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A prediction model for worsening diabetic retinopathy after panretinal photocoagulation

BACKGROUND: As one of the severe complications of diabetes mellitus, diabetic retinopathy (DR) is the leading cause of blindness in the working age worldwide. Although panretinal photocoagulation (PRP) was standard treatment, PRP-treated DR still has a high risk of progression. Hence, this study aim...

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Autores principales: Li, Jinglan, Li, Xuanlong, Lei, Mingxing, Li, Wanyue, Chen, Wenqian, Ma, Tianju, Gao, Yi, Ye, Zi, Li, Zhaohui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9419399/
https://www.ncbi.nlm.nih.gov/pubmed/36028852
http://dx.doi.org/10.1186/s13098-022-00892-z
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author Li, Jinglan
Li, Xuanlong
Lei, Mingxing
Li, Wanyue
Chen, Wenqian
Ma, Tianju
Gao, Yi
Ye, Zi
Li, Zhaohui
author_facet Li, Jinglan
Li, Xuanlong
Lei, Mingxing
Li, Wanyue
Chen, Wenqian
Ma, Tianju
Gao, Yi
Ye, Zi
Li, Zhaohui
author_sort Li, Jinglan
collection PubMed
description BACKGROUND: As one of the severe complications of diabetes mellitus, diabetic retinopathy (DR) is the leading cause of blindness in the working age worldwide. Although panretinal photocoagulation (PRP) was standard treatment, PRP-treated DR still has a high risk of progression. Hence, this study aimed to assess the risk factors and establish a model for predicting worsening diabetic retinopathy (DR-worsening) within five years after PRP. METHODS: Patients who were diagnosed with severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and treated with PRP were included, and those patients were randomly assigned to either a training or validation cohort. The multivariate logistic regression analysis was used to screen potential risk factors for DR-worsening in the training cohort. Then the model was established after including significant independent risk factors and further validated using discrimination and calibration. RESULTS: A total of 271 patients were included, and 56.46% of patients had an outcome of DR-worsening. In the training cohort (n = 135), age (odds ratio [OR] = 0.94, 95% confidence interval [CI] 0.90–0.98), baseline best corrected visual acuity (logMAR) (OR = 10.74, 95% CI 1.84–62.52), diabetic nephropathy (OR = 9.32, 95% CI 1.49–58.46), and hyperlipidemia (OR = 3.34, 95% CI 1.05–10.66) were screened out as the independent risk factors, which were incorporated into the predictive model. The area under the receiver operating characteristic curve and calibration slope in the training and validation cohort were 0.79, 0.96 (95% CI 0.60–1.31), and 0.79, 1.00 (95% CI 0.66–1.34), respectively. Two risk groups were developed depending on the best cut-off value of the predicted probability, and the actual probability was 34.90% and 82.79% in the low-risk and high-risk groups, respectively (P < 0.001). CONCLUSIONS: This study developed and internally validated a new model to predict the probability of DR-worsening after PRP treatment within five years. The model can be used as a rapid risk assessment system for clinical prediction of DR-worsening and identify individuals at a high risk of DR-worsening at an early stage and prescribe additional treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-022-00892-z.
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spelling pubmed-94193992022-08-28 A prediction model for worsening diabetic retinopathy after panretinal photocoagulation Li, Jinglan Li, Xuanlong Lei, Mingxing Li, Wanyue Chen, Wenqian Ma, Tianju Gao, Yi Ye, Zi Li, Zhaohui Diabetol Metab Syndr Research BACKGROUND: As one of the severe complications of diabetes mellitus, diabetic retinopathy (DR) is the leading cause of blindness in the working age worldwide. Although panretinal photocoagulation (PRP) was standard treatment, PRP-treated DR still has a high risk of progression. Hence, this study aimed to assess the risk factors and establish a model for predicting worsening diabetic retinopathy (DR-worsening) within five years after PRP. METHODS: Patients who were diagnosed with severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and treated with PRP were included, and those patients were randomly assigned to either a training or validation cohort. The multivariate logistic regression analysis was used to screen potential risk factors for DR-worsening in the training cohort. Then the model was established after including significant independent risk factors and further validated using discrimination and calibration. RESULTS: A total of 271 patients were included, and 56.46% of patients had an outcome of DR-worsening. In the training cohort (n = 135), age (odds ratio [OR] = 0.94, 95% confidence interval [CI] 0.90–0.98), baseline best corrected visual acuity (logMAR) (OR = 10.74, 95% CI 1.84–62.52), diabetic nephropathy (OR = 9.32, 95% CI 1.49–58.46), and hyperlipidemia (OR = 3.34, 95% CI 1.05–10.66) were screened out as the independent risk factors, which were incorporated into the predictive model. The area under the receiver operating characteristic curve and calibration slope in the training and validation cohort were 0.79, 0.96 (95% CI 0.60–1.31), and 0.79, 1.00 (95% CI 0.66–1.34), respectively. Two risk groups were developed depending on the best cut-off value of the predicted probability, and the actual probability was 34.90% and 82.79% in the low-risk and high-risk groups, respectively (P < 0.001). CONCLUSIONS: This study developed and internally validated a new model to predict the probability of DR-worsening after PRP treatment within five years. The model can be used as a rapid risk assessment system for clinical prediction of DR-worsening and identify individuals at a high risk of DR-worsening at an early stage and prescribe additional treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-022-00892-z. BioMed Central 2022-08-26 /pmc/articles/PMC9419399/ /pubmed/36028852 http://dx.doi.org/10.1186/s13098-022-00892-z 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
Li, Jinglan
Li, Xuanlong
Lei, Mingxing
Li, Wanyue
Chen, Wenqian
Ma, Tianju
Gao, Yi
Ye, Zi
Li, Zhaohui
A prediction model for worsening diabetic retinopathy after panretinal photocoagulation
title A prediction model for worsening diabetic retinopathy after panretinal photocoagulation
title_full A prediction model for worsening diabetic retinopathy after panretinal photocoagulation
title_fullStr A prediction model for worsening diabetic retinopathy after panretinal photocoagulation
title_full_unstemmed A prediction model for worsening diabetic retinopathy after panretinal photocoagulation
title_short A prediction model for worsening diabetic retinopathy after panretinal photocoagulation
title_sort prediction model for worsening diabetic retinopathy after panretinal photocoagulation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9419399/
https://www.ncbi.nlm.nih.gov/pubmed/36028852
http://dx.doi.org/10.1186/s13098-022-00892-z
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