Cataract remains one of the leading causes of avoidable blindness and visual impairment worldwide, particularly among the aging population. Cataract extraction with intraocular lens (IOL) implantation has become one of the most commonly performed ophthalmic procedures and has evolved from a vision-restoring surgery to a refractive procedure aimed at achieving excellent visual quality and spectacle independence.[1] With advancements in phacoemulsification techniques, foldable IOL designs, and surgical precision, patient expectations regarding postoperative refractive outcomes have increased substantially. Therefore, accurate prediction of the postoperative refractive state has become a crucial component of successful cataract surgery.[2]The accuracy of IOL power calculation is largely dependent on precise preoperative assessment of ocular biometric parameters. Even minor errors in biometric measurements can result in significant postoperative refractive errors, including residual myopia, hyperopia, or astigmatism, which may affect patient satisfaction despite an otherwise uncomplicated surgical procedure.[3] Accurate evaluation of parameters such as axial length (AL), keratometric values (K), anterior chamber depth (ACD), lens thickness (LT), central corneal thickness (CCT), and white-to-white (WTW) corneal diameter is therefore essential for achieving predictable refractive outcomes.[4]

Optical biometry has emerged as the preferred technique for preoperative ocular measurement due to its non-contact nature, superior precision, and excellent reproducibility compared with conventional ultrasound-based methods.[5,6] Modern optical biometers based on partial coherence interferometry and optical low-coherence reflectometry provide comprehensive assessment of multiple ocular parameters required for contemporary IOL power calculation formulas. Devices such as the Lenstar LS900 allow measurement of axial length, keratometry, anterior chamber depth, lens thickness, corneal diameter, and other biometric variables with high repeatability, thereby improving the accuracy of IOL power prediction.[7,]Among various biometric parameters, axial length is considered one of the most influential determinants of refractive accuracy because small measurement errors can significantly alter the calculated IOL power, particularly in eyes with short or long axial lengths. Keratometric measurements are equally important as corneal curvature contributes significantly to the total refractive power of the eye.[8] Furthermore, variations in anterior chamber depth, lens thickness, and effective lens position prediction may influence postoperative refractive results, especially with newer generation IOL calculation formulas such as SRK/T and Barrett Universal II.[9]Despite continuous improvements in optical biometry technology and IOL calculation algorithms, postoperative refractive surprises continue to occur due to individual variations in ocular anatomy, measurement limitations, differences between predicted and actual effective lens position, and errors in formula-based calculations.[10-12] Dense cataracts, poor fixation, and inadequate optical signal acquisition may also affect measurement reliability and potentially influence final refractive outcomes.Understanding the relationship between preoperative optical biometry parameters and postoperative refractive results is essential for optimizing IOL selection, improving surgical planning, and enhancing patient satisfaction. Therefore, the present study, “Correlation Between Preoperative Optical Biometry Parameters and Refractive Outcomes After Cataract Surgery,” aims to evaluate the association between preoperative biometric measurements and postoperative refractive outcomes following cataract extraction with IOL implantation, and to identify factors influencing the accuracy of refractive prediction.

