Abstract
Introduction
Patients with retinitis pigmentosa (RP) frequently exhibit zonular weakness, which poses challenges for intraocular lens (IOL) stability and refractive prediction. This study aimed to evaluate the impact of capsular tension ring (CTR) implantation on the predictive accuracy of 12 IOL power calculation formulas in patients with RP receiving cataract surgery.
Methods
We conducted a retrospective cohort study where the predictive accuracy of 12 IOL formulas was assessed using refractive prediction error (PE), mean absolute error (MAE), root-mean-square absolute error (RMSAE), and the percentage of eyes achieving target refraction within ± 0.25 D to ± 1.00 D. These metrics were compared between eyes with (n = 23) and without (n = 30) CTR implantation. The influence of lens thickness (LT) on formula accuracy was also evaluated.
Results
In the overall cohort of 53 eyes from 38 patients with RP, the Barrett Universal II (BUII) formula yielded the lowest numerical MAE (0.45 D) and RMSAE (0.56 D), though none of the formulas were statistically superior in RMSAE. In the CTR group, significantly lower MAE was observed for Cooke K6 (P = 0.024), Kane (P = 0.016), Emmetropia Verifying Optical (EVO) 2.0 (P = 0.040), and PEARL-DGS (P = 0.021) compared to the non-CTR group. The CTR group also exhibited a significantly higher percentage of eyes within ± 0.50 D (P = 0.016), ± 0.75 D (P < 0.001), and ± 1.00 D (P < 0.001) of target refraction. Increasing LT correlated with a hyperopic shift for all formulas; however, BUII demonstrated relatively stable MAE across LT quartiles.
Conclusions
Implantation of a CTR was associated with significantly improved predictive accuracy for several modern IOL formulas in patients with RP. The BUII formula showed the highest overall predictive accuracy.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40123-025-01292-y.
Keywords: Retinitis pigmentosa, Cataract surgery, Intraocular lens power calculation, Capsular tension ring
Key Summary Points
| Why carry out this study? |
| Retinitis pigmentosa (RP) is frequently complicated by cataracts. No studies to date have specifically assessed the accuracy of intraocular lens (IOL) formulas for patients with RP receiving cataract surgery. |
| While capsular tension rings (CTRs) are commonly used in patients with RP to stabilize zonules and improve surgical outcomes, it is unclear whether CTR implantation actually improves the accuracy of IOL power calculations. |
| What was learned from the study? |
| This study provides the first detailed comparison of multiple IOL formulas in RP-associated cataracts, evaluating their performance both with and without CTR implantation. |
| Implantation of a CTR was associated with significantly improved predictive accuracy for several modern IOL formulas in patients with RP, leading to a markedly higher percentage of eyes achieving refractive outcomes within ± 0.50 D, ± 0.75 D, and ± 1.00 D of target compared to those without CTRs. |
| The Barrett Universal II (BUII) formula demonstrated the highest calculation accuracy and also exhibited relative stability across varying lens thicknesses in patients with RP. |
Introduction
Retinitis pigmentosa (RP) is an inherited type of retinal dystrophy characterized by progressive vision loss due to photoreceptor degeneration [1]. Cataracts are the most common anterior segment complication associated with RP. Lens opacity typically develops during the middle stages of the disease and is often accompanied by significant clinical symptoms, with glare—particularly in bright light—being the primary complaint [2, 3]. The timing of cataract surgery in patients with RP is generally earlier, with the average age at surgery ranging from 47 to 63 years, as reported in several studies [4–8]. Cataract surgery has been shown to significantly improve visual acuity and alleviate subjective symptoms in the majority of patients with RP [2, 3, 9]. However, the unique challenges posed by RP require specific considerations in the surgical approach.
Compared with cataracts in patients without RP, those in patients with RP are often associated with zonular instability and a higher risk of intraocular lens (IOL) dislocation, as well as an increased incidence of posterior capsular opacification (PCO) and anterior capsule contraction [10–12]. Comparative analysis of anterior segment biometric data has revealed that patients with RP exhibit increased lens thickness, steepening of the anterior lens curvature, and increased corneal diameter compared to cataracts in patients without RP [13]. These factors complicate IOL power calculations prior to surgery. While advances in optical biometry have greatly improved the accuracy of measurements of ocular biological parameters, precise IOL formula calculations remain challenging and controversial for patients with RP. To date, no studies have specifically assessed the accuracy of IOL formulas for patients with RP receiving cataract surgery.
Many previous studies have suggested that capsular tension rings (CTRs) provide effective intraoperative support to stabilize zonules and enhance postoperative IOL fixation, leading to favorable surgical outcomes [7, 8, 14]. However, whether CTR implantation improves the accuracy of IOL power calculations remains unclear. Moreover, few studies have evaluated how the accuracy of new-generation IOL formulas is affected by CTR implantation in the eyes of patients with RP.
The purpose of this study was to compare the impact of CTR implantation on the accuracy of multiple IOL power calculation formulas—namely, Barrett Universal II (BUII), Cooke K6, Emmetropia Verifying Optical (EVO) 2.0, Haigis, Hoffer Q, Hoffer QST, Holladay, Kane, Karmona, LADAS, PEARL-DGS, and SRK_T—specifically in patients with RP with complicated cataracts.
Methods
Ethical Approval
This retrospective cohort study adhered to the principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the Eye Hospital of Wenzhou Medical University (Approval No. H2025-027-K-26). As the study utilized data obtained from routine clinical practice and did not involve any interventions, human biological specimens, or sensitive personal information, individual informed consent was waived in accordance with the Ethical Guidelines of the Ethics Committee of the Eye Hospital of Wenzhou Medical University. Additionally, the study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist to ensure the rigor and quality of the observational research.
