JCRS this month
Updated: Sep 29
Concise updates of important studies from JCRS this month
All you need to know about the latest in IOL optics, IOL power calculation, corneal topography and refractive outcomes from peer reviewed papers this month
This paperchallenges a common assumption in toric IOL calculation: that the SIA we calculate from preoperative and postoperative keratometry represents the true effect of surgery. The paper argues that a considerable part of the measured SIA may simply be measurement noise.
Noise-Floor Benchmarking of Surgically Induced Astigmatism
The central question
When we calculate surgically induced astigmatism, we normally do this: Preoperative corneal astigmatism → cataract surgery → postoperative corneal astigmatism
The vector difference is labelled SIA.
But there is a fundamental problem: The cornea may not have changed by as much as the measured difference suggests.Why?
Because both the preoperative and postoperative measurements have their own measurement variability. Therefore:
Measured SIA = True surgical effect + measurement noise
The authors wanted to determine how much of the observed SIA could be explained simply by measurement variability. How did they investigate this?
The study was a retrospective, single-surgeon case series. They analysed:
324 eyes using standard keratometry (K)
269 eyes using total keratometry (TK)
Preoperative and postoperative measurements were obtained with the IOLMaster 700.
The investigators calculated SIA as a vector difference, rather than simply subtracting cylinder magnitudes.
This distinction is important. For astigmatism:
0.50 D @ 180° → 0.50 D @ 90°
is not a zero change simply because the cylinder magnitude is unchanged.
The axis is part of the measurement. The clever part: creating a "zero-SIA" noise model
This is probably the most interesting aspect of the study.
The investigators first established the repeatability/noise characteristics of their measurements.
They estimated measurement variability of:
σ = 0.12 D for standard K
σ = 0.14 D for TK
They then used a Monte Carlo simulation to create a hypothetical situation in which:
The surgeon caused absolutely zero astigmatic change. They repeatedly simulated preoperative and postoperative measurements with the known measurement variability.
This produced a distribution of apparent SIA that would arise even if the surgery produced no astigmatism at all.
That distribution is the noise floor. This gives us a very useful conceptual distinction:
Observed SIA. What we calculate from the patient's pre- and postoperative measurements.
Noise-floor SIA
What we could observe purely because the measurement is imperfect.
True/systematic SIA
The component that remains after accounting for the measurement noise.
What did they actually find? The measured mean SIA was:
Measurement Mean SIA
Standard K 0.38 ± 0.23 D
Total K (TK) 0.42 ± 0.25 D
So, on the surface, TK appeared to show slightly greater SIA than standard K.
But this is where the paper becomes much more interesting.
The authors found that a substantial proportion of these measured SIA values overlapped with the zero-SIA noise distribution.
In other words:
Some of the 0.38 D or 0.42 D that we call "SIA" may simply be measurement variability.
The measured distributions did show a statistically significant displacement from zero, meaning that there was a genuine systematic surgical component.
But that component was described as modest after considering the noise floor.
K versus TK: another important finding
The paper also compares standard keratometry with total keratometry.
The Performance Efficiency Index was:
K: 88.8%
TK: 89.4%
These values were remarkably close.
So this study does not demonstrate a major difference in SIA measurement performance between standard K and TK.
The difference:
89.4 − 88.8 = 0.6 percentage points is very small. This is an important nuance.
The study is not really saying:
"TK is better than K for measuring SIA."
Instead, it is demonstrating that both measurement approaches have a substantial noise floor, and that this needs to be considered when interpreting SIA.
Why is this important for toric IOL calculation?
This has a direct connection to something we frequently discuss in toric IOL calculations:
SIA is an input. When we enter an SIA value into a toric calculator, we are effectively saying:
"This surgeon's incision produces approximately X diopters of astigmatic change in this direction."
But where did that number come from? Usually from historical postoperative data.
And this paper raises an important question: How much of the historical SIA is real surgical effect, and how much is measurement noise?
That is a very important question when dealing with small-incision modern cataract surgery.
The 0.30–0.40 D SIA problem
Consider a surgeon who calculates: Mean SIA = 0.35 D It is tempting to interpret this as:
"My incision induces 0.35 D of astigmatism."
But the study suggests that we should be more cautious.
If the measurement system itself has approximately 0.12–0.14 D standard deviation of repeatability, a measured 0.35 D change cannot automatically be assumed to represent a 0.35 D biological/surgical effect. Some of it may be the noise floor.
This becomes particularly relevant when we are trying to fine-tune toric calculations to fractions of a dioptre.
