AstigMETRICS: An Automated Tool for Standardized Vector Metrics Tables and Group Comparisons in Refractive Surgery

TL;DR

AstigMETRICS is a free tool that automates the creation of standardized vector analysis tables for astigmatism outcomes in refractive surgery, improving consistency and ease of reporting.

Contribution

The novel contribution is a freely available, user-friendly software for automated vector metrics and statistical comparisons in refractive surgery studies.

Findings

AstigMETRICS calculates seven standard vector metrics using the Alpins method and performs statistical comparisons automatically.

The software generates publication-ready tables with clinically relevant proportions and high-resolution images for three simulated surgical scenarios.

It supports both parametric and nonparametric tests, providing p-values and effect sizes for paired and unpaired study designs.

Abstract

Background/Objectives: Standardized reporting of astigmatism outcomes is essential for comparability, reproducibility, and interpretation of refractive surgery studies. Vectorial analyses based on established metrics are increasingly required by major journals, yet no freely available tool exists for generating publication-ready vector analysis tables with statistical comparisons. This study presents AstigMETRICS, a standalone application for automated calculation, formatting, and statistical comparison of standard vector metrics in refractive surgery. Methods: AstigMETRICS was developed in MATLAB and compiled as a standalone executable requiring no programming knowledge. The software accepts preoperative, intended, and postoperative astigmatism data in spreadsheet format for both refractive and corneal measurements. It calculates seven standard vector metrics following the Alpins method: the target-induced astigmatism (TIA), surgically induced astigmatism (SIA), difference vector (DV), correction index (CI), magnitude of error (ME), angle of error (AE), and index of success (IOS). Statistical comparisons are performed automatically using appropriate parametric or nonparametric tests for paired and unpaired study designs, with p-values and Cohen’s d effect sizes reported. Results: AstigMETRICS generates standardized tables reporting the means, standard deviations, and clinically relevant proportions (percentage of eyes with an ME within ±0.50 D or ±1.00 D, and an AE within ±15°). Three simulated datasets were created to validate the software functionality across common surgical scenarios: a contralateral eye laser vision correction, toric phakic IOL implantation, and cataract surgery with toric IOLs. The output tables are displayed in standardized format and saved as high-resolution TIFF images suitable for publication. The software is freely available and a download link is provided in this article. Conclusions: AstigMETRICS enables clinicians and researchers to perform standardized, reproducible astigmatism vector analyses with built-in statistical comparisons. This freely available tool simplifies outcome reporting and improves methodological consistency in refractive surgery research.

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