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Classification of Tear Film Lipid Layer En Face Maps Obtained Using Optical Coherence Tomography and Their Correlation With Clinical Parameters

The purpose of this study was to investigate the correlation between the pattern of optical coherence tomography (OCT) en face maps of the tear film lipid layer (TFLL) and lipid layer thickness (LLT), fluorescein breakup time (FBUT), and Schirmer I test values in healthy subjects. METHODS: Measureme...

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Detalles Bibliográficos
Autores principales: Stegmann, Hannes, Aranha Dos Santos, Valentin, Schmidl, Doreen, Garhöfer, Gerhard, Fard, Ali, Bagherinia, Homayoun, Schmetterer, Leopold, Werkmeister, René M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornea 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9973450/
https://www.ncbi.nlm.nih.gov/pubmed/36730374
http://dx.doi.org/10.1097/ICO.0000000000003172
Descripción
Sumario:The purpose of this study was to investigate the correlation between the pattern of optical coherence tomography (OCT) en face maps of the tear film lipid layer (TFLL) and lipid layer thickness (LLT), fluorescein breakup time (FBUT), and Schirmer I test values in healthy subjects. METHODS: Measurements from four clinical data sets were retrospectively analyzed, and TFLL patterns were classified into 3 categories: homogeneous (HOM), wavy (WAV), or dotted (DOT) appearance. Linear mixed model analyses were performed. Intraclass correlation coefficients and index of qualitative variation were computed to investigate interrater and intrasubject variabilities. RESULTS: For the LLT, a significant difference between HOM and DOT (P < 0.001, β(HOMvsDOT) = −6.42 nm) and WAV and DOT (P = 0.002, β(WAVvsDOT) = −4.04 nm) was found. Furthermore, the difference between WAV and DOT regarding FBUT (P < 0.001, β(WAVvsDOT) = −3.065 seconds) was significant, while no significant differences between any of the classes with respect to the Schirmer I test values were found. An intraclass correlation coefficient of 89.0% reveals a good interrater reliability, and an index of qualitative variation of 60.0% shows, on average, a considerable variability in TFLL pattern class for repeated measurements over 1 hour. CONCLUSIONS: A new classification method for OCT en face maps of the TFLL is presented. Significant differences between patterns were found with respect to LLT and FBUT. A dotted pattern on dark background appears to be the most stable type of TFLL. The analysis of OCT en face maps of the TFLL provides complimentary information to conventional imaging methods and might give new insights into the characteristics of the TFLL.