Participants
Data were collected from patients diagnosed with cataracts complicated by RP who received cataract surgery at the Cataract Department of the Eye Hospital of Wenzhou Medical University between October 2018 and December 2024. The inclusion criteria included the availability of high-quality preoperative biometric data obtained using the swept-source frequency-domain OCT-based biometers (IOLMaster700, Carl Zeiss Meditec AG) and successful cataract surgery with IOL implantation in the capsular bag. Exclusion criteria included the presence of ocular comorbidities or a history of ocular surgery, lens dislocation, inability to complete postoperative refraction assessment, or a postoperative visual acuity (logMAR) greater than 1.0. Follow-up evaluations were conducted 1–3 months after surgery. If both eyes met the inclusion criteria, data from both eyes were included in the analysis. The presence of RP and cataracts was confirmed through medical records, which also provided ocular measurement data as detailed below.
Acquired Information and IOL Power Calculations
All participants underwent standard preoperative biometry using the IOLMaster 700. The parameters measured included axial length (AL), anterior chamber depth (ACD), lens thickness (LT), flat and steep keratometry readings, central corneal thickness (CCT), and horizontal corneal diameter (white-to-white). Visual acuity testing was performed with the 5 m Standard Logarithmic Visual Acuity (5SL) Chart under photopic light conditions (85 cd/m2), and results were converted to logMAR for analysis. The intraocular lens (IOL) power was calculated using multiple new-generation formulas via their respective online calculators. According to the protocols outlined in “Update on IOL Power Calculation Study Protocols” [15], the optimized lens constants for the specific IOL models used in this study were obtained from the IOLCon website (https://iolcon.org/). These constants are pre-optimized for each calculation formula based on large datasets.
For each eye, the predicted postoperative spherical equivalent (SE) was calculated using the different formulas. All surgeries were performed by three experienced surgeons and involved standard phacoemulsification through a 2.2-mm clear corneal incision. Following anterior continuous curvilinear capsulorhexis (ACCC) and hydrodissection, the lens nucleus was emulsified. After cortical aspiration, a foldable IOL was implanted in the capsular bag. The type of IOL implanted was selected based on the patient’s preferences following thorough communication and discussion with the surgeon. The specific models of implanted IOLs are listed in the Supplementary Material. The decision to implant a CTR was made intraoperatively on the basis of the surgeon’s assessment of capsular bag stability, which is determined by the extent of anterior capsular wrinkling observed during ACCC.
Subjective refraction was conducted by experienced optometrists at least 1 month after the surgery to determine the actual SE. The refractive prediction error (PE) was defined as the difference between the actual and predicted SEs. The following metrics were calculated to evaluate predictive accuracy: mean error (ME), mean absolute error (MAE), median absolute error (MedAE), root-mean-square of absolute PE (RMSAE), and the percentage of eyes with a PE within ± 0.25 D, ± 0.50 D, ± 0.75 D, and ± 1.00 D.
Statistical Analyses
All statistical analyses were performed using R software (version 4.2.2, https://www.r-project.org). The normality of continuous variables was assessed using the Kolmogorov–Smirnov test and visual inspection of Q–Q plots. Demographic and biometric parameters were analyzed using either Student’s t test or Mann–Whitney U test, depending on the normality of the data. Categorical variables were compared using Fisher’s exact test. For within-group comparisons of absolute errors across formulas, the Kruskal‒Wallis test was employed, followed by Mann–Whitney U tests for between-group comparisons. To address the potential influence of inter-eye correlation from including bilateral data, a sensitivity analysis was performed. For this analysis, only one eye was included per participant. This resulted in a sub-cohort of 38 eyes from 38 unique patients. The cutoff values for the LT subgroups were based on the quartiles (25th, 50th, and 75th percentiles) of the study population’s distribution. All tests were two-sided, and a difference where P < 0.05 was considered statistically significant.
Results
Demographics and Formula Accuracy
A total of 53 eyes from 38 patients were included in this study, and the flowchart of the included eyes is displayed in Fig. 1. Among them, 23 eyes from 14 patients received CTR implantation to stabilize capsular bags. Patient demographics and ocular biometric data for the entire cohort, as well as for the CTR and non-CTR subgroups, are presented in Table 1. No statistically significant differences in ocular biometric parameters were observed between the CTR and non-CTR groups (Mann‒Whitney U test, P > 0.05 for all comparisons).
Fig. 1.
The flowchart of the included individuals in this study. RP retinitis pigmentosa, LT lens thickness, CCT central corneal thickness, VA visual acuity, BCVA best-corrected visual acuity, CTR capsular tension ring
Table 1.
