Comparative accuracy of IOL power calculation formulas in nanophthalmic eyes undergoing cataract surgery
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.7.12Keywords:
Nanophthalmos, IOL power calculation, short axial length, cataract surgery, Accuracy of IOL PowerDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Aim: To compare the predictive accuracy of three widely used IOL power calculation formulas—Hoffer Q, SRK/T, and SRK II—in adult patients with nanophthalmos undergoing cataract surgery or clear lens extraction. Methods: This retrospective observational study included 45 eyes with axial lengths ≤ 20.5 mm diagnosed with nanophthalmos. All patients underwent uncomplicated cataract surgery or clear lens extraction with posterior chamber IOL implantation. Preoperative biometry was performed using ZEISS IOL Master 700 or NANO AXIS A-scan. IOL power was calculated using Hoffer Q, SRK/T, and SRK II formulas. Postoperative spherical equivalent was recorded at one month, and prediction error was calculated as the difference between actual and predicted refraction. Mean absolute error (MAE) and percentage of eyes within ±0.25 D, ±0.50 D, ±1.00 D, and ±2.00 D were assessed. Statistical analysis included one-sample t-tests and descriptive statistics using SPSS version 26. Results: The Hoffer Q formula showed the lowest mean absolute prediction error (−0.44 ± 0.30 D), followed by SRK/T (+0.68 ± 0.73 D), while SRK II exhibited the highest error (+3.28 ± 0.52 D). The Hoffer Q formula demonstrated superior accuracy, with 75.6% of eyes within ±0.50 D and 93.3% within ±1.00 D of the target refraction. SRK II showed a statistically significant hyperopic shift (p < 0.001), whereas Hoffer Q and SRK/T did not show statistically significant differences from zero prediction error. Conclusion: Among the three formulas studied, the Hoffer Q formula provided the most accurate IOL power prediction in nanophthalmic eyes, with the lowest refractive error and highest consistency. These findings support the use of Hoffer Q in managing cataract patients with nanophthalmos and highlight the need for further evaluation of advanced formulas in this subgroup.Abstract
How to Cite
Downloads
Similar Articles
- Rohit Mittal, Devinder Kumar, Harmel Singh Chahal, Antioxidant and Free Radical Scavenging Activity of Methanolic Extract of (Hordeum vulgare) Barley , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Sudheer Choudari, K. Rajasekhar, Ch. Sudheer, Comparative study of the foundation model of a 220 kV transmission line tower with different footing steps - Finite element analysis , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- P. Vinnarasi, K. Menaka, Advanced hybrid feature selection techniques for analyzing the relationship between 25-OHD and TSH , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Parameswari P.L., N. Amsaveni, Veeramani Marimuthu, E-Resource Utilization Among Kuwait University Faculty: an Analytical Study , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Purnendu B. Acharjee, Bhupaesh Ghai, Muniyandy Elangovan, S. Bhuvaneshwari, Ravi Rastogi, P. Rajkumar, Exploring AI-driven approaches to drug discovery and development , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- A. Tamilmani, K. Muthuramalingam, An enhanced support vector machine bbased multiclass classification method for crop prediction , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Vaishali Yeole, Rushikesh Yeole, Pradheep Manisekaran, Analysis and prediction of stomach cancer using machine learning , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Sirajum Munira Priety, Farhan Bin Manjur, AI Driven Approach in Smart Manufacturing in Bangladesh , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Jyoti Vishwakarma, Sunil Kumar, Navigating the Skies: An Analysis of ESG Practices in the Airline Industry , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Temesgen A. Asfaw, Deep learning hyperparameter’s impact on potato disease detection , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 11 12 13 14 15 16 17 18 19 20 > >>
You may also start an advanced similarity search for this article.

