ArticleJournal of clinical medicine2022
Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Influence of Ocular Biometric Parameters on IOL Power Calculation Accuracy in Short and Normal Axial Length Eyes.Ophthalmology and therapy · 2026Article
- Secondary Trifocal Intraocular Lens Implantation in Dense Cataracts: A Promising Alternative to One-Step Surgery.Ophthalmology and therapy · 2025Article
- Updated study on demographic and ocular biometric characteristics of cataract patients indicates new trends in cataract surgery.Scientific reports · 2025Article
- Statistical power analysis and sample size planning for moderated mediation models.Behavior research methods · 2024Article
- Modified intraocular lens power selection method according to biometric subgroups Eom IOL power calculator.Scientific reports · 2024Article
- Research progress on prediction of postoperative intraocular lens position.Indian journal of ophthalmology · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo predict the effective lens position (ELP) using conditional process analysis according to preoperative axial length.
settingYeouido St. Mary hospital.
designA retrospective case series.
methodsThis 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.
resultsWhen 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).
conclusionsThe 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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