Patient demographics and ocular biometric data
| Parameter | Total | CTR | Non-CTR | P value |
|---|---|---|---|---|
| Male, n (%) | 25 (65.8) | 8 (57.1) | 17 (70.8) | 0.391 |
| Right eye, n (%) | 25 (47.2) | 10 (43.5) | 15 (50.0) | 0.783 |
| Age, years | 55.0 ± 13.8 (30, 80) | 50.1 ± 12.6 (30, 77) | 58.7 ± 13.9 (36, 80) | 0.051 |
| Axial length, mm | 23.54 ± 1.51 (20.62, 29.13) | 23.85 ± 1.43 (21.15, 27.34) | 23.31 ± 1.55 (20.62, 29.13) | 0.136 |
| Anterior chamber depth, mm | 2.70 ± 0.40 (1.96, 3.82) | 2.82 ± 0.43 (2.16, 3.82) | 2.62 ± 0.37 (1.96, 3.24) | 0.078 |
| Lens thickness, mm | 4.85 ± 0.52 (3.37, 6.77) | 4.73 ± 0.52 (3.37, 5.50) | 4.94 ± 0.54 (4.13, 6.77) | 0.358 |
| Flat keratometry, D | 43.28 ± 1.54 (39.85, 47.05) | 43.26 ± 1.30 (41.03, 45.87) | 43.31 ± 1.73 (39.85, 47.05) | 0.893 |
| Steep keratometry, D | 44.52 ± 1.41 (41.68, 47.96) | 44.64 ± 1.27 (42.23, 46.17) | 44.44 ± 1.53 (41.68, 47.96) | 0.530 |
| Corneal central thickness, μm | 553 ± 26 (513, 616) | 561 ± 22 (536, 603) | 548 ± 27 (513, 616) | 0.065 |
| White-to-white, mm | 11.75 ± 0.45 (10.8, 12.7) | 11.8 ± 0.4 (10.8, 12.4) | 11.7 ± 0.5 (11.0, 12.7) | 0.461 |
| IOL power, D | 22.4 ± 4.2 (6, 33) | 22.1 ± 3.2 (15, 28.5) | 22.6 ± 4.8 (6, 33) | 0.659 |
| Preoperative visual acuity (logMAR) | 1.1 ± 0.7 (0.1, 2.3) | 1.1 ± 0.6 (0.4, 2.3) | 1.1 ± 0.8 (0.1, 2.3) | 0.587 |
| Postoperative visual acuity (logMAR) | 0.5 ± 0.3 (0, 1.0) | 0.5 ± 0.3 (0, 1.0) | 0.4 ± 0.3 (0.1, 1.0) | 0.248 |
Results are expressed as mean ± SD (range). Anterior chamber depth, as measured from corneal epithelium to lens anterior surface
CTR capsular tension ring, D diopter, IOL intraocular lens, SD standard deviation
The refractive prediction outcomes for the 12 IOL power calculation formulas evaluated in the overall cohort are summarized in Table 2. The BUII formula demonstrated an ME closest to zero (0.01 ± 0.57 D). Several formulas resulted in a slight myopic shift, including Hoffer Q (− 0.16 ± 0.65 D), Hoffer QST (− 0.10 ± 0.65 D), and Holladay (− 0.22 ± 0.67 D). Conversely, Cooke K6 (0.22 ± 0.56 D), EVO 2.0 (0.15 ± 0.59 D), Kane (0.09 ± 0.58 D), Karmona (0.21 ± 0.59 D), and PEARL-DGS (0.37 ± 0.60 D) tended toward hyperopic prediction. The LADAS formula showed an ME near zero (− 0.02 ± 1.11 D) but with a larger standard deviation.
Table 2.
Refractive prediction errors of 12 IOL calculation formulas overall in the 53 eyes
| Formulas | ME ± SD | MAE ± SD | MedAE | RMSAE | Percentage of eyes within different PE (%) | |||
|---|---|---|---|---|---|---|---|---|
| ± 0.25 D | ± 0.50 D | ± 0.75 D | ± 1.00 D | |||||
| BUII | 0.01 ± 0.57 | 0.45 ± 0.34 | 0.40 | 0.56 | 37.74% | 60.38% | 83.02% | 98.11% |
| Cooke K6 | 0.22 ± 0.56 | 0.46 ± 0.38 | 0.41 | 0.60 | 30.77% | 59.62% | 80.77% | 96.15% |
| EVO 2.0 | 0.15 ± 0.59 | 0.47 ± 0.37 | 0.45 | 0.60 | 33.96% | 56.60% | 81.13% | 96.23% |
| Haigis | 0.02 ± 0.60 | 0.47 ± 0.36 | 0.41 | 0.59 | 32.08% | 60.38% | 83.02% | 90.57% |
| Hoffer Q | − 0.16 ± 0.65 | 0.51 ± 0.43 | 0.40 | 0.67 | 32.08% | 58.49% | 73.58% | 83.02% |
| Hoffer QST | − 0.10 ± 0.65 | 0.48 ± 0.45 | 0.41 | 0.65 | 41.51% | 64.15% | 79.25% | 90.57% |
| Holladay | − 0.22 ± 0.67 | 0.55 ± 0.43 | 0.50 | 0.70 | 30.19% | 50.94% | 77.36% | 83.02% |
| Kane | 0.09 ± 0.58 | 0.47 ± 0.36 | 0.47 | 0.59 | 35.85% | 52.83% | 79.25% | 96.23% |
| Karmona | 0.21 ± 0.59 | 0.47 ± 0.41 | 0.38 | 0.62 | 39.22% | 58.82% | 78.43% | 94.12% |
| LADAS | − 0.02 ± 1.11 | 0.60 ± 0.61 | 0.45 | 0.85 | 26.42% | 54.72% | 75.47% | 88.68% |
| PEARL-DGS | 0.37 ± 0.60 | 0.54 ± 0.45 | 0.48 | 0.70 | 32.00% | 52.00% | 74.00% | 86.00% |
| SRK_T | 0.04 ± 0.77 | 0.58 ± 0.50 | 0.50 | 0.76 | 30.19% | 52.83% | 69.81% | 84.91% |
BUII Barrett Universal II, D diopters, EVO Emmetropia Verifying Optical, IOL intraocular lens, ME mean prediction error, MAE mean absolute error, MedAE median absolute error, RMSAE root-mean-square absolute error, PE prediction error, SD standard deviation, CTR capsular tension ring
In the overall cohort, the BUII formula demonstrated the highest overall accuracy, yielding the lowest numerical MAE of 0.45 D and RMSAE of 0.56 D (Table 2). However, when evaluating the percentage of eyes within specific refractive targets, the Hoffer QST formula achieved the highest proportion within ± 0.25 D (41.51%) and ± 0.50 D (64.15%). For the broader target of ± 1.00 D, BUII was among the top-performing formulas (98.11%). Despite these numerical differences, no statistically significant differences were observed in the RMSAEs among the 12 formulas (Supplementary Table 1) or the percentage of eyes within ± 1.00 D (all P > 0.05), suggesting that several modern formulas perform comparably in this challenging population.
