Towards a Functional Classification of Intraocular Lenses

Joaquín Fernández

The increasing variety of intraocular lenses (IOLs) has made it imperative to define clear, evidence-based classification criteria. Historically, IOLs were often classified by optical principles or physical structures, but these lacked a direct correlation with real-world functional outcomes, such as the visual acuity experienced across the Depth of field (DoF) or Range of field (RoF). This inconsistency led to confusion among ophthalmologists and surgeons when comparing technologies and choosing the most suitable lenses for patients. Functional classification, therefore, bridges this gap, offering a standardized way to evaluate IOLs based on clinical outcomes that matter to patients, such as visual and patient reported outcomes.

How was the functional classification developed?
 

The creation of the new classification is grounded in rigorous scientific methodology (1). A scoping review of existing studies identified key visual performance metrics. Cluster analysis of the data revealed patterns that allowed IOLs to be categorized based on their functional outcomes. Specifically, two variables for defining cut-offs emerged from the monocular distance-corrected defocus curve:

• The RoF achieved, measured at specific visual acuity cut-offs of 0.2 and 0.3 logMAR.
• The visual acuity improvement (ΔVA) between intermediate and near vision.

By relying on defocus curves and statistical techniques, the classification was developed to reflect evidence-based cut-offs.

Functional classification categories
 

The functional classification introduces six categories of IOL performance based on the RoF and ΔVA cut-offs detailed in Table 1.

How to classify the performance of an IOL based on the defocus curve
 

Although the table might be difficult to digest for clinicians unfamiliar with the defocus curve, it is straightforward to classify the performance of an IOL in two steps for partial RoF and three steps for Full RoF IOLs:

• Step 1: Normalize the defocus curve to allow comparisons between studies.This involves adjusting the curve upward or downward so that the far-distance peak aligns at 0D and 0 logMAR. This normalization is essential to minimize bias caused by differences in sample age or testing conditions across studies.
• Step 2: Identify the achieved RoF. In Figure 1, colored backgrounds delineate areas based on the cut-offs specified in the table. A dashed line separates the two main categories: Partial RoF and Full RoF.
• Step 3: For Full RoF IOLs, determine the visual acuity difference between the minimum at intermediate distance and the maximum at near distance.

The Functional Classification of Intraocular Lenses: Table 1

In Figure 1, two monocular distance-corrected defocus curves are illustrated. After Step 1, both curves align at 0 logMAR and 0D. In Step 2, the red curve is identified as a Partial RoF IOL within the Enhanced range, leading to its classification as PARTIAL-RoF Enhanced. For the orange curve, Step 2 identifies it as a Full RoF IOL. Proceeding to Step 3, the visual acuity difference between intermediate and near points exceeds 0.05 logMAR but remains below 0.14 logMAR. According to the table, this performance is classified as FULL-RoF Smooth.

Which organizations support the functional classification and what’s next?
 

The functional classification was proposed as a future line of work from the ESCRS Functional Vision Working Group (2). This classification does not replace the current international standards, but provides further evidence to the cut-offs already included in the ISO 11979-7:2024 and the ANSI guidelines (3,4). It represents a complement that introduces, based on evidence, IOL performances that are not differentiated in the current standards and allow the surgeon to have a better understanding based on clinical outcomes.

Future plans aim to address current limitations, particularly the omission of significant functional outcomes like dysphotopsia. As clinical studies improve their reporting quality, outcomes such as patient-reported experiences may be integrated into an updated classification system. In addition, although the cut-offs have been derived from scientific method and not from opinions, the terminology used for referring to each one of the six categories is based on the authors’ academic knowledge. This subjectivity may result in a nomenclature that, in certain contexts, could benefit from better adaptation to socio-cultural particularities. At ESCRS, we are committed to the pursuit of consensus among the three key stakeholders: scientific societies, regulators (ISO), and the industry.

Figure 1. Schematic example for classifying functional performance based on the monocular distance corrected defocus curve