The present study was conducted as a prospective observational study to evaluate the correlation between preoperative optical biometry parameters and postoperative refractive outcomes after cataract surgery. The study was carried out in the Department of Ophthalmology at a tertiary care hospital. The study included patients undergoing cataract extraction with intraocular lens (IOL) implantation during the study period. Study Population A total of 110 patients with age-related cataract who underwent routine cataract surgery with posterior chamber intraocular lens implantation were included in the study. All enrolled participants underwent preoperative optical biometry assessment followed by postoperative refractive evaluation. Inclusion Criteria Patients fulfilling the following criteria were included: • Patients diagnosed with age-related cataract requiring cataract surgery. • Patients undergoing uncomplicated phacoemulsification with posterior chamber IOL implantation. • Patients with available preoperative optical biometry measurements. • Patients willing to participate and provide written informed consent. • Patients available for postoperative follow-up and refractive assessment. Exclusion Criteria Patients were excluded if they had: • Previous ocular surgery or trauma. • Coexisting corneal pathology affecting visual outcomes. • Glaucoma, retinal diseases, macular pathology, or optic nerve disorders. • Complicated cataract surgery or intraoperative complications. • Irregular astigmatism or significant ocular surface disease. • Inability to obtain reliable optical biometry measurements. Preoperative Evaluation All patients underwent detailed ophthalmological evaluation before surgery. The assessment included demographic data collection, detailed ocular history, visual acuity assessment, slit-lamp examination, intraocular pressure measurement, and fundus evaluation whenever possible. Preoperative optical biometry was performed using an optical biometer, and the following parameters were recorded: • Axial length (AL) • Keratometric values (K1 and K2) • Mean keratometry (Km) • Anterior chamber depth (ACD) • Lens thickness (LT) • Central corneal thickness (CCT) • White-to-white corneal diameter (WTW) The measured biometric parameters were used for IOL power calculation using standard modern IOL calculation formulas. Surgical Procedure All patients underwent standard phacoemulsification cataract surgery with implantation of a foldable posterior chamber intraocular lens. The same surgical protocol was followed for all patients to minimize procedural variability. Cases with intraoperative complications were excluded from final analysis. Postoperative Assessment Patients were followed up postoperatively at scheduled intervals. Uncorrected visual acuity, best-corrected visual acuity, and manifest refraction were assessed after stabilization of the refractive status. The postoperative spherical equivalent (SE) was calculated and compared with the predicted refractive outcome. The refractive outcome was evaluated using: • Mean absolute prediction error (MAE) • Absolute refractive error • Difference between predicted and achieved spherical equivalent Study Variables and Outcome Measures The primary outcome measure was the correlation between preoperative optical biometry parameters and postoperative refractive outcomes. The association between the following parameters and refractive accuracy was assessed: • Axial length • Keratometric values • Anterior chamber depth • Lens thickness • White-to-white diameter The postoperative refractive outcome was considered the dependent variable. Statistical Analysis Data collected during the study were entered into a structured database and analyzed using SPSS.21statistical software. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequency and percentage.The correlation between preoperative biometric parameters and postoperative refractive outcomes was assessed using Pearson’s correlation coefficient or Spearman’s correlation analysis depending on data distribution. The association between biometric parameters and refractive prediction error was evaluated using appropriate statistical tests.A p-value <0.05 was considered statistically significant.

A total of 110 patients undergoing cataract surgery with intraocular lens implantation were included in the present study. The baseline demographic and clinical characteristics of the study participants are summarized in Table 1. The majority of patients belonged to the 61–70 years age group (38.2%), followed by 51–60 years (30.9%), with a mean age of 62.4 ± 8.6 years. Males constituted 52.7% of the study population, while females accounted for 47.3%. Among the different types of cataract, nuclear cataract was the most common (56.4%), followed by cortical cataract (25.5%) and posterior subcapsular cataract (18.1%). The distribution of operated eyes was nearly equal, with the right eye involved in 50.9% and the left eye in 49.1% of cases (Table 1).Preoperative optical biometry assessment demonstrated that the mean axial length of the study participants was 23.48 ± 1.12 mm. The mean keratometry value was 44.18 ± 1.52 D, while the mean anterior chamber depth was 3.12 ± 0.38 mm. The mean lens thickness, central corneal thickness, and white-to-white corneal diameter were 4.42 ± 0.46 mm, 538.6 ± 31.4 µm, and 11.72 ± 0.42 mm, respectively. The detailed distribution of preoperative optical biometric parameters is presented in Table 2. The graphical representation of these biometric parameters is shown in Figure 1.Postoperative refractive outcomes after cataract surgery are summarized in Table 3. A total of 38 patients (34.5%) achieved a postoperative spherical equivalent within ±0.25 D, while 82 patients (74.5%) achieved accuracy within ±0.50 D. Refractive accuracy within ±1.00 D was achieved in 94.5% of patients, whereas only 5.5% had a refractive error greater than ±1.00 D. The mean absolute prediction error was 0.42 ± 0.36 D. Regarding final refractive outcomes, emmetropic results were achieved in 78.2% of cases, while residual myopia and residual hyperopia were observed in 12.7% and 9.1% of patients, respectively (Table 3).Correlation analysis between preoperative optical biometry parameters and postoperative refractive error demonstrated significant associations with multiple biometric variables (Table 4). Axial length showed a significant negative correlation with postoperative refractive error (r = -0.46, p<0.001). Mean keratometry demonstrated a significant positive correlation (r = 0.32, p=0.001). Anterior chamber depth showed a negative correlation (r = -0.28, p=0.003), whereas lens thickness showed a positive correlation with refractive error (r = 0.24, p=0.011). White-to-white corneal diameter demonstrated a weak but statistically significant negative correlation (r = -0.18, p=0.049). The graphical representation of correlations between biometric parameters and postoperative refractive error is depicted in Figure 2.When refractive prediction accuracy was analyzed according to axial length categories, patients with normal axial length (22–25 mm) demonstrated the lowest mean absolute prediction error (0.36 ± 0.28 D). Short eyes (<22 mm) showed higher prediction error (0.68 ± 0.42 D), while long eyes (>25 mm) also demonstrated increased error (0.59 ± 0.38 D). The difference in prediction error among axial length groups was statistically significant (p=0.002) (Table 5).Further analysis was performed to identify factors associated with higher postoperative refractive error (>0.50 D). Patients with refractive error >0.50 D had significantly different axial length, anterior chamber depth, lens thickness, and white-to-white diameter values compared with those achieving refractive accuracy ≤0.50 D (Table 6). Axial length was significantly lower in patients with higher refractive error (23.08 ± 1.24 mm vs 23.62 ± 1.04 mm, p=0.031). Reduced anterior chamber depth was significantly associated with increased refractive error (2.94 ± 0.41 mm vs 3.18 ± 0.36 mm, p=0.006). Increased lens thickness was also associated with higher refractive error (4.61 ± 0.51 mm vs 4.36 ± 0.42 mm, p=0.018). White-to-white diameter showed a significant association (p=0.048), whereas mean keratometry did not show a statistically significant difference (p=0.168) (Table 6). The factors associated with higher postoperative refractive error are graphically represented in Figure 3.

