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
- Kumar Sanu, Equabal Jawaid, POND EUTROPHICATION AND FOOD TYPE AS DETERMINANT OF GROWTH AND SURVIVAL IN Clarias batrachus (LINN.) , The Scientific Temper: Vol. 9 No. 1&2 (2018): The Scientific Temper
- Deneshkumar V, Jebitha R, Jithu G, Multistate modeling for estimating clinical outcomes of COVID-19 patients , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Bommaiah Boya, Premara Devaraju, Integrating clinical and ECG data for heart disease prediction: A hybrid deep learning approach based on two modalities with particle swarm optimization , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- S. Mohamed Iliyas, M. Mohamed Surputheen, A.R. Mohamed Shanavas, Enhanced Block Chain Financial Transaction Security Using Chain Link Smart Agreement based Secure Elliptic Curve Cryptography , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- K. Vani, S. Britto Ramesh Kumar, FSECAD: Feature-Selected Explainable Cloud Anomaly Detection Framework , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Abhishek Dwivedi, Nikhat Raza Khan, Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- T. Ramyaveni, V. Maniraj, Hyperparameter tuning of diabetes prediction using machine learning algorithm with pelican optimization algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Prabhu, A. Chandrabose, Optimization based energy aware scheduling in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Dimpal Khambhati, Chirag Patel, Analyzing cardiac physiology: ECG ensemble averaging and morphological features under treadmill-induced stress in LabVIEW , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- Syed Amin Jameel, Abdul Rahim Mohamed Shanavas, Deep-Ultranet: Diabetic Retinopathy Grading System Using Ultra-Widefield Retinal Images , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
<< < 17 18 19 20 21 22 23 24 25 26 > >>
You may also start an advanced similarity search for this article.

