Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders
Background/AimsTo compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders.MethodsThe Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 In...
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| Veröffentlicht in: | British journal of ophthalmology 2020-02, Vol.104 (2), p.282-290 |
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| creator | Chua, Jacqueline Schwarzhans, Florian Nguyen, Duc Quang Tham, Yih Chung Sia, Josh Tjunrong Lim, Claire Mathijia, Shivani Cheung, Carol Tin, Aung Fischer, Georg Cheng, Ching-Yu Vass, Clemens Schmetterer, Leopold |
| description | Background/AimsTo compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders.MethodsThe Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 Indians) with high-quality SD-OCT images from individuals without eye diseases were identified. Optic disc and macular cube scans were registered to determine the distance between fovea and optic disc centres (fovea distance) and their respective angle (fovea angle). Retinal vessels were segmented in the projection images and used to calculate the circumpapillary retinal vessel density profile. Compensated RNFL thickness was generated based on optic disc (ratio, orientation and area), fovea (distance and angle), retinal vessel density, refractive error and age. Linear regression models were used to investigate the effects of clinical factors on RNFL thickness.ResultsRetinal vessel density reduced significantly with increasing age (1487±214 µm in 40–49, 1458±208 µm in 50–59, 1429±223 µm in 60–69 and 1415±233 µm in ≥70). Compensation reduced the variability of RNFL thickness, where the effect was greatest for Chinese (10.9%; p |
| doi_str_mv | 10.1136/bjophthalmol-2019-314086 |
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| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7025730</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2348242425</sourcerecordid><originalsourceid>FETCH-LOGICAL-b492t-c4adecbedd3c4e7f66e9c15651edbfa151b1d86bb16d44f8cd99dbdf6818cf8b3</originalsourceid><addsrcrecordid>eNqNkUuLFDEUhQtRnJ7RvyABN25KcyvpVNVGkMYXDLjRdcjjpittVVImVQO9nV9umm6H0ZUkkOTmO4fcnKoiQN8CMPFOH-I8LIMapzjWDYW-ZsBpJ55UG-CiK6W2f1ptKKVtDSDgqrrO-VCOjYD2eXXFAKCDjm-q-12cZgxZLT4GEh1JuPigRhIw3SFxXickozpiIsvgzc-AORN1mrns0JI1-7AncV68KSoTB0wYDJIlTnGf1DwciVYnsNiroEr5AgYX12Ax5RfVM6fGjC8v603149PH77sv9e23z193H25rzftmqQ1XFo1Ga5nh2DohsDewFVtAq52CLWiwndAahOXcdcb2vdXWidKocZ1mN9X7s--86gmtwbAkNco5-Umlo4zKy79vgh_kPt7JljbbltFi8OZikOKvFfMiJ58NjqMKGNcsm4Y1tGeCtwV9_Q96iGsq_1ooxruGl7EtVHemTIo5J3QPjwEqT0HLx0HLU9DyHHSRvnrczIPwT7IFYGdAT4f_t_0N_ju-7g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2348242425</pqid></control><display><type>article</type><title>Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders</title><source>PubMed Central</source><creator>Chua, Jacqueline ; Schwarzhans, Florian ; Nguyen, Duc Quang ; Tham, Yih Chung ; Sia, Josh Tjunrong ; Lim, Claire ; Mathijia, Shivani ; Cheung, Carol ; Tin, Aung ; Fischer, Georg ; Cheng, Ching-Yu ; Vass, Clemens ; Schmetterer, Leopold</creator><creatorcontrib>Chua, Jacqueline ; Schwarzhans, Florian ; Nguyen, Duc Quang ; Tham, Yih Chung ; Sia, Josh Tjunrong ; Lim, Claire ; Mathijia, Shivani ; Cheung, Carol ; Tin, Aung ; Fischer, Georg ; Cheng, Ching-Yu ; Vass, Clemens ; Schmetterer, Leopold</creatorcontrib><description>Background/AimsTo compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders.MethodsThe Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 Indians) with high-quality SD-OCT images from individuals without eye diseases were identified. Optic disc and macular cube scans were registered to determine the distance between fovea and optic disc centres (fovea distance) and their respective angle (fovea angle). Retinal vessels were segmented in