Subgroup Analysis: CTR vs. Non-CTR Groups
The predictive outcomes for the 12 formulas were further analyzed by comparing the CTR group (n = 23) and the non-CTR group (n = 30), as detailed in Table 3. In the CTR group, a statistically significant difference in the MAE was observed among the 12 formulas (P < 0.001, Kruskal‒Wallis test). The Kane formula had the lowest MAE (0.33 D) and RMSAE (0.41 D), closely followed by Cooke K6 (MAE 0.33 D, RMSAE 0.42 D) and Karmona (MAE 0.34 D, RMSAE 0.44 D) (Supplementary Table 2). In the non-CTR group, no statistically significant differences in the MAEs were found among the 12 formulas (P = 0.561, Kruskal‒Wallis test). Within this group, BUII had the lowest MAE (0.50 D) and RMSAE (0.63 D) among the newer generation of formulas, although the variability was generally greater (Supplementary Table 3).
Table 3.
Predictive outcomes for the 12 IOL calculation formulas between CTR and non-CTR groups
| Formula | CTR (n = 23) | Non-CTR (n = 30) | P value* | ||||
|---|---|---|---|---|---|---|---|
| ME ± SD | MAE | RMSAE | ME ± SD | MAE | RMSAE | ||
| BUII | − 0.12 ± 0.45 | 0.38 ± 0.26 | 0.46 | 0.11 ± 0.63 | 0.50 ± 0.39 | 0.63 | 0.332 |
| Cooke K6 | 0.06 ± 0.43 | 0.33 ± 0.27 | 0.42 | 0.35 ± 0.62 | 0.56 ± 0.43 | 0.70 | 0.024 |
| EVO 2.0 | − 0.06 ± 0.43 | 0.35 ± 0.25 | 0.43 | 0.30 ± 0.65 | 0.57 ± 0.43 | 0.70 | 0.040 |
| Haigis | − 0.12 ± 0.45 | 0.41 ± 0.21 | 0.46 | 0.14 ± 0.68 | 0.52 ± 0.45 | 0.68 | 0.865 |
| Hoffer Q | − 0.32 ± 0.47 | 0.48 ± 0.29 | 0.56 | − 0.04 ± 0.75 | 0.54 ± 0.51 | 0.73 | 0.726 |
| Hoffer QST | − 0.30 ± 0.40 | 0.42 ± 0.26 | 0.49 | 0.05 ± 0.76 | 0.52 ± 0.55 | 0.75 | 0.774 |
| Holladay | − 0.40 ± 0.52 | 0.58 ± 0.30 | 0.65 | − 0.09 ± 0.74 | 0.53 ± 0.52 | 0.74 | 0.146 |
| Kane | − 0.05 ± 0.41 | 0.33 ± 0.24 | 0.41 | 0.20 ± 0.68 | 0.57 ± 0.40 | 0.69 | 0.016 |
| Karmona | 0.07 ± 0.44 | 0.34 ± 0.28 | 0.44 | 0.10 ± 1.08 | 0.75 ± 0.77 | 1.07 | 0.061 |
| LADAS | − 0.20 ± 0.48 | 0.42 ± 0.30 | 0.51 | 0.11 ± 1.05 | 0.74 ± 0.74 | 1.04 | 0.104 |
| PEARL-DGS | 0.22 ± 0.45 | 0.38 ± 0.32 | 0.49 | 0.50 ± 0.68 | 0.57 ± 0.51 | 0.83 | 0.021 |
| SRK_T | − 0.18 ± 0.53 | 0.45 ± 0.32 | 0.55 | 0.22 ± 0.87 | 0.68 ± 0.59 | 0.89 | 0.202 |
BUII Barrett Universal II, D diopters, EVO Emmetropia Verifying Optical, IOL intraocular lens, ME mean prediction error, MAE mean absolute error, RMSAE root-mean-square absolute error, SD standard deviation, CTR capsular tension ring
*Comparison of MAE between the CTR and non-CTR groups, determined by the Mann–Whitney U test
The primary finding of our study was the significant positive impact of CTR implantation on refractive prediction accuracy. For several modern formulas, the MAE was significantly lower in the CTR group compared to the non-CTR group (Table 3, Supplementary Fig. 1). The most significant improvement was seen with the Cooke K6 (0.33 D vs. 0.56 D; P = 0.024), Kane (0.33 D vs. 0.57 D; P = 0.016), EVO 2.0 (0.35 D vs. 0.57 D; P = 0.040), and PEARL-DGS (0.38 D vs. 0.57 D; P = 0.021). While other formulas showed a similar trend of lower MAE in the CTR group, these differences did not reach statistical significance.
This improvement was also reflected in the percentage of eyes achieving target refraction (Fig. 2; Table 4). A significantly higher percentage of eyes in the CTR group achieved PEs within ± 0.50 D (P = 0.016), ± 0.75 D (P < 0.001), and ± 1.00 D (P < 0.001) compared to the non-CTR group. Furthermore, when specific formulas were evaluated, the Cooke K6, EVO 2.0, Kane, and PEARL-DGS formulas each showed a significantly higher percentage of eyes achieving a PE within ± 0.50 D in the CTR group compared to the non-CTR group (all P < 0.05). To ensure the robustness of our findings, a sensitivity analysis including only one eye per patient (n = 38) was conducted. The detailed results are presented in Supplementary Fig. 2 and Supplementary Table 4. This analysis confirmed the primary finding that CTR implantation was associated with improved refractive accuracy.
Fig. 2.
The percentages of eyes with PE of ± 0.25 D, ± 0.50 D, ± 0.75 D and ± 1.00 D among 12 IOL calculation formulas in CTR and non-CTR groups. BUII Barrett Universal II, EVO Emmetropia Verifying Optical, CTR capsular tension ring, IOL intraocular lens, PE prediction error. *P value < 0.05, Fisher’s exact test on the PE of ± 0.50 D between the two groups
Table 4.