And this has an even bigger implication
Suppose two toric formulas differ in predicted residual astigmatism by:
0.03–0.05 D
That difference may be statistically detectable in a very large dataset.
But if the underlying measurement system has a noise floor of approximately 0.1 D or more, we should ask:
Is the difference meaningful clinically, or are we trying to optimise the calculation beyond the precision of our measurements?
This is a fascinating companion to the other JCRS paper mentioned below immediately —the 6,753-eye comparison of six contemporary toric formulas. That paper found mean vector errors clustered around roughly 0.54–0.62 D. This new paper provides another piece of the puzzle:
The calculation formula is not the only source of uncertainty. The measurement itself has a noise floor
Six Contemporary Toric Formulas: What Does a 6,753-Eye Study Tell Us?
A new study by Skrzypecki, Koch and Wang, published in the Journal of Cataract & Refractive Surgery, provides one of the largest contemporary comparisons of toric IOL calculation methods, analysing 6,753 eyes.
The study compared four modern toric calculation approaches incorporated into the ESCRS IOL Calculator:
Barrett Toric — using measured posterior corneal curvature (Barrett M)
Barrett Toric — using predicted posterior corneal curvature (Barrett P)
EVO Toric — using measured posterior corneal curvature (EVO M)
EVO Toric — using predicted posterior corneal curvature (EVO P)
Kane Toric
Hoffer QST
Thus, although there were four underlying formula families, Barrett and EVO were each evaluated in two posterior-cornea configurations, producing six calculation methods.
The study design
This was a retrospective database study involving multiple surgical centres in the United States.
An important methodological point is that the eyes had received non-toric IOLs. The investigators therefore used the preoperative biometry and corneal astigmatism to calculate what residual astigmatism each toric formula would have predicted. Preoperative measurements, including posterior corneal curvature, were obtained with the IOLMaster 700.
The primary outcome was not simply cylinder magnitude. The investigators used vector analysis, assessing:
Mean vector magnitude prediction error
The proportion of eyes with prediction error ≤0.50 D
Centroid error
Variance of the prediction errors
This is important because astigmatism is a two-dimensional vector—both magnitude and axis matter.
What did they find?
The Kane formula had the lowest mean vector magnitude prediction error: 0.537 D.
The corresponding errors were:
Calculation method Mean vector magnitude error
Kane 0.537 D
EVO P 0.561 D
EVO M 0.570 D
Barrett M 0.579 D
Barrett P 0.583 D
Hoffer QST 0.622 D
The differences were statistically significant, with P < 0.001 for the comparisons. Kane also produced the highest proportion of eyes with a prediction error of ≤0.50 D.
Interestingly, the Kane advantage was maintained across the three major astigmatism-orientation groups:
With-the-rule
Against-the-rule
Oblique astigmatism
Kane also demonstrated the lowest total variance among the methods studied.
But there is a more important message. At first glance, the result might appear straightforward:
Kane produced the lowest prediction error.
But that would be an incomplete interpretation of the paper. The actual differences were relatively small. The difference between Kane and EVO P was only:
0.561 − 0.537 = 0.024 D
The difference between Kane and Barrett M was:
0.579 − 0.537 = 0.042 D
Even compared with Hoffer QST, the difference was approximately: 0.085 D.
So although these differences were statistically significant—something that is quite possible in a dataset of 6,753 eyes—their clinical significance is much less obvious.
The authors themselves make this point, concluding that the clinical relevance of these relatively small differences remains uncertain.
Why is this important for toric IOL calculation?
Toric IOL prediction is fundamentally more complicated than simply calculating the spherical equivalent. The final refractive cylinder depends on several interacting variables:
-Anterior corneal astigmatism
-posterior corneal astigmatism
-effective lens position
-toric rotational stability
-surgically induced astigmatism
-actual postoperative IOL orientation**
The calculation therefore attempts to predict a postoperative vector, not simply a cylinder number.
This study is particularly relevant because it demonstrates that even with sophisticated modern formulas, the residual error is not eliminated.
A particularly interesting finding: posterior corneal curvature
Barrett and EVO were each tested using two approaches:
M = measured posterior corneal curvature
P = predicted posterior corneal curvature
This gives us an interesting comparison.
For Barrett:
Barrett M: 0.579 D
Barrett P: 0.583 D
For EVO:
EVO M: 0.570 D
EVO P: 0.561 D
Thus, the use of measured posterior corneal curvature did not uniformly produce a lower prediction error.
