Repeatability of biometric measures from the LenStar LS900 in a cataractous population

TL;DR

This study examines how consistent biometric measurements are in cataract patients using the LenStar LS900 device and how their uncertainties interact.

Contribution

The paper provides uncertainty and correlation data for biometric measures to improve error propagation models in cataract surgery.

Findings

Sw values for corneal thickness and axial length were 3.6 µm and 107.5 µm, respectively.

LT and ACD uncertainties showed strong negative correlation.

Keratometric axis uncertainty is unreliable due to large decay over astigmatism.

Abstract

To investigate the repeatability of biometric measures and assess interactions between their uncertainties for use in an error propagation model, using patient data.

Cross-sectional non-randomised study evaluating a dataset containing 969 LenStar 900 biometric measurements taken before cataract surgery. Only complete scans with at least 3 successful measurements for each eye performed on the same day were considered. For each sequence, the aggregated mean (AMEAN) and population standard deviations (ASD) were derived. The within-subject standard deviation Sw was extracted for: corneal thickness, CCT, anterior chamber depth ACD, lens thickness LT, axial length AL, corneal diameter WTW, and the keratometric power vector components equivalent power KEQ, and the projections of corneal astigmatism KC0 and KC45. Correlations between the uncertainties were assessed using Spearman rank correlations.

For the 266 eyes matching the inclusion criteria, Sw was 3.6/ 24.7/35.5/ 17.7/ 107.5 µm for CCT/ ACD/ LT/ AL WTW and 0.18/ 0.12/ 0.10 dioptres for KEQ/ KC0/ KC45. The keratometric axis ASD is inversely proportional to the keratometric astigmatism AMEAN. LT and ACD uncertainties are strongly negatively correlated, with KEQ and KC0 uncertainties moderately correlated.

The uncertainty and correlation data presented here could be used to define a Monte-Carlo based error propagation model mapping the biometric measures and uncertainties to variations in predicted refraction after cataract surgery. We recommend using power vector components for error propagation models since the large decay over keratometric astigmatism makes keratometric axis uncertainty unreliable.

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