The percentages of eyes with refractive prediction errors of ± 0.25 D, ± 0.50 D, ± 0.75 D and ± 1.00 D among 12 IOL calculation formulas in the CTR and non-CTR groups
| Formula | CTR (n = 23) | Non-CTR (n = 30) | ||||||
|---|---|---|---|---|---|---|---|---|
| Percentage of eyes within different PE (%) | ||||||||
| ± 0.25D | ± 0.50D | ± 0.75D | ± 1.00D | ± 0.25D | ± 0.50D | ± 0.75D | ± 1.00D | |
| BUII | 43.48% | 69.57% | 86.96% | 100.00% | 33.33% | 53.33% | 80.00% | 96.67% |
| EVO 2.0 | 52.17% | 69.57% | 86.96% | 100.00% | 20.00% | 46.67% | 76.67% | 93.33% |
| Kane | 47.83% | 73.91% | 91.30% | 100.00% | 26.67% | 36.67% | 70.00% | 93.33% |
| PEARL-DGS | 39.13% | 65.22% | 86.96% | 91.30% | 25.00% | 39.29% | 60.71% | 78.57% |
| Cooke K6 | 39.13% | 78.26% | 95.65% | 95.65% | 24.14% | 44.83% | 68.97% | 96.55% |
| Karmona | 56.52% | 78.26% | 86.96% | 100.00% | 46.43% | 64.29% | 71.43% | 82.14% |
| LADAS | 34.78% | 65.22% | 82.61% | 95.65% | 20.00% | 46.67% | 70.00% | 83.33% |
| Hoffer QST | 39.13% | 56.52% | 86.96% | 100.00% | 43.33% | 70.00% | 73.33% | 83.33% |
| Haigis | 26.09% | 60.87% | 91.30% | 100.00% | 36.67% | 60.00% | 76.67% | 83.33% |
| Holladay | 17.39% | 34.78% | 82.61% | 86.96% | 40.00% | 63.33% | 73.33% | 80.00% |
| SRK_T | 34.78% | 65.22% | 78.26% | 95.65% | 26.67% | 43.33% | 63.33% | 76.67% |
| HofferQ | 17.39% | 56.52% | 78.26% | 91.30% | 43.33% | 60.00% | 70.00% | 76.67% |
BUII Barrett Universal II, D diopters, EVO Emmetropia Verifying Optical, IOL intraocular lens, PE prediction error, CTR capsular tension ring
Influence of the Lens Thickness on the Prediction Error
The ME for each formula was evaluated across the subgroups on the basis of the LT, stratified by the 25th, 50th, and 75th percentiles of LT, as shown in Fig. 3. A general trend toward a more hyperopic ME with increasing LT was observed for all the formulas. The variability in ME among the formulas also increased substantially with increasing LT. The BUII formula demonstrated relatively stable MEs across the different LT quartiles. In the thinnest LT subgroup (≤ 4.615 mm), most formulas presented an ME close to zero or a slight myopic shift. Notably, in the thickest LT subgroup (> 5.1 mm), the PEARL-DGS formula demonstrated the largest hyperopic ME (exceeding + 0.82 D) (Supplementary Table 5).
Fig. 3.
Mean prediction error (in diopters, D) of each formula was evaluated within subgroups of lens thickness (LT, in millimeters). Data were stratified into four groups using the 25th, 50th, and 75th percentiles of LT as cutoffs. BUII Barrett Universal II, EVO Emmetropia Verifying Optical
Discussion
The aim of this study was to evaluate the predictive accuracy of 12 IOL power calculation formulas in patients with RP-complicated cataracts and evaluate the impact of CTR implantation on refractive outcomes. Our findings indicate that the BUII formula demonstrated the highest calculation accuracy among all tested formulas. Furthermore, the use of a CTR significantly enhances the predictability of calculations performed with certain formulas and improves overall refractive precision.
Despite numerous investigational treatment modalities, including gene-dependent strategies and stem cell therapy, no consistently effective treatment currently exists to reverse the course of RP [16, 17]. For patients with RP who develop significant cataracts, surgical intervention can improve vision and enhance their quality of life. Many previous studies have demonstrated that new-generation IOL formulas, such as BU II, EVO 2.0, Kane, and RBF 3.0, offer significantly improved predictive accuracy, even in patients with challenging eyes, including those post-vitrectomy, those with silicone oil tamponade, and those who have previously received refractive surgery [18–20]. However, research specifically addressing the predictive accuracy of IOL formulas in the RP patient population is lacking. In the overall cohort analysis of the present study, the BUII formula demonstrated the closest ME to emmetropia and the lowest numerical MAE and RMSAE. Although numerous studies have established BUII as a highly accurate formula across various ALs and ocular parameters [21–23], our results indicate that its accuracy was still lower compared to that reported in other studies on the accuracy of new-generation IOL power calculation in routine patients with cataract [24–26]. This further highlights the challenges associated with IOL power calculation in patients with RP. Nevertheless, it is crucial to note that in our cohort, no statistically significant differences in the RMSAE value were found among the 12 formulas. This lack of statistical distinction, despite numerical variations, may be attributable to the relatively modest sample size, which could limit the power to detect subtle but potentially clinically relevant differences. The Hoffer QST formula calculated the highest percentage of eyes within ± 0.50 D, suggesting it is useful for achieving clinically acceptable outcomes, although its ME tended toward a slight myopic shift. The tendency of traditional formulas such as Haigis, Hoffer Q, and Holladay to predict myopia and newer formulas such as Cooke K6, EVO 2.0, and Kane to predict hyperopia in our cohort reflects known characteristics that often require surgeon-specific constant optimization [27–29].