the projection images and used to calculate the circumpapillary retinal vessel density profile. Compensated RNFL thickness was generated based on optic disc (ratio, orientation and area), fovea (distance and angle), retinal vessel density, refractive error and age. Linear regression models were used to investigate the effects of clinical factors on RNFL thickness.ResultsRetinal vessel density reduced significantly with increasing age (1487±214 µm in 40–49, 1458±208 µm in 50–59, 1429±223 µm in 60–69 and 1415±233 µm in ≥70). Compensation reduced the variability of RNFL thickness, where the effect was greatest for Chinese (10.9%; p<0.001), followed by Malays (6.6%; p=0.075) and then Indians (4.3%; p=0.192). Compensation reduced the age-related RNFL decline by 55% in all participants (β=−3.32 µm vs β=−1.50 µm/10 years; p<0.001). Nearly 62% of the individuals who were initially classified as having abnormally thin RNFL (outside the 99% normal limits) were later reclassified as having normal RNFL.ConclusionsRNFL thickness compensated for anatomical parameters reduced the variability of measurements and may improve glaucoma detection, which needs to be confirmed in future studies.</description><identifier>ISSN: 0007-1161</identifier><identifier>EISSN: 1468-2079</identifier><identifier>DOI: 10.1136/bjophthalmol-2019-314086</identifier><identifier>PMID: 31118184</identifier><language>eng</language><publisher>England: BMJ Publishing Group LTD</publisher><subject>Age ; Blood pressure ; Clinical Science ; Diabetes ; Eye diseases ; Glaucoma ; Hypertension ; Optics ; Quality ; Regression analysis ; Tomography</subject><ispartof>British journal of ophthalmology, 2020-02, Vol.104 (2), p.282-290</ispartof><rights>Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.</rights><rights>2020 Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b492t-c4adecbedd3c4e7f66e9c15651edbfa151b1d86bb16d44f8cd99dbdf6818cf8b3</citedby><cites>FETCH-LOGICAL-b492t-c4adecbedd3c4e7f66e9c15651edbfa151b1d86bb16d44f8cd99dbdf6818cf8b3</cites><orcidid>0000-0002-9672-1819 ; 0000-0002-4916-6074 ; 0000-0002-7189-1707 ; 0000-0002-6474-5293</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025730/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025730/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31118184$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chua, Jacqueline</creatorcontrib><creatorcontrib>Schwarzhans, Florian</creatorcontrib><creatorcontrib>Nguyen, Duc Quang</creatorcontrib><creatorcontrib>Tham, Yih Chung</creatorcontrib><creatorcontrib>Sia, Josh Tjunrong</creatorcontrib><creatorcontrib>Lim, Claire</creatorcontrib><creatorcontrib>Mathijia, Shivani</creatorcontrib><creatorcontrib>Cheung, Carol</creatorcontrib><creatorcontrib>Tin, Aung</creatorcontrib><creatorcontrib>Fischer, Georg</creatorcontrib><creatorcontrib>Cheng, Ching-Yu</creatorcontrib><creatorcontrib>Vass, Clemens</creatorcontrib><creatorcontrib>Schmetterer, Leopold</creatorcontrib><title>Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders</title><title>British journal of ophthalmology</title><addtitle>Br J Ophthalmol</addtitle><description>Background/AimsTo compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders.MethodsThe Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 Indians) with high-quality SD-OCT images from individuals without eye diseases were identified. Optic disc and macular cube scans were registered to determine the distance between fovea and optic disc centres (fovea distance) and their respective angle (fovea angle). Retinal vessels were segmented in the projection images and used to calculate the circumpapillary retinal vessel density profile. Compensated RNFL thickness was generated based on optic disc (ratio, orientation and area), fovea (distance and angle), retinal vessel