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Modelling and developing diabetic retinopathy risk scores on Indian type 2 diabetes patients

  • Faiz Noor Khan Yusufi
  • Aquil Ahmed
  • Jamal Ahmad
Original Article

Abstract

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.

Keywords

Diabetic retinopathy Logistic regression Regularization Ridge regression Risk score Type 2 diabetes mellitus 

Abbreviations

BMI

Body mass index

BSF

Blood sugar fasting

DBP

Diastolic blood pressure

DR

Diabetic retinopathy

HbA1c

Glycosylated haemoglobin

HDL-C

HDL cholesterol

HTN

Hypertension

ICMR

Indian Council of Medical Research

LDL-C

LDL cholesterol

PP

Post prandial blood sugar

ROC

Receiver operating characteristic

SBP

Systolic blood pressure

T2DM

Type 2 diabetes mellitus (used only in tables)

TC

Total cholesterol

TG

Triglycerides

VLDL-C

VLDL cholesterol

Notes

Acknowledgements

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.

Authors’ contributions

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

Informed consent was obtained from all individual participants included in the study.

Ethical approval

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.

Supplementary material

13410_2018_652_MOESM1_ESM.docx (125 kb)
ESM 1 (DOCX 126 kb)

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Copyright information

© Research Society for Study of Diabetes in India 2018

Authors and Affiliations

  • Faiz Noor Khan Yusufi
    • 1
  • Aquil Ahmed
    • 1
  • Jamal Ahmad
    • 2
  1. 1.Department of Statistics & Operations ResearchAligarh Muslim UniversityAligarhIndia
  2. 2.Rajiv Gandhi Centre for Diabetes & Endocrinology, Jawaharlal Nehru Medical CollegeAligarh Muslim UniversityAligarhIndia

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