The most compelling finding of this study is the significant improvement in predictive accuracy associated with CTR implantation. For formulas such as Cooke K6, EVO 2.0, Kane, and PEARL-DGS, the MAE was significantly lower in the CTR group than in the non-CTR group. Moreover, the CTR group presented a significantly greater proportion of eyes that achieved PEs within ± 0.50 D, ± 0.75 D, and ± 1.00 D. These findings suggest that CTRs contribute to a more stable and predictable effective lens position (ELP). Research regarding the impact of CTR implantation on refractive outcomes in specific cataract types has yielded mixed results, and a clear consensus has not been reached. For example, some findings suggest that implanting CTRs does not consistently affect refractive outcomes compared with routine phacoemulsification [30–32]. In contrast, another study recommended the use of CTRs during cataract surgery in the eyes of patients with extremely high myopia to achieve more accurate refractive outcomes [33]. The mechanism may involve improved capsular bag stability, reduced IOL tilt or decentration, and a more consistent anteroposterior IOL position, particularly in the eyes of patients that might have subtle zonular weakness or large capsular bags where the IOL position could be less predictable [34–36]. The observation that the MAE significantly differed among the formulas in the CTR group (with Kane, Cooke K6, and Karmona performing best) but not in the non-CTR group, further underscores that a stable capsular environment allows the inherent strengths of certain formulas to become more apparent. In contrast, the greater variability in the non-CTR group might mask these differences. To mitigate inter-eye correlation, we performed a sensitivity analysis including only one eye per patient. This analysis confirmed our primary finding that CTR use is associated with improved refractive predictability, thereby strengthening our conclusion. The slight variation in which specific formulas achieved statistical significance between the primary and sensitivity analyses likely reflects the reduced statistical power of the smaller sample size, and suggests that the benefit of CTR may be a general stabilizing effect rather than one specific to a single formula’s algorithm. These findings provide a strong rationale for considering CTR use in patients with RP not only for standard indications but also to enhance refractive predictability.
It has been established in previous studies that RP-associated cataracts can lead to structural ocular alterations, with increased LT being a prominent feature [13]. Our analysis of LT revealed a general trend towards a more hyperopic ME with increasing LT for all the formulas, accompanied by increased variability in prediction errors. This finding is consistent with the findings of a previous study, which reported a myopic shift with thin lenses and a hyperopic shift with thick lenses [37]. The BUII and Kane formulas appeared relatively more stable across the LT quartiles, suggesting that they may better account for variations in LT. The pronounced myopic ME of Hoffer QST in the 4.86–5.1 mm LT group and the significant hyperopic ME of PEARL-DGS in the thickest LT group (> 5.1 mm) highlight the need for caution when these formulas are used for the eyes of patients with extreme LT values. Surgeons should be aware of these trends and may consider adjusting their target refraction or selecting formulas known for better performance in such cases. The underlying reason for the hyperopic shift with thicker lenses could be related to the anterior displacement of the IOL’s optical center relative to the predicted ELP, especially given that patients with RP are more likely to present with zonular weakness [10].
This study has several strengths, including the direct comparison of 12 IOL formulas and the focused analysis of the impact of CTRs and LTs. The present study has several limitations, including its retrospective, non-randomised design and the inclusion of bilateral data. This study is its relatively small sample size; however, this is comparable to sample sizes reported in previous case series [10, 38, 39]. Furthermore, due to our limited sample size and the heterogeneity of IOLs used, a study-specific optimization was not feasible. Additionally, the decision to implant a CTR was made intraoperatively based on the surgeon’s judgment of zonular weakness, which may have introduced selection bias. Future research should focus on larger, prospective multicenter studies to validate these findings, particularly the benefits of CTRs on refractive predictability with various modern IOL formulas.
Conclusions
In conclusion, for patients with RP, this study suggests that while many modern IOL formulas offer good predictive accuracy, the implantation of a CTR was associated with significantly improved predictive accuracy for several modern IOL formulas, leading to more precise refractive outcomes. The BUII formula achieved the best calculation accuracy among all formulas evaluated in our study, particularly with varying LTs. Clinicians managing cataracts in patients with RP should consider the potential benefits of CTRs for improving refractive predictability and be mindful of the influence of LT when selecting IOL formulas and planning target refractions.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the participants of the study.
Author Contributions
Conceptualization: Siyan Li; Methodology: Dandan Wang, Yinying Zhao, and Er Mo; Formal analysis and investigation: Qin Chu, Ke Feng, Jiaying Wu, and Hongzhe Li; Writing—original draft preparation: Siyan Li, Dandan Wang, and Qin Chu; Writing—review and editing: Siyan Li, Pingjun Chang and Yun-e Zhao; Funding acquisition: Siyan Li and Yun-e Zhao.
Funding
This research was supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ24H120004), Wenzhou Science and Technology Research Project (Grant No. Y2023174), National Natural Science Foundation of China (82371042), and the Zhejiang Medical Health Science and Technology Project (Grant No. 2024KY1292). The journal’s Rapid Service Fee was funded by the authors.
Data Availability
No data are available. All data generated or analyzed during this study are included in this article.
Declarations
Conflict of Interest
Siyan Li, Dandan Wang, Qin Chu, Ke Feng, Jiaying Wu, Yinying Zhao, Er Mo, Hongzhe Li, Pingjun Chang, and Yun-e Zhao have no financial or other conflicts of interest to disclose. Ke Feng’s current affiliation is the Department of Ophthalmology, Southern University of Science and Technology Hospital, Shenzhen, China.
Ethics Approval
This study was performed in accordance with the tenets of the Declaration of Helsinki and approved by the Ethics Committee of the Eye Hospital of Wenzhou Medical University (H2025-027-K-26). As the study utilized data obtained from routine clinical practice and did not involve any interventions, human biological specimens, or sensitive personal information, individual informed consent was waived in accordance with the Ethical Guidelines of the Ethics Committee of the Eye Hospital of Wenzhou Medical University.