Table 1: Baseline Demographic and Clinical Characteristics of Study Participants (n=110)

Table 2: Distribution of Preoperative Optical Biometry Parameters Among Study Participants (n=110)

Figure 1 Distribution of Preoperative Optical Biometry Parameters Among Study Participants (n=110)

Table 3: Distribution of Postoperative Refractive Outcomes After Cataract Surgery (n=110)

Table 4: Correlation Between Preoperative Optical Biometry Parameters and Postoperative Refractive Error (n=110)

Statistical test applied: Pearson correlation analysis

Figure 2 Correlation Between Preoperative Optical Biometry Parameters and Postoperative Refractive Error (n=110)

Table 5: Association Between Axial Length Categories and Refractive Prediction Accuracy (n=110)

Table 6: Factors Associated with Higher Postoperative Refractive Error (>0.50 D) (n=110)

Figure 3 Factors Associated with Higher Postoperative Refractive Error (>0.50 D) (n=110)

The present prospective observational study evaluated the association between preoperative optical biometry parameters and postoperative refractive outcomes after cataract surgery among 110 patients. The study demonstrated that accurate assessment of ocular biometric variables, including axial length, keratometry, anterior chamber depth, lens thickness, and white-to-white diameter, significantly influenced postoperative refractive predictability. Overall, 74.5% of patients achieved refractive accuracy within ±0.50 D, with a mean absolute prediction error of 0.42 ± 0.36 D, indicating good accuracy of modern optical biometry-based intraocular lens (IOL) power calculation.The mean age of participants was 62.4 ± 8.6 years, with the majority belonging to the 61–70 years age group (38.2%), and males comprising 52.7% of cases. Nuclear cataract was the predominant cataract type (56.4%). These findings reflect the typical demographic pattern of age-related cataract populations, where progressive anatomical changes in the crystalline lens and ocular structures necessitate precise biometric assessment for optimal refractive outcomes.The present study recorded a mean axial length of 23.48 ± 1.12 mm, mean keratometry of 44.18 ± 1.52 D, and mean anterior chamber depth of 3.12 ± 0.38 mm. Similar biometric profiles have been reported in previous studies evaluating optical biometry for cataract surgery. Akalın et al.[13] assessed optical low-coherence reflectometry-based measurements and demonstrated the clinical utility of parameters such as axial length, keratometry, anterior chamber depth, and lens thickness for accurate IOL power prediction. The high reproducibility and precision of optical biometers have contributed significantly to improved refractive outcomes.In the current study, 78.2% of patients achieved emmetropic outcomes, while residual myopia and hyperopia were observed in 12.7% and 9.1%, respectively. The mean absolute prediction error of 0.42 ± 0.36 D was comparable with large registry-based studies, including the European Registry of Quality Outcomes for Cataract and Refractive Surgery, which reported approximately 72.7% of eyes within ±0.50 D and 93.0% within ±1.00 D. Hussain et al.[14] similarly reported satisfactory refractive predictability following cataract surgery using optical biometry, highlighting the importance of accurate biometric measurements.Axial length showed a significant negative correlation with postoperative refractive error (r = -0.46, p<0.001) in the present study, emphasizing its critical role in IOL power calculation. Even minor errors in axial length measurement can produce clinically significant refractive deviations. Mean keratometry demonstrated a significant positive correlation with refractive error (r = 0.32, p=0.001), confirming the importance of precise corneal power estimation. Similarly, anterior chamber depth showed a significant negative correlation (r = -0.28, p=0.003), reflecting the influence of effective lens position prediction on postoperative refraction.Patients with normal axial length (22–25 mm) demonstrated the lowest mean absolute prediction error (0.36 ± 0.28 D), whereas short eyes and long eyes showed higher errors (0.68 ± 0.42 D and 0.59 ± 0.38 D, respectively; p=0.002). These findings support previous observations that extreme axial lengths remain challenging for accurate IOL power calculation due to limitations in effective lens position estimation and formula performance.Additionally, patients with postoperative refractive error >0.50 D had significantly shorter axial length (23.08 ± 1.24 mm vs 23.62 ± 1.04 mm; p=0.031), reduced anterior chamber depth (2.94 ± 0.41 mm vs 3.18 ± 0.36 mm; p=0.006), and increased lens thickness (4.61 ± 0.51 mm vs 4.36 ± 0.42 mm; p=0.018). These findings highlight the influence of individual ocular anatomical variations on refractive outcomes.

