Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

Yanzhi Yang; Juntao Tan; Yuxin He; Huanhuan Huang; Tingting Wang; Jun Gong; Yunyu Liu; Qin Zhang; Xiaomei Xu

doi:10.3389/fendo.2022.1099302

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

Front Endocrinol (Lausanne). 2023 Jan 5:13:1099302. doi: 10.3389/fendo.2022.1099302. eCollection 2022.

Authors

Yanzhi Yang¹, Juntao Tan², Yuxin He³, Huanhuan Huang⁴, Tingting Wang⁵, Jun Gong⁶, Yunyu Liu⁷, Qin Zhang¹, Xiaomei Xu^{8

9}

Affiliations

¹ Department of Endocrinology and Metabolism, Chengdu First People's Hospital, Chengdu, China.
² Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
³ Department of Medical Administration, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
⁴ Department of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁵ College of Medical Informatics, Chongqing Medical University, Chongqing, China.
⁶ Department of Information Center, The University Town Hospital of Chongqing Medical University, Chongqing, China.
⁷ Medical Records Department, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁸ Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁹ Department of Gastroenterology, Chengdu Fifth People's hospital, Chengdu, China.

Abstract

Background: Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.

Methods: Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Results: The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.

Conclusions: The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.

Keywords: diabetes mellitus; diabetic retinopathy; medically underserved settings; predictive model; webpage.

Publication types

Multicenter Study
Research Support, Non-U.S. Gov't

MeSH terms

Area Under Curve
Calibration
Diabetes Mellitus*
Diabetic Retinopathy* / diagnosis
Humans
Retinal Diseases*
Retrospective Studies