density, refractive error and age. Linear regression models were used to investigate the effects of clinical factors on RNFL thickness.ResultsRetinal vessel density reduced significantly with increasing age (1487±214 µm in 40–49, 1458±208 µm in 50–59, 1429±223 µm in 60–69 and 1415±233 µm in ≥70). Compensation reduced the variability of RNFL thickness, where the effect was greatest for Chinese (10.9%; p<0.001), followed by Malays (6.6%; p=0.075) and then Indians (4.3%; p=0.192). Compensation reduced the age-related RNFL decline by 55% in all participants (β=−3.32 µm vs β=−1.50 µm/10 years; p<0.001). Nearly 62% of the individuals who were initially classified as having abnormally thin RNFL (outside the 99% normal limits) were later reclassified as having normal RNFL.ConclusionsRNFL thickness compensated for anatomical parameters reduced the variability of measurements and may improve glaucoma detection, which needs to be confirmed in future studies.</description><subject>Age</subject><subject>Blood pressure</subject><subject>Clinical Science</subject><subject>Diabetes</subject><subject>Eye diseases</subject><subject>Glaucoma</subject><subject>Hypertension</subject><subject>Optics</subject><subject>Quality</subject><subject>Regression analysis</subject><subject>Tomography</subject><issn>0007-1161</issn><issn>1468-2079</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>9YT</sourceid><sourceid>ACMMV</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNqNkUuLFDEUhQtRnJ7RvyABN25KcyvpVNVGkMYXDLjRdcjjpittVVImVQO9nV9umm6H0ZUkkOTmO4fcnKoiQN8CMPFOH-I8LIMapzjWDYW-ZsBpJ55UG-CiK6W2f1ptKKVtDSDgqrrO-VCOjYD2eXXFAKCDjm-q-12cZgxZLT4GEh1JuPigRhIw3SFxXickozpiIsvgzc-AORN1mrns0JI1-7AncV68KSoTB0wYDJIlTnGf1DwciVYnsNiroEr5AgYX12Ax5RfVM6fGjC8v603149PH77sv9e23z193H25rzftmqQ1XFo1Ga5nh2DohsDewFVtAq52CLWiwndAahOXcdcb2vdXWidKocZ1mN9X7s--86gmtwbAkNco5-Umlo4zKy79vgh_kPt7JljbbltFi8OZikOKvFfMiJ58NjqMKGNcsm4Y1tGeCtwV9_Q96iGsq_1ooxruGl7EtVHemTIo5J3QPjwEqT0HLx0HLU9DyHHSRvnrczIPwT7IFYGdAT4f_t_0N_ju-7g</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Chua, Jacqueline</creator><creator>Schwarzhans, Florian</creator><creator>Nguyen, Duc Quang</creator><creator>Tham, Yih Chung</creator><creator>Sia, Josh Tjunrong</creator><creator>Lim, Claire</creator><creator>Mathijia, Shivani</creator><creator>Cheung, Carol</creator><creator>Tin, Aung</creator><creator>Fischer, Georg</creator><creator>Cheng, Ching-Yu</creator><creator>Vass, Clemens</creator><creator>Schmetterer, Leopold</creator><general>BMJ Publishing Group LTD</general><general>BMJ Publishing Group</general><scope>9YT</scope><scope>ACMMV</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BTHHO</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9672-1819</orcidid><orcidid>https://orcid.org/0000-0002-4916-6074</orcidid><orcidid>https://orcid.org/0000-0002-7189-1707</orcidid><orcidid>https://orcid.org/0000-0002-6474-5293</orcidid></search><sort><creationdate>20200201</creationdate><title>Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders</title><author>Chua, Jacqueline ; Schwarzhans, Florian ; Nguyen, Duc Quang ; Tham, Yih Chung ; Sia, Josh Tjunrong ; Lim, Claire ; Mathijia, Shivani ; Cheung, Carol ; Tin, Aung ; Fischer, Georg ; Cheng, Ching-Yu ; Vass, Clemens ; Schmetterer, Leopold</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b492t-c4adecbedd3c4e7f66e9c15651edbfa151b1d86bb16d44f8cd99dbdf6818cf8b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age</topic><topic>Blood pressure</topic><topic>Clinical Science</topic><topic>Diabetes</topic><topic>Eye diseases</topic><topic>Glaucoma</topic><topic>Hypertension</topic><topic>Optics</topic><topic>Quality</topic><topic>Regression analysis</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chua, Jacqueline</creatorcontrib><creatorcontrib>Schwarzhans, Florian</creatorcontrib><creatorcontrib>Nguyen, Duc