Modelling and developing diabetic retinopathy risk scores on Indian type 2 diabetes patients
- 22 Downloads
The objective was to develop diabetic retinopathy (DR) risk scores and compute prevalence and incidence probabilities of DR in Indian type 2 diabetes mellitus patients. A double sample of size 388 was collected from the R.G. Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India, randomly distributed among training and test sets. DR risk scores of Iran and China were administered on Indian training set. Since prevalence probabilities of DR calculated by Logit model were unacceptable, thus actual data of Iranian and Chinese studies were simulated from their variable characteristics. Ridge regression was selected as optimal by regularization and cross-validation techniques. The yearly incidences of DR from ridge probabilities were determined using absorbing Markov chain. Receiver operating characteristic (ROC) curve and Hosmer Lemeshow test were exerted for model discrimination and calibration. Furthermore, these outcomes were implemented on the test sample. Out of 284 training sample patients, 23 had DR currently. Iranian score with an area of 0.815 (95% CI 0.765–0.859) was the better fit. Ridge coefficients acquired from Chinese simulated data contented the Indian data, providing accurate probabilities and an area of 0.784 (95% CI 0.731–0.830). Validating on test data, ROC curves for current, 1 year and 2 years prediction resulted in areas of 0.819, 0.811 and 0.686. Iranian score and simulated Chinese ridge coefficients for prevalence of DR were the best fit on Indian type 2 diabetes patients. Markov two-state model can be applied to forecast yearly incidence of DR.
KeywordsDiabetic retinopathy Logistic regression Regularization Ridge regression Risk score Type 2 diabetes mellitus
Body mass index
Blood sugar fasting
Diastolic blood pressure
Indian Council of Medical Research
Post prandial blood sugar
Receiver operating characteristic
Systolic blood pressure
Type 2 diabetes mellitus (used only in tables)
We thank the Director of Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U for allowing us to collect and use the data.
Declaration of funding
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Faiz N.K. Yusufi carried out the collection of data, all data analysis and prepared the manuscript and takes responsibility as the guarantor.
Aquil Ahmed provided assistance in preparing the manuscript.
Jamal Ahmad supervised the collection of data and participated in manuscript preparation.
All authors have read and approved the content of the manuscript.
Compliance with ethical standards
Conflict of interest
Faiz Noor Khan Yusufi, Aquil Ahmed and Jamal Ahmad declare that they have no conflict of interest.
Informed consent was obtained from all individual participants included in the study.
The patients’ written consent was acquired in addition to an ethical approval through the Institutional Ethical Committee, Faculty of Medicine, J.N.M.C., A.M.U., Aligarh.
- 1.Rajput R, Kumar KM, Seshadri, K et al. Prevalence of chronic kidney disease (CKD) in type 2 diabetes mellitus patients: START-India study. J Diabetes Metab 2017; 8(2).Google Scholar
- 5.New Indian Express Indians with diabetes may number 120 million in 20 years: Indian Institute of Public Health 2017; Available at http://www.newindianexpress.com/nation/2017/apr/06/indians-with-diabetes-may-number-120-million-in-20-years-indian-institute-of-public-health-1590602.html. Accessed 23 June 2017.
- 6.Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian diabetes score for screening for undiagnosed diabetic subjects. (CURES-24). J Assoc Physicians India. 2005;53:759–63.Google Scholar
- 10.Liew G, Wong TY, Mitchell P, et al. Retinopathy predicts coronary heart disease mortality. Heart. 2009;95(5):3910–394.Google Scholar
- 11.Hosseini SM, Maracy MR, Amini M, et al. A risk score development for diabetic retinopathy screening in Isfahan-Iran. J Res Med Sci. 2009;14(2):105.Google Scholar
- 14.Shanmugasundaram D, Jeyaseelan L, George S, et al. Analysis strategy for comparison of skewed outcomes from biological data: a recent development. Ann Biol Res. 2014;5(12):16–20.Google Scholar
- 16.Jabbar HK, Khan DRZ. Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study). Comp Sci Comm Instr Dev 2014; ISBN: 978-981-09-5247-1: 163–172.Google Scholar
- 21.Goeman J, Meijer R, Chaturvedi N. L1 and L2 penalized regression models. R Foundation for Statistical Computing. 2016; Available at https://cran.r-project.org/web/packages/penalized/vignettes/penalized.pdf. Accessed 23 June 2017.
- 22.ICMR. Executive Summary: ICMR- YDR Registry (Phase-1) Report. 2014; Available at http://www.icmr.nic.in/final/diabetes/Executive%20summary%20YDR%2025082014.pdf. Accessed 23 June 2017.