That is an important observation because it reminds us that:
Adding a measured parameter does not automatically translate into a clinically meaningful improvement in prediction.
The way that parameter is incorporated into the complete calculation—and how accurately the parameter itself is measured—also matters.
Another important observation: every formula had a systematic component
The authors report that centroid values were significantly different from zero for all formulas.
This means that the errors were not purely random.
In simple terms, the formulas were not just “missing” the target randomly from eye to eye. There was also a systematic directional component to the residual error.
This is particularly relevant when thinking about toric calculation as a vector problem.
A formula can have:
-random variability,
-systematic bias,
or both.
Reducing the random component improves consistency, while eliminating systematic bias requires correcting the direction and magnitude of the underlying prediction error.
What the study does NOT tell us
There is an important limitation when interpreting the findings.
The study analysed eyes implanted with non-toric IOLs and used their preoperative data to model predicted residual astigmatism. It therefore evaluates the predictive behaviour of the calculation methods, rather than being a conventional head-to-head clinical trial in which the different toric formulas were actually used prospectively to select toric IOLs in separate treatment groups.
Consequently, real-world toric outcomes can still be affected by factors that are outside the mathematical formula itself.
For example:
1. IOL rotational stability
A perfectly calculated toric IOL will not deliver the intended correction if it rotates.
2. Preoperative measurement repeatability
A 0.75 D difference in measured corneal cylinder or a change in axis can be more consequential than a few hundredths of a diopter difference between modern formulas.
3. Posterior corneal astigmatism
The posterior cornea contributes to total corneal astigmatism and can influence both magnitude and axis.
4. Surgically induced astigmatism
Incision location, incision size and individual wound response introduce another source of prediction error.
5. Effective lens position
The toric calculation ultimately has to translate corneal astigmatism into the appropriate IOL-plane correction.
6. Axis alignment
A toric IOL that is mathematically correct but not aligned correctly will produce residual astigmatism.
The practical takeaway
For me, the most useful interpretation of this study is not simply that Kane had the lowest numerical error.
The broader message is:
Modern toric formulas are remarkably close to one another, and the differences between them are much smaller than the overall residual prediction error.
The mean errors in this study clustered between approximately 0.54 and 0.62 D, despite the sophisticated mathematical approaches used by all six methods.
That suggests that the next gains in toric IOL outcomes may not come solely from developing another slightly different mathematical formula.
They may increasingly come from improving the quality of the inputs:
Better corneal measurement → better astigmatism vector → better posterior corneal estimation → better ELP prediction → better toric power and axis selection → better postoperative alignment.
In other words:
The formula is only one component of the toric prediction chain.
A useful way to think about the 6,753-eye study
I would summarise the paper in one sentence-
Kane demonstrated the lowest statistical prediction error among the six contemporary toric calculation methods studied, but the differences were small enough that measurement quality, posterior corneal astigmatism, surgically induced astigmatism and postoperative IOL alignment remain highly relevant determinants of the final refractive result.
Reference
Skrzypecki J, Koch DD, Wang L. Accuracy of six contemporary toric formulas: Analysis of 6,753 Eyes. J Cataract Refract Surg. 2026;52:1005–1009. DOI: 10.1097/j.jcrs.0000000000001964.
What is the impact of pupil size on defocus curve attained with diffractive multifocal IOLs?
Davidova P, Kaiser KP, Jandewerth T, et al.Impact of pupil size on defocus curve, contrast sensitivity and patient satisfaction in a diffractive, simultaneous vision intraocular lens.J Cataract Refract Surg. 2026;52(9):843–849.DOI: 10.1097/j.jcrs.0000000000001912.
What did they study?
This was a post-hoc analysis of prospective studies involving 52 patients, mean age 68 years, who had bilateral implantation of the AcrySof IQ PanOptix diffractive simultaneous-vision IOL.
Patients were assessed 3 months after surgery. The investigators measured:
monocular defocus curves
photopic and mesopic pupil size
binocular visual acuity at different distances
contrast sensitivity
spectacle independence
satisfaction with several everyday activities.
Importantly, pupil size was measured objectively using infrared pupillometry.
The pupil-size classification is particularly interesting
They did not simply report an average pupil.
They categorized pupils as:
Condition | Small | Medium | Large |
Mesopic | ≤4 mm | >4–5 mm | >5 mm |
Photopic | ≤2.5 mm | >2.5–4 mm | >4 mm |
The mean measured pupil sizes were:
Mesopic: 4.24 mm, range 2.60–6.10 mm
Photopic: 3.38 mm, range 1.20–5.60 mm.