Footnotes
Pingjun Chang and Yun-e Zhao: Joint corresponding authors.
Prior Presentation: This work was presented at the 29th Congress of the Chinese Ophthalmological Society, held in Hangzhou, China, on September 5, 2025.
Siyan Li, Dandan Wang, and Qin Chu contributed equally as first authors.
Contributor Information
Pingjun Chang, Email: cpj@eye.ac.cn.
Yun-e Zhao, Email: zye@eye.ac.cn, Email: zyehzeye@126.com.
References
- 1.Hartong DT, Berson EL, Dryja TP. Retinitis pigmentosa. Lancet. 2006;368(9549):1795–809. [DOI] [PubMed] [Google Scholar]
- 2.Karti O, Saatci AO. Cataract surgery in retinitis pigmentosa. Med Hypothesis Discov Innov Ophthalmol. 2024;13(2):96–103. 10.51329/mehdiophthal1500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Nguyen XT, Thiadens A, Fiocco M, et al. Outcome of cataract surgery in patients with retinitis pigmentosa. Am J Ophthalmol. 2023;246:1–9. 10.1016/j.ajo.2022.10.001. [DOI] [PubMed] [Google Scholar]
- 4.Jackson H, Garway-Heath D, Rosen P, Bird AC, Tuft SJ. Outcome of cataract surgery in patients with retinitis pigmentosa. Br J Ophthalmol. 2001;85(8):936–8. 10.1136/bjo.85.8.936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Fishman GA, Anderson RJ, Lourenco P. Prevalence of posterior subcapsular lens opacities in patients with retinitis pigmentosa. Br J Ophthalmol. 1985;69(4):263–6. 10.1136/bjo.69.4.263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Yoshida N, Ikeda Y, Murakami Y, et al. Factors affecting visual acuity after cataract surgery in patients with retinitis pigmentosa. Ophthalmology. 2015;122(5):903–8. 10.1016/j.ophtha.2014.12.003. [DOI] [PubMed] [Google Scholar]
- 7.Bayyoud T, Bartz-Schmidt KU, Yoeruek E. Long-term clinical results after cataract surgery with and without capsular tension ring in patients with retinitis pigmentosa: a retrospective study. BMJ Open. 2013. 10.1136/bmjopen-2013-002616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hong Y, Li H, Sun Y, Ji Y. A review of complicated cataract in retinitis pigmentosa: pathogenesis and cataract surgery. J Ophthalmol. 2020;2020:6699103. 10.1155/2020/6699103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Georgiou M, Shakarchi AF, Elhusseiny AM, Michaelides M, Sallam AB. Cataract surgery outcomes in retinitis pigmentosa: a comparative clinical database study. Am J Ophthalmol. 2024;262:34–9. 10.1016/j.ajo.2024.01.037. [DOI] [PubMed] [Google Scholar]
- 10.Dikopf MS, Chow CC, Mieler WF, Tu EY. Cataract extraction outcomes and the prevalence of zonular insufficiency in retinitis pigmentosa. Am J Ophthalmol. 2013;156(1):82-88.e2. 10.1016/j.ajo.2013.02.002. [DOI] [PubMed] [Google Scholar]
- 11.Hong Y, Li H, Sun Y, Ji Y. A review of complicated cataract in retinitis pigmentosa: pathogenesis and cataract surgery. J Ophthalmol. 2020;2020(1):6699103. 10.1155/2020/6699103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Chatterjee S, Agrawal D, Agrawal D, Parchand SM, Sahu A. Cataract surgery in retinitis pigmentosa. Indian J Ophthalmol. 2021;69(7):1753–7. 10.4103/ijo.IJO_2916_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Iida M, Masuda Y, Ohira R, et al. Ocular shape of cataract with retinitis pigmentosa: case–control study. J Cataract Refract Surg. 2025;51(3):229–35. 10.1097/j.jcrs.0000000000001582. [DOI] [PubMed] [Google Scholar]
- 14.Chen CX, Wang JD, Zhang JS, et al. Effect of lens capsular tension ring on preventing capsular contraction syndrome in the surgery of retinitis pigmentosa combined with cataract: retrospective case series. Int J Clin Pract. 2021;75(8):e14272. 10.1111/ijcp.14272. [DOI] [PubMed] [Google Scholar]
- 15.Hoffer KJ, Savini G. Update on intraocular lens power calculation study protocols: the better way to design and report clinical trials. Ophthalmology. 2021;128(11):e115–20. 10.1016/j.ophtha.2020.07.005. [DOI] [PubMed] [Google Scholar]
- 16.Nguyen X-T-A, Moekotte L, Plomp AS, Bergen AA, van Genderen MM, Boon CJF. Retinitis pigmentosa: current clinical management and emerging therapies. Int J Mol Sci. 2023;24(8):7481. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Verbakel SK, van Huet RAC, Boon CJF, et al. Non-syndromic retinitis pigmentosa. Prog Retin Eye Res. 2018;66:157–86. 10.1016/j.preteyeres.2018.03.005. [DOI] [PubMed] [Google Scholar]
- 18.Xia T, Martinez CE, Tsai LM. Update on intraocular lens formulas and calculations. Asia Pac J Ophthalmol (Phila). 2020;9(3):186–93. 10.1097/apo.0000000000000293. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ferrara S, Crincoli E, Savastano A, Savastano MC, Catania F, Rizzo S. Refractive outcomes with new generation formulas for intraocular lens power calculation in radial keratotomy patients. Cornea. 2024;43(2):178–83. 10.1097/ico.0000000000003301. [DOI] [PubMed] [Google Scholar]
