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Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length

Purpose: To predict the effective lens position (ELP) using conditional process analysis according to preoperative axial length. Setting: Yeouido St. Mary hospital. Design: A retrospective case series. Methods: This study included 621 eyes from 621 patients who underwent conventional cataract surger...

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Autores principales: Yoo, Young-Sik, Whang, Woong-Joo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950665/
https://www.ncbi.nlm.nih.gov/pubmed/35329795
http://dx.doi.org/10.3390/jcm11061469
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author Yoo, Young-Sik
Whang, Woong-Joo
author_facet Yoo, Young-Sik
Whang, Woong-Joo
author_sort Yoo, Young-Sik
collection PubMed
description Purpose: To predict the effective lens position (ELP) using conditional process analysis according to preoperative axial length. Setting: Yeouido St. Mary hospital. Design: A retrospective case series. Methods: This study included 621 eyes from 621 patients who underwent conventional cataract surgery at Yeouido St. Mary Hospital. Preoperative axial length (AL), mean corneal power (K), and anterior chamber depth (ACD) were measured by partial coherence interferometry. AL was used as an independent variable for the prediction of ELP, and 621 eyes were classified into four groups according to AL. Using conditional process analysis, we developed 24 structural equation models, with ACD and K acting as mediator, moderator or not included as variables, and investigated the model that best predicted ELP. Results: When AL was 23.0 mm or shorter, the predictability for ELP was highest when ACD and K acted as moderating variables (R2 = 0.217). When AL was between 23.0 mm and 24.5 mm or longer than 26.0 mm, the predictability was highest when K acted as a mediating variable and ACD acted as a moderating variable (R2 = 0.217 and R2 = 0.401). On the other hand, when AL ranged from 24.5 mm to 26.0 mm, the model with ACD as a mediating variable and K as a moderating variable was the most accurate (R2 = 0.220). Conclusions: The optimal structural equation model for ELP prediction in each group varied according to AL. Conditional process analysis can be an alternative to conventional multiple linear regression analysis in ELP prediction.
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spelling pubmed-89506652022-03-26 Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length Yoo, Young-Sik Whang, Woong-Joo J Clin Med Article Purpose: To predict the effective lens position (ELP) using conditional process analysis according to preoperative axial length. Setting: Yeouido St. Mary hospital. Design: A retrospective case series. Methods: This study included 621 eyes from 621 patients who underwent conventional cataract surgery at Yeouido St. Mary Hospital. Preoperative axial length (AL), mean corneal power (K), and anterior chamber depth (ACD) were measured by partial coherence interferometry. AL was used as an independent variable for the prediction of ELP, and 621 eyes were classified into four groups according to AL. Using conditional process analysis, we developed 24 structural equation models, with ACD and K acting as mediator, moderator or not included as variables, and investigated the model that best predicted ELP. Results: When AL was 23.0 mm or shorter, the predictability for ELP was highest when ACD and K acted as moderating variables (R2 = 0.217). When AL was between 23.0 mm and 24.5 mm or longer than 26.0 mm, the predictability was highest when K acted as a mediating variable and ACD acted as a moderating variable (R2 = 0.217 and R2 = 0.401). On the other hand, when AL ranged from 24.5 mm to 26.0 mm, the model with ACD as a mediating variable and K as a moderating variable was the most accurate (R2 = 0.220). Conclusions: The optimal structural equation model for ELP prediction in each group varied according to AL. Conditional process analysis can be an alternative to conventional multiple linear regression analysis in ELP prediction. MDPI 2022-03-08 /pmc/articles/PMC8950665/ /pubmed/35329795 http://dx.doi.org/10.3390/jcm11061469 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yoo, Young-Sik
Whang, Woong-Joo
Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length
title Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length
title_full Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length
title_fullStr Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length
title_full_unstemmed Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length
title_short Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length
title_sort conditional process analysis for effective lens position according to preoperative axial length
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950665/
https://www.ncbi.nlm.nih.gov/pubmed/35329795
http://dx.doi.org/10.3390/jcm11061469
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