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Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?

BACKGROUND: Hemoglobin A1C (HbA(1)C) test is the best care evaluation measurement due to a strong correlation between the test results and diabetic complications. So, this cross-sectional study aimed to assess whether the level of HbA(1)C can predict Diabetic Retinopathy (DR) among Type 2 diabetes m...

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Autores principales: Setareh, Javad, Hoseinzade, Ghazale, Khoundabi, Batoul, Kamali, Mahsa, Ebrahimi, Ata, Fazlollahpour-Naghibi, Andarz, Zareei, Mohammad, Mohamaditabar, Mona, Makaremi, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620629/
https://www.ncbi.nlm.nih.gov/pubmed/36316667
http://dx.doi.org/10.1186/s12886-022-02608-3
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author Setareh, Javad
Hoseinzade, Ghazale
Khoundabi, Batoul
Kamali, Mahsa
Ebrahimi, Ata
Fazlollahpour-Naghibi, Andarz
Zareei, Mohammad
Mohamaditabar, Mona
Makaremi, Ali
author_facet Setareh, Javad
Hoseinzade, Ghazale
Khoundabi, Batoul
Kamali, Mahsa
Ebrahimi, Ata
Fazlollahpour-Naghibi, Andarz
Zareei, Mohammad
Mohamaditabar, Mona
Makaremi, Ali
author_sort Setareh, Javad
collection PubMed
description BACKGROUND: Hemoglobin A1C (HbA(1)C) test is the best care evaluation measurement due to a strong correlation between the test results and diabetic complications. So, this cross-sectional study aimed to assess whether the level of HbA(1)C can predict Diabetic Retinopathy (DR) among Type 2 diabetes mellitus (T2DM) in the Iranian population. METHOD: One hundred sixty-eight diabetic patients were selected via the convenience sampling method. Data were collected by research made questionnaire scale and laboratory test had been done. To estimate the cut off point for some variables statistical tests, formal measures of classification performance, model evaluation criteria and a decision Tree were used. RESULTS: The prevalence of DR was 29.8%. The Receiver Operating Characteristic (ROC) curve and decision tree showed the optimal cut-off point for the HbA1C variable that separates the patient with and without DR is HbA(1)C = 8.15. CONCLUSION: Current study showed an appropriate cutoff point for detecting the development of DR among diabetic patients. So, this cutoff point can be used as guide evidence in several clinical judgments on the Iranian population.
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spelling pubmed-96206292022-11-01 Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients? Setareh, Javad Hoseinzade, Ghazale Khoundabi, Batoul Kamali, Mahsa Ebrahimi, Ata Fazlollahpour-Naghibi, Andarz Zareei, Mohammad Mohamaditabar, Mona Makaremi, Ali BMC Ophthalmol Research BACKGROUND: Hemoglobin A1C (HbA(1)C) test is the best care evaluation measurement due to a strong correlation between the test results and diabetic complications. So, this cross-sectional study aimed to assess whether the level of HbA(1)C can predict Diabetic Retinopathy (DR) among Type 2 diabetes mellitus (T2DM) in the Iranian population. METHOD: One hundred sixty-eight diabetic patients were selected via the convenience sampling method. Data were collected by research made questionnaire scale and laboratory test had been done. To estimate the cut off point for some variables statistical tests, formal measures of classification performance, model evaluation criteria and a decision Tree were used. RESULTS: The prevalence of DR was 29.8%. The Receiver Operating Characteristic (ROC) curve and decision tree showed the optimal cut-off point for the HbA1C variable that separates the patient with and without DR is HbA(1)C = 8.15. CONCLUSION: Current study showed an appropriate cutoff point for detecting the development of DR among diabetic patients. So, this cutoff point can be used as guide evidence in several clinical judgments on the Iranian population. BioMed Central 2022-10-31 /pmc/articles/PMC9620629/ /pubmed/36316667 http://dx.doi.org/10.1186/s12886-022-02608-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Setareh, Javad
Hoseinzade, Ghazale
Khoundabi, Batoul
Kamali, Mahsa
Ebrahimi, Ata
Fazlollahpour-Naghibi, Andarz
Zareei, Mohammad
Mohamaditabar, Mona
Makaremi, Ali
Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?
title Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?
title_full Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?
title_fullStr Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?
title_full_unstemmed Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?
title_short Can the level of HbA(1)C predict diabetic retinopathy among type II diabetic patients?
title_sort can the level of hba(1)c predict diabetic retinopathy among type ii diabetic patients?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620629/
https://www.ncbi.nlm.nih.gov/pubmed/36316667
http://dx.doi.org/10.1186/s12886-022-02608-3
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