The influence of lens position, vault prediction, and posterior cornea on phakic posterior chamber intraocular lens power.
ICL
IOL
IPCL
LION
Lens iterative optimization network
OCOS
Vault
Vault prediction
phakic intraocular lens
Journal
American journal of ophthalmology
ISSN: 1879-1891
Titre abrégé: Am J Ophthalmol
Pays: United States
ID NLM: 0370500
Informations de publication
Date de publication:
11 Jan 2024
11 Jan 2024
Historique:
received:
31
10
2023
revised:
04
01
2024
accepted:
05
01
2024
medline:
14
1
2024
pubmed:
14
1
2024
entrez:
13
1
2024
Statut:
aheadofprint
Résumé
Achieving precise refractive outcomes in phakic posterior chamber intraocular lens (pIOL) implantation is crucial for patient satisfaction. This study investigates factors affecting pIOL power calculations, focusing on myopic eyes, and evaluates the potential benefits of advanced predictive models. Retrospective, single-center, algorithm improvement study METHODS: Various variations with different effective lens position (ELP) algorithms were analyzed. The algorithms included a fixed constant model, and a multiple linear regression model and were tested with and without incorporation of the posterior corneal curvature (Rcp). Furthermore, the impact of inserting the postoperative vault, the space between the pIOL and the crystalline lens, into the ELP algorithm was examined, and a simple vault prediction model was assessed. Integrating Rcp and the measured vault into pIOL calculations did not significantly improve accuracy. Transitioning from constant model approaches to ELP concepts based on linear regression models significantly improved pIOL power calculations. Linear regression models outperformed constant models, enhancing refractive outcomes for both ICL and IPCL pIOL platforms. This study underscores the utility of implementing ELP concepts based on linear regression models into pIOL power calculation. Linear regression based ELP models offered substantial advantages for achieving desired refractive outcomes, especially in lower to medium power pIOL models. For pIOL power calculations in both pIOL platforms we tested with preoperative measurements from a Scheimpflug device, we found improved results with the LION 1
Sections du résumé
BACKGROUND
BACKGROUND
Achieving precise refractive outcomes in phakic posterior chamber intraocular lens (pIOL) implantation is crucial for patient satisfaction. This study investigates factors affecting pIOL power calculations, focusing on myopic eyes, and evaluates the potential benefits of advanced predictive models.
DESIGN
METHODS
Retrospective, single-center, algorithm improvement study METHODS: Various variations with different effective lens position (ELP) algorithms were analyzed. The algorithms included a fixed constant model, and a multiple linear regression model and were tested with and without incorporation of the posterior corneal curvature (Rcp). Furthermore, the impact of inserting the postoperative vault, the space between the pIOL and the crystalline lens, into the ELP algorithm was examined, and a simple vault prediction model was assessed.
RESULTS
RESULTS
Integrating Rcp and the measured vault into pIOL calculations did not significantly improve accuracy. Transitioning from constant model approaches to ELP concepts based on linear regression models significantly improved pIOL power calculations. Linear regression models outperformed constant models, enhancing refractive outcomes for both ICL and IPCL pIOL platforms.
CONCLUSIONS
CONCLUSIONS
This study underscores the utility of implementing ELP concepts based on linear regression models into pIOL power calculation. Linear regression based ELP models offered substantial advantages for achieving desired refractive outcomes, especially in lower to medium power pIOL models. For pIOL power calculations in both pIOL platforms we tested with preoperative measurements from a Scheimpflug device, we found improved results with the LION 1
Identifiants
pubmed: 38218514
pii: S0002-9394(24)00011-4
doi: 10.1016/j.ajo.2024.01.008
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declarations of competing interest Dr. Wendelstein reports research support from Carl Zeiss Meditec AG. He reports personal fees from Carl Zeiss Meditec AG, Alcon Surgical, Rayner Surgical, and Johnson & Johnson Vision outside of the submitted work. He was supported by an “ESCRS Peter Barry Fellowship Award”. Dr. Langenbucher reports personal fees from Hoya Surgical and Johnson & Johnson Vision outside the submitted work. Dr. Seiler reports personal fees and consultant functions from Glaucos, Schwind and Ziemer outside of the submitted work. Dr. Savini reports personal fees from Alcon, Carl Zeiss Meditec AG, Johnson & Johnson Vision, Oculus, SIFI and Staar Surgical outside the submitted work. Dr. Yeo reports personal fees from Alcon, Bausch and Lomb, Carl Zeiss Meditec, Kowa and Rayner outside of the submitted work. He is also a consultant for Bausch and Lomb and Carl Zeiss Meditec outside of the submitted work. Dr. Yeo licenses his EVO formula to Bausch and Lomb. Dr. Taneri received speaker's honoraria from AddVision. The remaining authors have no financial or proprietary interest in the materials presented herein.