That ≤2.5 mm photopic threshold is therefore an actual study definition—not an arbitrary cutoff added to the figure.
What happened to the defocus curve?
The investigators found that smaller pupils were associated with better visual acuity across the defocus curve and a larger range of depth of field. However, the relationship wasn't equally strong everywhere. The effect was particularly apparent toward near
Photopic pupil size showed:
a weak correlation with near visual performance
some correlation with the intermediate range
stronger relevance to the range of depth of field.
So the important message isn't simply:
Small pupil = better defocus curve.
It is more accurately:
With this diffractive simultaneous-vision IOL, smaller pupil size was associated particularly with better near performance and a wider useful range of vision.
What is “range of depth of field” in this study?
They evaluated the range of defocus over which visual acuity remained at or better than specified logMAR thresholds, including ≤0.2 and ≤0.3 logMAR.
Pupil size showed a moderate correlation with these ranges under both photopic and mesopic conditions. In practical terms: smaller pupil → the patient could maintain a given level of visual acuity over a wider defocus range.
What about spectacle independence?
This is another clinically relevant finding.
Smaller photopic pupils were associated with greater near spectacle independence.
The effect wasn't reported as a universal improvement in spectacle independence at every distance.
The strongest relationship was specifically with near spectacle independence.
What about patient satisfaction?
Interestingly, the pupil effect was not universal across all activities.
There was a trend toward greater satisfaction with newspaper reading in patients with smaller pupils. But for the other assessed activities, pupil size was not statistically significant according to the abstract.
The optical explanation
This is where the paper becomes particularly interesting for IOL optics.
A smaller pupil means that the eye samples a smaller central portion of the optical system.
That reduces the contribution of peripheral rays and therefore reduces the impact of:
higher-order aberrations
peripheral optical imperfections
defocus-related blur
At the same time, a smaller aperture produces the familiar increase in depth of field.
For a diffractive simultaneous-vision IOL, however, there is another layer: the pupil determines how much of the diffractive optic is actually contributing to the retinal image.
Therefore, the clinical defocus curve is not purely a property of the IOL. It is the result of an interaction: IOL optical design + pupil size + ocular aberrations + retinal/visual factors
The authors' results provide clinical evidence for this interaction. A recent review discussing this study likewise notes that smaller pupils with PanOptix were associated with better visual acuity across the defocus curve and greater depth of field.
END of Sept 2026 update
Does the apply of capsular tension ring (CTR) improve rotational stability in high myopia patients - Evidence from JCRS
Toric IOL Stability: What Determines It?
The following three factors are important in determining the stability of a toric IOL.
First, capsular-bag geometry: This includes the size and shape of the capsular bag. Parameters such as axial length and white-to-white may provide useful clinical clues about the underlying ocular anatomy, although they are indirect biomarkers of capsular-bag dimensions.
Second, zonular support: Zonular laxity can allow the IOL to decenter or rotate. In a toric IOL, this can reduce the effectiveness of astigmatic correction and manifest clinically as residual astigmatism.
Third, the interaction between the IOL and the capsular bag: IOL diameter, haptic design, capsular-bag dimensions, and the way the haptics interact with the capsule can all influence postoperative rotational stability, centration, and tilt.
High axial length may influence all three of these factors—capsular-bag geometry, zonular support, and the IOL–capsular-bag interaction.
What did this study investigate?
This study looked at whether adding a capsular tension ring, or CTR, could improve toric IOL stability in eyes with high axial myopia.
The study included 90 eyes of 68 patients, all with an axial length of at least 26 mm.
The patients received either:
a toric IOL alone, or
a toric IOL combined with a CTR.
Two toric IOL designs were studied: a C-loop haptic IOL and a plate-haptic IOL.
The investigators assessed:
IOL rotation
rotation ≥10 degrees
residual astigmatism
IOL decentration
IOL tilt
corrected distance visual acuity
These were assessed particularly at 1 week and 3 months after surgery.
What did they find?
The most important finding was that the CTR improved early rotational stability.
At 1 week:
Mean rotation was 4.26° without CTR versus 3.20° with CTR.
Rotation of ≥10° occurred in 12.9% without CTR versus 1.8% with CTR.
The multivariate analysis found that CTR use was associated with a substantially lower risk of ≥10° rotation:
RR = 0.21, P = .045.
In other words, within this study, CTR was associated with approximately a 79% lower relative risk of significant rotation.