Quang</creatorcontrib><creatorcontrib>Tham, Yih Chung</creatorcontrib><creatorcontrib>Sia, Josh Tjunrong</creatorcontrib><creatorcontrib>Lim, Claire</creatorcontrib><creatorcontrib>Mathijia, Shivani</creatorcontrib><creatorcontrib>Cheung, Carol</creatorcontrib><creatorcontrib>Tin, Aung</creatorcontrib><creatorcontrib>Fischer, Georg</creatorcontrib><creatorcontrib>Cheng, Ching-Yu</creatorcontrib><creatorcontrib>Vass, Clemens</creatorcontrib><creatorcontrib>Schmetterer, Leopold</creatorcontrib><collection>BMJ Open Access Journals</collection><collection>BMJ Journals:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>BMJ Journals</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>British journal of ophthalmology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chua, Jacqueline</au><au>Schwarzhans, Florian</au><au>Nguyen, Duc Quang</au><au>Tham, Yih Chung</au><au>Sia, Josh Tjunrong</au><au>Lim, Claire</au><au>Mathijia, Shivani</au><au>Cheung, Carol</au><au>Tin, Aung</au><au>Fischer, Georg</au><au>Cheng, Ching-Yu</au><au>Vass, Clemens</au><au>Schmetterer, Leopold</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders</atitle><jtitle>British journal of ophthalmology</jtitle><addtitle>Br J Ophthalmol</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>104</volume><issue>2</issue><spage>282</spage><epage>290</epage><pages>282-290</pages><issn>0007-1161</issn><eissn>1468-2079</eissn><abstract>Background/AimsTo compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders.MethodsThe Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 Indians) with high-quality SD-OCT images from individuals without eye diseases were identified. Optic disc and macular cube scans were registered to determine the distance between fovea and optic disc centres (fovea distance) and their respective angle (fovea angle). Retinal vessels were segmented in the projection images and used to calculate the circumpapillary retinal vessel density profile. Compensated RNFL thickness was generated based on optic disc (ratio, orientation and area), fovea (distance and angle), retinal vessel density, refractive error and age. Linear regression models were used to investigate the effects of clinical factors on RNFL thickness.ResultsRetinal vessel density reduced significantly with increasing age (1487±214 µm in 40–49, 1458±208 µm in 50–59, 1429±223 µm in 60–69 and 1415±233 µm in ≥70). Compensation reduced the variability of RNFL thickness, where the effect was greatest for Chinese (10.9%; p<0.001), followed by Malays (6.6%; p=0.075) and then Indians (4.3%; p=0.192). Compensation reduced the age-related RNFL decline by 55% in all participants (β=−3.32 µm vs β=−1.50 µm/10 years; p<0.001). Nearly 62% of the individuals who were initially classified as having abnormally thin RNFL (outside the 99% normal limits) were later reclassified as having normal RNFL.ConclusionsRNFL thickness compensated for anatomical parameters reduced the variability of measurements and may improve glaucoma detection, which needs to be confirmed in future studies.</abstract><cop>England</cop><pub>BMJ Publishing Group LTD</pub><pmid>31118184</pmid><doi>10.1136/bjophthalmol-2019-314086</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-9672-1819</orcidid><orcidid>https://orcid.org/0000-0002-4916-6074</orcidid><orcidid>https://orcid.org/0000-0002-7189-1707</orcidid><orcidid>https://orcid.org/0000-0002-6474-5293</orcidid><oa>free_for_read</oa></addata></record> |
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| source | PubMed Central |
| subjects | Age Blood pressure Clinical Science Diabetes Eye diseases Glaucoma Hypertension Optics Quality Regression analysis Tomography |
| title | Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders |
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