.1%;BMI: 25.4±3.5 kg/m2),其中糖耐量正常1380人,糖尿病前期762人,糖尿病305人。随访期间,144人(5.9%)发生视网膜病变。主要预测因素包括BMI、腰臀比、甘油三酯、收缩压和舒张压、高血压史和种族。与基线nomogram (AUC: 0.75 vs. 0.64, P <;0.001),并表现出平衡的敏感性和特异性。DCA在一系列风险阈值范围内显示了更大的联合nomogram临床应用。结论联合nomogram视网膜病变风险分层方法可用于早期视网膜病变风险分层。它可能支持个性化筛查,并引入更广泛的代谢性视网膜病变概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
22.90
自引率
2.00%
发文量
248
审稿时长
51 days
期刊介绍: Diabetes and Metabolic Syndrome: Clinical Research and Reviews is the official journal of DiabetesIndia. It aims to provide a global platform for healthcare professionals, diabetes educators, and other stakeholders to submit their research on diabetes care. Types of Publications: Diabetes and Metabolic Syndrome: Clinical Research and Reviews publishes peer-reviewed original articles, reviews, short communications, case reports, letters to the Editor, and expert comments. Reviews and mini-reviews are particularly welcomed for areas within endocrinology undergoing rapid changes.
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