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

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...

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Autores principales: Romero-Aroca, Pedro, Baget-Bernaldiz, Marc, Navarro-Gil, Raul, Feliu, Albert, Maarof, Najla, Moreno, Antonio, Cristiano, Julian, Valls, Aida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921832/
https://www.ncbi.nlm.nih.gov/pubmed/35300029
http://dx.doi.org/10.2147/OPTH.S351790
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author Romero-Aroca, Pedro
Baget-Bernaldiz, Marc
Navarro-Gil, Raul
Feliu, Albert
Maarof, Najla
Moreno, Antonio
Cristiano, Julian
Valls, Aida
author_facet Romero-Aroca, Pedro
Baget-Bernaldiz, Marc
Navarro-Gil, Raul
Feliu, Albert
Maarof, Najla
Moreno, Antonio
Cristiano, Julian
Valls, Aida
author_sort Romero-Aroca, Pedro
collection PubMed
description 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.
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spelling pubmed-89218322022-03-16 Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population Romero-Aroca, Pedro Baget-Bernaldiz, Marc Navarro-Gil, Raul Feliu, Albert Maarof, Najla Moreno, Antonio Cristiano, Julian Valls, Aida Clin Ophthalmol Original Research 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. Dove 2022-03-10 /pmc/articles/PMC8921832/ /pubmed/35300029 http://dx.doi.org/10.2147/OPTH.S351790 Text en © 2022 Romero-Aroca et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Romero-Aroca, Pedro
Baget-Bernaldiz, Marc
Navarro-Gil, Raul
Feliu, Albert
Maarof, Najla
Moreno, Antonio
Cristiano, Julian
Valls, Aida
Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population
title Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population
title_full Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population
title_fullStr Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population
title_full_unstemmed Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population
title_short Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population
title_sort validation of an algorithm for the prediction of diabetic retinopathy in type 1 diabetic patients in a spanish population
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921832/
https://www.ncbi.nlm.nih.gov/pubmed/35300029
http://dx.doi.org/10.2147/OPTH.S351790
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