However, by 3 months, the difference in rotation between the groups was no longer statistically significant.
What happened in very long eyes?
This is perhaps the most clinically interesting part of the study.
In eyes with AL ≥30 mm, CTR implantation was associated with:
less IOL rotation at 1 week,
less residual astigmatism at 1 week, and
delayed occurrence of ≥10° rotation.
The Kaplan-Meier analysis also showed a significant difference in the timing of significant rotation in the AL ≥30 mm subgroup.
This suggests that the benefit of CTR may become more relevant as the eye becomes extremely elongated.
What about IOL design?
The study found that the particular plate-haptic IOL had a higher risk of ≥10° rotation than the particular C-loop IOL:
RR = 6.613, P = .01.
Importantly, this should not be interpreted as proving that all plate-haptic IOLs rotate 6.6 times more than all C-loop IOLs. The study compared two specific IOL models.
Interestingly, in the plate-haptic subgroup, CTR implantation significantly reduced IOL tilt:
2.03° without CTR versus 0.96° with CTR, P = .005.
What happened to centration and tilt?
At 3 months, CTR implantation was associated with significantly less IOL decentration:
0.43 mm without CTR versus 0.28 mm with CTR.
However, there was no significant difference in overall IOL tilt between the CTR and control groups. There was also no significant difference in corrected distance visual acuity between the groups.
What can clinicians take from this study?
The practical lesson is that CTR may be particularly useful when there is concern about capsular-bag stability in highly myopic eyes, especially in extremely long eyes with AL ≥30 mm. The study provides a rationale for considering CTR when implanting a toric IOL in an anatomically challenging, highly elongated eye.
It also reminds us that IOL design matters. The interaction between the haptic architecture and the capsular bag may influence postoperative rotational stability.
What should clinicians NOT conclude?
This study does not prove that every highly myopic eye requires a CTR.
It does not establish that:
CTR permanently prevents toric IOL rotation;
CTR improves visual acuity in all highly myopic eyes;
every plate-haptic IOL is inherently 6.6 times more likely to rotate;
axial length alone determines IOL stability; or
CTR is universally superior to no CTR.
The study was relatively small, single-center, and non-randomized, and it evaluated only two specific toric IOL models.
The Take-Home Message
So, my take-home message from this study would be:
Toric IOL stability is a biomechanical interaction between the capsular bag, zonules, and IOL design. High axial length may adversely influence all three. A CTR can provide additional capsular support and, in this study, improved early rotational stability—particularly in eyes with AL ≥30 mm.
But the decision to use a CTR should remain individualized, based on the anatomy of the eye, the expected capsular stability, and the characteristics of the IOL being implanted.
A second study by Lin et al., published in the Journal of Cataract & Refractive Surgery in 2025, looked specifically at plate-haptic toric IOLs with and without a capsular tension ring.
This was a prospective randomized paired-eye study involving 93 patients, or 186 eyes. Each patient's two eyes were randomized, with one eye receiving a CTR and the fellow eye receiving the same plate-haptic toric IOL without a CTR. Patients were followed for 12 months.
What did they find?
The CTR group showed less toric IOL rotation during the early postoperative period.
The important finding was that the IOL stabilized approximately:
within 1 week with a CTR
compared with approximately 2 weeks without a CTR.
At 2 weeks, residual astigmatism was also lower with the CTR:
0.49 D vs 0.61 D, P = .036.
The benefit was particularly apparent in eyes with characteristics suggesting a larger capsular bag, including:
AL ≥26 mm
WTW ≥11.6 mm
lens thickness ≥4.5 mm
and ACD <3.2 mm.
The authors proposed that the CTR may stabilize the IOL by expanding/supporting the equatorial capsular bag, increasing IOL–capsule contact and friction, and reducing the available space for IOL movement during the early postoperative period.
An important clinical message
The CTR reduced rotation but did not completely eliminate it. Six eyes required repositioning—five without CTR and one with CTR.
So the message from this study is:
CTR appears to be particularly useful for controlling early rotation of plate-haptic toric IOLs, especially when biometric parameters suggest a relatively large capsular bag.
And this study fits very nicely with the previous paper:
Lin et al. → CTR accelerates early stabilization, particularly in eyes with larger anatomical dimensions.
Sun et al. → CTR appears particularly beneficial in very long eyes, especially AL ≥30 mm, and reduces early rotation and decentration.
Together, these studies support the concept that the anatomy of the capsular bag—not simply the choice of toric IOL—is an important determinant of rotational stability.