The present study demonstrated that preoperative optical biometry parameters significantly influenced postoperative refractive outcomes after cataract surgery. Axial length, keratometry, anterior chamber depth, lens thickness, and white-to-white diameter showed important associations with refractive accuracy. Accurate optical biometric assessment enabled predictable IOL power calculation, with the majority of patients achieving refractive outcomes within acceptable limits. These findings highlight the importance of comprehensive biometric evaluation and individualized IOL planning to improve refractive predictability and patient satisfaction following cataract surgery.

Limitations

The study was conducted with a relatively limited sample size of 110 patients and from a single tertiary care centre, which may restrict the generalizability of the findings. The study did not include long-term assessment of refractive stability and patient-reported visual satisfaction. Variations related to different IOL formulas, lens designs, and surgical techniques were not evaluated, which may have influenced postoperative refractive outcomes. Larger multicentric studies with extended follow-up are required to validate these findings.

1. Tang Y, Wang X, Wang J, Huang W, Gao Y, Luo Y, et al. Prevalence and causes of visual impairment in a Chinese adult population: the Taizhou Eye Study. 2015;122(7):1480-1488.
2. Morsch P, Barbosa A, Bittencourt L. Vision impairment and blindness in individuals aged 60 years and older in Latin America and the Caribbean. Rev Panam Salud Publica. 2024;48:e101.
3. Shetty N, Kaweri L, Koshy A. Repeatability of biometry measured by three devices and its impact on predicted intraocular lens power. J Cataract Refract Surg. 2021;47(5):585-592.
4. Reitblat O, Levy A, Kleinmann G, Assia EI. Accuracy of intraocular lens power calculation using three optical biometry measurement devices: the OA-2000, Lenstar-LS900 and IOLMaster-500. Eye (Lond). 2018;32(7):1244-1252.
5. Song JS, Yoon DY, Hyon JY. Comparison of ocular biometry and refractive outcomes using IOL Master 500, IOL Master 700, and Lenstar LS900. Korean J Ophthalmol. 2020;34(2).
6. Norrby S. Sources of error in intraocular lens power calculation. J Cataract Refract Surg. 2008;34(3):368-376.
7. Savini G, Taroni L, Hoffer KJ. Recent developments in intraocular lens power calculation methods—Update 2020. Ann Transl Med. 2020;8(22).
8. Sella R, Korenfeld MS. The effect of patient age on some new and older IOL power calculation formulas. Acta Ophthalmol. 2024;102:e696-e704.
9. Hoffer KJ, Savini G. Update on intraocular lens power calculation study protocols: the better way to design and report clinical trials. 2021;128(8):e115-e120.
10. Cruysberg LPJ, Doors M, Verbakel F, Berendschot TTJM, De Brabander J, Nuijts RMMA, et al. Evaluation of the Lenstar LS 900 non-contact biometer. Br J Ophthalmol. 2010;94(1):106-110.
11. Chen W, McAlinden C, Pesudovs K, Wang Q, Lu F. Scheimpflug–Placido topographer and optical low-coherence reflectometry biometer: repeatability and agreement. J Cataract Refract Surg. 2012;38(9):1626-1632.
12. McAlinden C, Wang Q, Pesudovs K, Yang X, Bao Y, Yu A, et al. Axial length measurement failure rates with the IOLMaster and Lenstar LS 900 in eyes with cataract. PLoS One. 2015;10:e0128929.
13. Akalın İ, Tüfek M, Türkyılmaz M, Öztürk F. Comparison of preoperative and postoperative measurements of optical low-coherence reflectometry biometry and assessment of its refractive predictability. Int Ophthalmol. 2019;39(6):1337-1343.
14. Hussain M, Quraishy MM, Akram M. Refractive results after cataract surgery using optical biometry. Pak J Ophthalmol. 2020;36(1):53-56.

