Posters

CELLA: a software tool for tracking and predicting endothelial cell loss after phakic IOL implantation

Poster Details

First Author: J.Quadrado Gil PORTUGAL

Co Author(s):    M. Raimundo   J. Simao   H. Queirós   A. Rosa   M. Quadrado   J. Neto-Murta     

Abstract Details

Purpose:

While phakic IOLs have excellent refractive results, endothelial safety remains a critical issue mandating regular follow-up of implanted patients. Manually tracking endothelial cell loss (ECL) over time without the support of structured analysis may miss accelerated ECL or the approach of critical thresholds where explantation should be considered. We purpose the use of a software tool to both follow and predict ECL in this context.

Setting:

Ophthalmology Department, Centro Hospitalar e Universitário de Coimbra, Portugal

Methods:

Development of a software application prototype, named Cella, using Matlab® 2018b that allows input of serial EC measurements over time for each patient. Both retrospective analysis and predictive analysis (using linear regression of multiple measurements) is automatically performed. ECD larger than 25% from pre-operative cell density or ECL < 1500/mm2 were considered as critical endpoints for prediction analysis (as per the 2017 AAO Special Report in this matter).

Results:

After serially inputting all performed measurements for an individual patient, our tool automatically determines the early surgical ECL (within 6 months) and the annual ECL rate (ignoring early/surgical loss and adjusted for physiological loss). All results can be seen as absolute counts or relative counts (percentages). For prediction analysis, based on the last available measurements, the software predicts the age when the patient is expected to attain a critical 25% ECL or ECL < 1500/mm2

Conclusions:

We believe this tool is a valuable aide in the day-to-day evaluation of patients implanted with phakic IOLs, obliviating manual calculations and enhancing the clinician’s ability to detect subtle but clinically significant ECL. Furthermore, using prediction analysis, it may help the clinician to better manage patient expectations regarding eventual future explantation according to evidence-based critical endpoints as well as scheduling adequate follow-up intervals.

Financial Disclosure:

None