Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.

No studies have yet evaluated jointly central foveal thickness (CFT) and the presence of intraretinal fluid (PIF) to diagnose diabetic macular oedema (DMO) using optic coherence tomography (OCT). We performed a cross-sectional observational study to validate OCT for the diagnosis of DMO using both C...

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Authors: Hernández-Martínez C, Palazón-Bru A, Azrak C, Navarro-Navarro A, Baeza-Díaz MV, Martínez-Toldos JJ, Gil-Guillén VF
Format: article
Status:Published version
Publication Date:2015
Country:España
Institution:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
Repository:r-FISABIO. Repositorio Institucional de Producción Científica
OAI Identifier:oai:fisabio.fundanetsuite.com:p8999
Online Access:https://fisabio.portalinvestigacion.com/publicaciones/8999
Access Level:Open access
Keyword:Diabetes complications
Macular oedema
Optical coherence tomography
Vision screening
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spelling Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.Hernández-Martínez CPalazón-Bru AAzrak CNavarro-Navarro ABaeza-Díaz MVMartínez-Toldos JJGil-Guillén VFDiabetes complicationsMacular oedemaOptical coherence tomographyVision screeningNo studies have yet evaluated jointly central foveal thickness (CFT) and the presence of intraretinal fluid (PIF) to diagnose diabetic macular oedema (DMO) using optic coherence tomography (OCT). We performed a cross-sectional observational study to validate OCT for the diagnosis of DMO using both CFT and PIF assessed by OCT (3D OCT-1 Maestro). A sample of 277 eyes from primary care diabetic patients was assessed in a Spanish region in 2014. OUTCOME: DMO diagnosed by stereoscopic mydriatic fundoscopy. OCT was used to measure CFT and PIF. A binary logistic regression model was constructed to predict the outcome using CFT and PIF. The area under the ROC curve (AUC) of the model was calculated and non-linear equations used to determine which CFT values had a high probability of the outcome (positive test), distinguishing between the presence or absence of PIF. Calculations were made of the sensitivity, specificity, and the positive (PLR) and negative (NLR) likelihood ratios. The model was validated using bootstrapping methodology. A total of 37 eyes had DMO. AUC: 0.88. Positive test: CFT =90 µm plus PIF (=310 µm if no PIF). Clinical parameters: sensitivity, 0.83; specificity, 0.89; PLR, 7.34; NLR, 0.19. The parameters in the validation were similar. In conclusion, combining PIF and CFT provided a tool to very precisely discriminate the presence of DMO. Similar studies are needed to provide greater scientific evidence for the use of PIF in the diagnosis of DMO.PEERJ INC2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fisabio.portalinvestigacion.com/publicaciones/8999PeerJISSN: 21678359reponame:r-FISABIO. Repositorio Institucional de Producción Científicainstname:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)Inglésinfo:eu-repo/semantics/openAccessoai:fisabio.fundanetsuite.com:p89992026-06-11T12:45:17Z
dc.title.none.fl_str_mv Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
title Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
spellingShingle Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
Hernández-Martínez C
Diabetes complications
Macular oedema
Optical coherence tomography
Vision screening
title_short Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
title_full Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
title_fullStr Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
title_full_unstemmed Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
title_sort Detection of diabetic macular oedema: validation of optical coherence tomography using both foveal thickness and intraretinal fluid.
dc.creator.none.fl_str_mv Hernández-Martínez C
Palazón-Bru A
Azrak C
Navarro-Navarro A
Baeza-Díaz MV
Martínez-Toldos JJ
Gil-Guillén VF
author Hernández-Martínez C
author_facet Hernández-Martínez C
Palazón-Bru A
Azrak C
Navarro-Navarro A
Baeza-Díaz MV
Martínez-Toldos JJ
Gil-Guillén VF
author_role author
author2 Palazón-Bru A
Azrak C
Navarro-Navarro A
Baeza-Díaz MV
Martínez-Toldos JJ
Gil-Guillén VF
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Diabetes complications
Macular oedema
Optical coherence tomography
Vision screening
topic Diabetes complications
Macular oedema
Optical coherence tomography
Vision screening
description No studies have yet evaluated jointly central foveal thickness (CFT) and the presence of intraretinal fluid (PIF) to diagnose diabetic macular oedema (DMO) using optic coherence tomography (OCT). We performed a cross-sectional observational study to validate OCT for the diagnosis of DMO using both CFT and PIF assessed by OCT (3D OCT-1 Maestro). A sample of 277 eyes from primary care diabetic patients was assessed in a Spanish region in 2014. OUTCOME: DMO diagnosed by stereoscopic mydriatic fundoscopy. OCT was used to measure CFT and PIF. A binary logistic regression model was constructed to predict the outcome using CFT and PIF. The area under the ROC curve (AUC) of the model was calculated and non-linear equations used to determine which CFT values had a high probability of the outcome (positive test), distinguishing between the presence or absence of PIF. Calculations were made of the sensitivity, specificity, and the positive (PLR) and negative (NLR) likelihood ratios. The model was validated using bootstrapping methodology. A total of 37 eyes had DMO. AUC: 0.88. Positive test: CFT =90 µm plus PIF (=310 µm if no PIF). Clinical parameters: sensitivity, 0.83; specificity, 0.89; PLR, 7.34; NLR, 0.19. The parameters in the validation were similar. In conclusion, combining PIF and CFT provided a tool to very precisely discriminate the presence of DMO. Similar studies are needed to provide greater scientific evidence for the use of PIF in the diagnosis of DMO.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fisabio.portalinvestigacion.com/publicaciones/8999
url https://fisabio.portalinvestigacion.com/publicaciones/8999
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv PEERJ INC
publisher.none.fl_str_mv PEERJ INC
dc.source.none.fl_str_mv PeerJ
ISSN: 21678359
reponame:r-FISABIO. Repositorio Institucional de Producción Científica
instname:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
instname_str Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
reponame_str r-FISABIO. Repositorio Institucional de Producción Científica
collection r-FISABIO. Repositorio Institucional de Producción Científica
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