1. Tang Y, Wang X, Wang J, Huang W, Gao Y, Luo Y, et al. Prevalence and causes of visual impairment in a Chinese adult population: the Taizhou Eye Study. 2015;122(7):1480-1488.
2. Morsch P, Barbosa A, Bittencourt L. Vision impairment and blindness in individuals aged 60 years and older in Latin America and the Caribbean. Rev Panam Salud Publica. 2024;48:e101.
3. Shetty N, Kaweri L, Koshy A. Repeatability of biometry measured by three devices and its impact on predicted intraocular lens power. J Cataract Refract Surg. 2021;47(5):585-592.
4. Reitblat O, Levy A, Kleinmann G, Assia EI. Accuracy of intraocular lens power calculation using three optical biometry measurement devices: the OA-2000, Lenstar-LS900 and IOLMaster-500. Eye (Lond). 2018;32(7):1244-1252.
5. Song JS, Yoon DY, Hyon JY. Comparison of ocular biometry and refractive outcomes using IOL Master 500, IOL Master 700, and Lenstar LS900. Korean J Ophthalmol. 2020;34(2).
6. Norrby S. Sources of error in intraocular lens power calculation. J Cataract Refract Surg. 2008;34(3):368-376.
7. Savini G, Taroni L, Hoffer KJ. Recent developments in intraocular lens power calculation methods—Update 2020. Ann Transl Med. 2020;8(22).
8. Sella R, Korenfeld MS. The effect of patient age on some new and older IOL power calculation formulas. Acta Ophthalmol. 2024;102:e696-e704.
9. Hoffer KJ, Savini G. Update on intraocular lens power calculation study protocols: the better way to design and report clinical trials. 2021;128(8):e115-e120.
10. Cruysberg LPJ, Doors M, Verbakel F, Berendschot TTJM, De Brabander J, Nuijts RMMA, et al. Evaluation of the Lenstar LS 900 non-contact biometer. Br J Ophthalmol. 2010;94(1):106-110.
11. Chen W, McAlinden C, Pesudovs K, Wang Q, Lu F. Scheimpflug–Placido topographer and optical low-coherence reflectometry biometer: repeatability and agreement. J Cataract Refract Surg. 2012;38(9):1626-1632.
12. McAlinden C, Wang Q, Pesudovs K, Yang X, Bao Y, Yu A, et al. Axial length measurement failure rates with the IOLMaster and Lenstar LS 900 in eyes with cataract. PLoS One. 2015;10:e0128929.
13. Akalın İ, Tüfek M, Türkyılmaz M, Öztürk F. Comparison of preoperative and postoperative measurements of optical low-coherence reflectometry biometry and assessment of its refractive predictability. Int Ophthalmol. 2019;39(6):1337-1343.
14. Hussain M, Quraishy MM, Akram M. Refractive results after cataract surgery using optical biometry. Pak J Ophthalmol. 2020;36(1):53-56.