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An Evidence-Based Pathway from Functional Classification to Intraocular Lens Specialization: A Case Example Applying

Joaquín Fernández¹, Filomena Ribeiro^2,3, Burkhard H Dick⁴

¹Qvision, Department of Ophthalmology of VITHAS Almería Hospital, Almería, Spain.

Ophthalmology and Therapy

|May 22, 2026

Summary

This summary is machine-generated.

The Predict-Explore-Confirm-Optimize (PECO) framework aids selecting simultaneous vision intraocular lenses (SVIOLs). This structured approach ensures evidence-based SVIOL selection for optimal patient outcomes.

Keywords:

Evidence Functional classification Intraocular lens Simultaneous vision

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Area of Science:

Ophthalmology
Biomedical Engineering

Background:

Simultaneous vision intraocular lenses (SVIOLs) enhance depth of field and reduce spectacle dependence post-cataract surgery.
A growing number of SVIOL models necessitates a structured approach for evidence-based selection.

Purpose of the Study:

To propose the Predict-Explore-Confirm-Optimize (PECO) framework for systematic evidence generation and clinical decision-making regarding SVIOLs.
To illustrate the application of the PECO framework using the Liberty trifocal SVIOL as a case example.

Main Methods:

The PECO framework involves sequential evidence generation: optical bench testing (Predict), early clinical experience (Explore), large clinical studies (Confirm), and targeted research on patient-specific factors (Optimize).
The Liberty trifocal SVIOL was evaluated through this framework, examining standard outcomes and specific factors like dysphotopsia, contrast sensitivity, and biometric accuracy.

Main Results:

The Liberty SVIOL was validated as a Full-DOFi Smooth SVIOL.
Evidence gathered in the Optimization stage refined understanding of patient expectations, surgical planning, and counseling for SVIOLs.

Conclusions:

The PECO framework provides a structured pathway for evidence generation and clinical decision-making for SVIOLs.
Optimal SVIOL outcomes are achieved through confirmed functional performance combined with individualized, evidence-informed specialization based on patient characteristics and clinical context.