- 20.Zhang J, Wang W, Liu Z, et al. Accuracy of new-generation intraocular lens calculation formulas in eyes undergoing combined silicone oil removal and cataract surgery. J Cataract Refract Surg. 2021;47(5):593–8. 10.1097/j.jcrs.0000000000000509. [DOI] [PubMed] [Google Scholar]
- 21.Savini G, Di Maita M, Hoffer KJ, et al. Comparison of 13 formulas for IOL power calculation with measurements from partial coherence interferometry. Br J Ophthalmol. 2021;105(4):484–9. 10.1136/bjophthalmol-2020-316193. [DOI] [PubMed] [Google Scholar]
- 22.Yoon JH, Whang WJ. Comparison of accuracy of six modern intraocular lens power calculation formulas. Korean J Ophthalmol. 2023;37(5):380–6. 10.3341/kjo.2023.0034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kane JX, Van Heerden A, Atik A, Petsoglou C. Accuracy of 3 new methods for intraocular lens power selection. J Cataract Refract Surg. 2017;43(3):333–9. 10.1016/j.jcrs.2016.12.021. [DOI] [PubMed] [Google Scholar]
- 24.Melles RB, Holladay JT, Chang WJ. Accuracy of intraocular lens calculation formulas. Ophthalmology. 2018;125(2):169–78. 10.1016/j.ophtha.2017.08.027. [DOI] [PubMed] [Google Scholar]
- 25.Kane JX, Van Heerden A, Atik A, Petsoglou C. Intraocular lens power formula accuracy: comparison of 7 formulas. J Cataract Refract Surg. 2016;42(10):1490–500. 10.1016/j.jcrs.2016.07.021. [DOI] [PubMed] [Google Scholar]
- 26.Darcy K, Gunn D, Tavassoli S, Sparrow J, Kane JX. Assessment of the accuracy of new and updated intraocular lens power calculation formulas in 10,930 eyes from the UK National Health Service. J Cataract Refract Surg. 2020;46(1):2–7. 10.1016/j.jcrs.2019.08.014. [DOI] [PubMed] [Google Scholar]
- 27.Ma S, Li C, Sun J, et al. Comparative analysis of eighteen IOL power calculation formulas using a modified formula performance index across diverse biometric parameters. Am J Ophthalmol. 2025;273:221–30. 10.1016/j.ajo.2025.02.035. [DOI] [PubMed] [Google Scholar]
- 28.Buonsanti D, Cooke DL, Hoffer KJ, et al. A novel method to optimize personal IOL constants. Am J Ophthalmol. 2025;269:355–61. 10.1016/j.ajo.2024.08.029. [DOI] [PubMed] [Google Scholar]
- 29.Langenbucher A, Hoffmann P, Cayless A, Wendelstein J, Szentmáry N. Limitations of constant optimization with disclosed intraocular lens power formulae. J Cataract Refract Surg. 2024;50(3):201–8. 10.1097/j.jcrs.0000000000001337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Saadet GI, Fatih Ö. Effect of the capsular tension ring on refractive outcome after phacoemulsification. Rom J Ophthalmol. 2021;65(1):59–63. 10.22336/rjo.2021.11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Schild AM, Rosentreter A, Hellmich M, Lappas A, Dinslage S, Dietlein TS. Effect of a capsular tension ring on refractive outcomes in eyes with high myopia. J Cataract Refract Surg. 2010;36(12):2087–93. 10.1016/j.jcrs.2010.06.065. [DOI] [PubMed] [Google Scholar]
- 32.Boomer JA, Jackson DW. Effect of the Morcher capsular tension ring on refractive outcome. J Cataract Refract Surg. 2006;32(7):1180–3. 10.1016/j.jcrs.2006.06.009. [DOI] [PubMed] [Google Scholar]
- 33.Zhao HY, Zhang JS, Li M, Chen DJ, Wan XH. Effect of capsular tension ring on the refractive outcomes of patients with extreme high axial myopia after phacoemulsification. Eur J Med Res. 2024;29(1):142. 10.1186/s40001-024-01726-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lee DH, Shin SC, Joo CK. Effect of a capsular tension ring on intraocular lens decentration and tilting after cataract surgery. J Cataract Refract Surg. 2002;28(5):843–6. 10.1016/s0886-3350(01)01174-9. [DOI] [PubMed] [Google Scholar]
- 35.Gimbel HV, Sun R. Role of capsular tension rings in preventing capsule contraction. J Cataract Refract Surg. 2000;26(6):791–2. 10.1016/s0886-3350(00)00505-8. [DOI] [PubMed] [Google Scholar]
- 36.Chang P, Hu Y, Wu X, et al. Influence of capsular tension rings on the IOL-capsule complex in patients with long axial length: a clinical observation based on SS-OCT. J Refract Surg. 2024;40(9):e654–61. 10.3928/1081597x-20240723-02. [DOI] [PubMed] [Google Scholar]
- 37.Hipólito-Fernandes D, Luís ME, Serras-Pereira R, et al. Anterior chamber depth, lens thickness and intraocular lens calculation formula accuracy: nine formulas comparison. Br J Ophthalmol. 2022;106(3):349–55. 10.1136/bjophthalmol-2020-317822. [DOI] [PubMed] [Google Scholar]
- 38.Chan TCY, Lam SC, Mohamed S, Wong RLM. Survival analysis of visual improvement after cataract surgery in advanced retinitis pigmentosa. Eye Lond. 2017;31(12):1747–8. 10.1038/eye.2017.164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Nakamura Y, Mitamura Y, Hagiwara A, et al. Relationship between retinal microstructures and visual acuity after cataract surgery in patients with retinitis pigmentosa. Br J Ophthalmol. 2015;99(4):508–11. 10.1136/bjophthalmol-2013-304819. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No data are available. All data generated or analyzed during this study are included in this article.