Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population

Pedro Romero-Aroca; Marc Baget-Bernaldiz; Raul Navarro-Gil; Albert Feliu; Najla Maarof; Antonio Moreno; Julian Cristiano; Aida Valls

doi:10.2147/OPTH.S351790

Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population

Clin Ophthalmol. 2022 Mar 10:16:715-722. doi: 10.2147/OPTH.S351790. eCollection 2022.

Authors

Pedro Romero-Aroca¹, Marc Baget-Bernaldiz¹, Raul Navarro-Gil¹, Albert Feliu², Najla Maarof³, Antonio Moreno³, Julian Cristiano³, Aida Valls³

Affiliations

¹ Ophthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain.
² Pediatric Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain.
³ Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain.

Abstract

Aim: The aim of the present study was to build a clinical decision support system (CDSS) that can predict the presence of diabetic retinopathy (DR) in type 1 diabetes (T1DM) patients.

Material and method: We built two versions of our CDSS to predict the presence of any-type DR and sight-threatening DR (STDR) in T1DM patients. The first version was trained using 324 T1DM and 826 T2DM patients. The second was trained with only the 324 T1DM patients.

Results: The first version achieved an accuracy (ACC) = 0.795, specificity (SP) = 83%, and sensitivity (S) = 65.7% to predict the presence of any-DR, and an ACC = 0.918, SP = 87.1% and S = 87.8% for STDR. The second model achieved ACC = 0.799, SP = 87.5% and S = 86.3% when predicting any-DR and ACC = 0.937, SP = 90.9% and S = 83.0% for STDR.

Conclusion: The two models better predict STDR than any-DR in T1DM patients. We will need a larger sample to strengthen our results.

Keywords: clinical decision support system; diabetic retinopathy; type 1 diabetes mellitus.

Grants and funding

This research was funded by Instituto de Salud Carlos III SPAIN, grant number: FIS projects PI18/00169 and European Regional Development fund (FEDER) European Union.