Evidence map›Paper›PMID 38837172›Full record

ArticleTranslational vision science & technology2024

A New Method to Minimize the Standard Deviation and Root Mean Square of the Prediction Error of Single-Optimized IOL Power Formulas.

Damien Gatinel, Guillaume Debellemanière, Alain Saad, Radhika Rampat, Avi Wallerstein, Mathieu Gauvin, Jacques Malet

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Article in Translational vision science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Damien GatinelRothschild Foundation Hospital, Anterior Segment and Refractive Surgery Department, Paris, France.
Guillaume DebellemanièreRothschild Foundation Hospital, Anterior Segment and Refractive Surgery Department, Paris, France.
Alain SaadRothschild Foundation Hospital, Anterior Segment and Refractive Surgery Department, Paris, France.
Radhika RampatRothschild Foundation Hospital, Anterior Segment and Refractive Surgery Department, Paris, France.
Avi WallersteinDepartment of Ophthalmology and Visual Sciences, McGill University, Montréal, Quebec, Canada.
Mathieu GauvinDepartment of Ophthalmology and Visual Sciences, McGill University, Montréal, Quebec, Canada.
Jacques MaletRothschild Foundation Hospital, Anterior Segment and Refractive Surgery Department, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to develop a simplified method to approximate constants minimizing the standard deviation (SD) and the root mean square (RMS) of the prediction error in single-optimized intraocular lens (IOL) power calculation formulas. Methods: The study introduces analytical formulas to determine the optimal constant value for minimizing SD and RMS in single-optimized IOL power calculation formulas. These formulas were tested against various datasets containing biometric measurements from cataractous populations and included 10,330 eyes and 4 different IOL models. The study evaluated the effectiveness of the proposed method by comparing the outcomes with those obtained using traditional reference methods. Results: In optimizing IOL constants, minor differences between reference and estimated A-constants were found, with the maximum deviation at -0.086 (SD, SRK/T, and Vivinex) and -0.003 (RMS, PEARL DGS, and Vivinex). The largest discrepancy for third-generation formulas was -0.027 mm (SD, Haigis, and Vivinex) and 0.002 mm (RMS, Hoffer Q, and PCB00/SN60WF). Maximum RMS differences were -0.021 and +0.021, both involving Hoffer Q. Post-minimization, the largest mean prediction error was 0.726 diopters (D; SD) and 0.043 D (RMS), with the highest SD and RMS after adjustments at 0.529 D and 0.875 D, respectively, indicating effective minimization strategies. Conclusions: The study simplifies the process of minimizing SD and RMS in single-optimized IOL power predictions, offering a valuable tool for clinicians. However, it also underscores the complexity of achieving balanced optimization and suggests the need for further research in this area. Translational Relevance: The study presents a novel, clinically practical approach for optimizing IOL power calculations.

Indexed as

Lenses, IntraocularOptics and PhotonicsAgedBiometryFemaleHumansLens Implantation, IntraocularMaleMiddle AgedRefraction, OcularVisual Acuity

Identifiers

PMID38837172
PMCPMC11160955

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.