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

作者全名："Yang, Yanzhi; Tan, Juntao; He, Yuxin; Huang, Huanhuan; Wang, Tingting; Gong, Jun; Liu, Yunyu; Zhang, Qin; Xu, Xiaomei"

作者地址："[Yang, Yanzhi; Zhang, Qin] Chengdu First Peoples Hosp, Dept Endocrinol & Metab, Chengdu, Peoples R China; [Tan, Juntao] Chongqing Med Univ, Operat Management Off, Affiliated Banan Hosp, Chongqing, Peoples R China; [He, Yuxin] Chongqing Med Univ, Dept Med Adm, Affiliated Banan Hosp, Chongqing, Peoples R China; [Huang, Huanhuan] Chongqing Med Univ, Dept Nursing, Affiliated Hosp 1, Chongqing, Peoples R China; [Wang, Tingting] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Gong, Jun] Chongqing Med Univ, Dept Informat Ctr, Univ Town Hosp, Chongqing, Peoples R China; [Liu, Yunyu] Chongqing Med Univ, Med Records Dept, Affiliated Hosp 2, Chongqing, Peoples R China; [Xu, Xiaomei] Chongqing Med Univ, Dept Infect Dis, Affiliated Hosp 1, Chongqing, Peoples R China; [Xu, Xiaomei] Chengdu Fifth Peoples Hosp, Dept Gastroenterol, Chengdu, Peoples R China"

通信作者："Zhang, Q (通讯作者)，Chengdu First Peoples Hosp, Dept Endocrinol & Metab, Chengdu, Peoples R China.; Xu, XM (通讯作者)，Chongqing Med Univ, Dept Infect Dis, Affiliated Hosp 1, Chongqing, Peoples R China.; Xu, XM (通讯作者)，Chengdu Fifth Peoples Hosp, Dept Gastroenterol, Chengdu, Peoples R China."

来源：FRONTIERS IN ENDOCRINOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000913454000001

JCR分区：Q2

影响因子：5.2

年份：2023

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：diabetes mellitus; diabetic retinopathy; predictive model; medically underserved settings; webpage

摘要："BackgroundComprehensive 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. MethodsClinical 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). ResultsThe 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. ConclusionsThe 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."

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