Prediction of diabetic retinopathy among type 2 diabetic patients in University of Gondar comprehensive specialized hospital, 2006–2021: A prognostic model

糖尿病性视网膜病变 医学 糖尿病 大学医院 视网膜病变 2型糖尿病 内科学 重症监护医学 验光服务 内分泌学
作者
Tsion Mulat Tebeje,Melaku Kindie Yenit,Solomon Gedlu Nigatu,Segenet Bizuneh Mengistu,Tigabu Kidie Tesfie,Negalgn Byadgie Gelaw,Yazachew Moges Chekol
出处
期刊:International Journal of Medical Informatics [Elsevier BV]
卷期号:190: 105536-105536
标识
DOI:10.1016/j.ijmedinf.2024.105536
摘要

There has been a paucity of evidence for the development of a prediction model for diabetic retinopathy (DR) in Ethiopia. Predicting the risk of developing DR based on the patient's demographic, clinical, and behavioral data is helpful in resource-limited areas where regular screening for DR is not available and to guide practitioners estimate the future risk of their patients. A retrospective follow-up study was conducted at the University of Gondar (UoG) Comprehensive Specialized Hospital from January 2006 to May 2021 among 856 patients with type 2 diabetes (T2DM). Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The data were validated by 10-fold cross-validation. Four ML techniques (naïve Bayes, K-nearest neighbor, decision tree, and logistic regression) were employed. The performance of each algorithm was measured, and logistic regression was a well-performing algorithm. After multivariable logistic regression and model reduction, a nomogram was developed to predict the individual risk of DR. Logistic regression was the best algorithm for predicting DR with an area under the curve of 92%, sensitivity of 87%, specificity of 83%, precision of 84%, F1-score of 85%, and accuracy of 85%. The logistic regression model selected seven predictors: total cholesterol, duration of diabetes, glycemic control, adherence to anti-diabetic medications, other microvascular complications of diabetes, sex, and hypertension. A nomogram was developed and deployed as a web-based application. A decision curve analysis showed that the model was useful in clinical practice and was better than treating all or none of the patients. The model has excellent performance and a better net benefit to be utilized in clinical practice to show the future probability of having DR. Identifying those with a higher risk of DR helps in the early identification and intervention of DR.
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