Postoperative spectacle independence is a common expectation among patients undergoing refractive cataract surgery. Although refractive surprises can occur, the latest IOL formulas allow most patients to achieve an outcome that is within ±0.50 D of the refractive target whenever preoperative measurements are accurate.

By reviewing their surgical outcomes, ophthalmologists can identify patterns, better understand what went wrong when results are suboptimal, and implement corrective measures. Unfortunately, IOL constant optimization remains an uncommon practice because of the technical complexity of traditional methods, particularly when proprietary or undisclosed formulas such as those included in the ESCRS IOL Calculator (iolcalculator.escrs.org) are used. The development of simpler methods could help, as discussed in this article.

THE CHALLENGES OF TRADITIONAL OPTIMIZATION

IOL constant optimization typically requires the collection of complete biometric data, access to or knowing the IOL formula, and a solid understanding of its underlying mathematics. The optimization process can be time-consuming and is frequently perceived as unnecessary because most physicians use newer-generation IOL formulas with built-in prediction enhancements.

THREE-VARIABLE OPTIMIZATION

A Simpler Alternative

In an effort to simplify IOL constant optimization, I was fortunate to work with an outstanding team of experts, including David L. Cooke, MD; Giacomo Savini, MD; Kenneth Hoffer, MD; Catarina Coutinho, MSc; Enrico Lupardi, MD; Jaime Aramberri, MD; and Jorge Buonsanti, MD.¹ We dubbed the resulting method three-variable optimization because it requires only three datapoints:

• No. 1: The average power of all implanted IOLs;
• No. 2: The constant used for the IOL calculation; and
• No. 3: The average prediction error, defined as the expected refraction (in spherical equivalent [SEQ]) subtracted from the actual postoperative refraction (also in SEQ).

We developed the approach using a dataset of 876 patients and validated the method on an independent dataset of 1,079 eyes. We compared the performance of three-variable optimization with traditional (full) optimization methods across all eyes and subgroups—short eyes (axial length < 22 mm), long eyes (axial length > 25 mm), and standard eyes (axial length = 22 to 25 mm).¹ Three-variable and standard optimization performed similarly, with no statistically significant differences across groups, but the former approach required less data and less or minimal technical knowledge.

Three-variable optimization works with classic and new-generation formulas such as the Barrett Universal II, Cooke K6, Hoffer QST, PEARL-DGS, Kane, Emmetropia Verifying Optical, and Hill-RBF (Radial Basis Function).

Number of Cases Required

According to Langenbucher et al,² IOL constants begin to stabilize after data from about 50 eyes have been entered, and data on approximately 100 eyes are required for Haigis triple optimization. This suggests that the three-variable optimization method can become a reliable tool in practice once a modest sample size has been gathered.

Personalized constants may differ between biometers, and a periodic update is recommended.

Getting Started

I recommend creating a simple spreadsheet (Figure 1) and routinely logging the following data for each patient:

• The IOL implanted;
• The constant used; and
• The average prediction error, defined as the expected refraction (in SEQ) subtracted from the actual postoperative refraction (also in SEQ).

A massive dataset is not required. Once data from about 30 to 40 patients have been gathered for a specific IOL and formula, a personal constant with useful accuracy can be created. A patient whose refraction is more myopic than expected will have a negative prediction error.

To facilitate the optimization process, my colleagues and I created a free online tool, which is available at IOLoptimization.com. The website is intuitive and straightforward. Users enter the three required variables, click, and receive their optimized constant (Figure 2). Dataset results can be saved.

WHY PERSONAL OPTIMIZATION MATTERS

As new IOL designs and technologies are introduced, it becomes increasingly important for cataract surgeons to track their outcomes and adjust for systematic refractive trends. Not infrequently, I hear an ophthalmologist say, “I’m getting slightly myopic results with this lens.” Rather than accept that bias or change their target, the physician could improve their surgical outcomes by optimizing their IOL constant. Gathering the relevant data, moreover, sometimes reveals to a surgeon that their results are different than they thought.

Many IOLs come to market with constants based on small sample sizes or early data. Personal outcomes can provide a more relevant, real-world calibration for a specific physician’s patient population and surgical technique.

Some clinicians hesitate to record small postoperative refractive errors (eg, ±0.25 or ±0.50 D), particularly if the patient is satisfied with their outcome. In this situation, fogging can be a helpful technique that avoids confusing patients or inciting dissatisfaction yet yields valuable refractive data.³ The patient’s vision is blurred with a plus lens, and the power is gradually reduced until the best SEQ is reached.

ALTERNATIVES

Reputable Sources

IOL constants are available from a variety of reputable sources, including the User Group for Laser Interference Biometry (ocusoft.de), IOLCon (iolcon.org), manufacturer recommendations, and formula authors’ websites. It is important to recognize, however, that differences between these constants exist. Although it may not align perfectly with any of the aforementioned sources, an ophthalmologist’s optimized constant reflects their own surgical outcomes, which ultimately is more valuable for achieving refractive precision.

I recommend that practitioners who have not yet collected their own data use the constant suggested by each formula author’s website or IOLCon whenever available.

Other Optimization Methods

Alternative approaches to personal constant optimization have also emerged. A novel method described by Damien Gatinel, MD, PhD, includes keratometry data and has been validated across various studies.⁴ Another approach developed by Enrico Lupardi, PhD, FEBO, uses web scraping to streamline the data-gathering process for optimization.⁵ Additionally, some biometric devices and software offered by companies offer constant optimization.

All of these excellent tools support the broader goal of encouraging surgeons and researchers to take control of their outcomes by customizing IOL constants.

FINAL THOUGHTS

Personal IOL constant optimization is not just for research. It is also a practical measure that can enhance surgical outcomes without greatly impeding workflow. By simplifying the process and reducing technical barriers, methods such as three-variable optimization and the approaches developed by Drs. Gatinel and Lupardi can help ophthalmologists improve the refractive accuracy of cataract surgery and offer better visual outcomes to their patients.

1. Buonsanti D, Cooke DL, Hoffer KJ, et al. A novel method to optimize personal IOL constants. Am J Ophthalmol. 2025;269:355-361.

2. Langenbucher A, Schwemm M, Eppig T, Schröder S, Cayless A, Szentmáry N. Optimal dataset sizes for constant optimization in published theoretical optical formulae. Curr Eye Res. 2021;46(10):1589-1596.

3. Aramberri J, Hoffer KJ, Olsen T, Savini G, Shammas HJ, eds. Intraocular Lens Calculations. Springer; 2024. https://doi.org/10.1007/978-3-031-50666-6

4. Gatinel D, Debellemanière G, Saad A, et al. A simplified method to minimize systematic bias of single-optimized intraocular lens power calculation formulas. Am J Ophthalmol. 2023;253:65-73.

5. Lupardi E, Hoffer KJ, Fontana L, Savini G. Method to analyze the refractive outcomes of online intraocular lens power formulas. J Cataract Refract Surg. 2023;49(3):321-322.