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10 - 14 Sept. 2016, Bella Center, Copenhagen, Denmark

This Meeting has been awarded 27 CME credits

 

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A method for the automatic collection of large population metrics to correctly identify factors that impact final visual acuity obtained with small aperture corneal inlays

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Session Details

Session Title: Presented Poster Session: Refractive Corneal Inlays

Session Date/Time: Monday 12/09/2016 | 15:00-16:30

Paper Time: 16:20

Venue: Poster Village: Pod 2

First Author: : B.Will USA

Co Author(s): :                  

Abstract Details

Purpose:

To understand the clinical effects of corneal anatomy, refractive error, tear film function, inlay centration and other factors that influence final visual acuity attained by small aperture corneal inlays (SACI) by collecting large population data using an automated software algorithm that requires little if any manual data entry. Using big data analytics techniques we identify those factor that positively impact the 1) speed at which glasses independence is attained and; 2) quality of distance and near vision obtained with SACI.

Setting:

Clinical data collection using the described software system occurs at the point of origin in clinics, out-patient surgical facilities and hospitals.

Methods:

With SACI the timing of postop near vision recovery varies widely between patients and the location identified to center the inlay to optimize vision in clinical practice is based primarily upon optical models. Currently, data capture for patient cohorts is performed using Excel® spreadsheets with manual data entry or, most often, not at all. Frequently single surgeons have insufficient surgical volume to perform complex outcomes analysis. Combining data across surgeons and surgical techniques is essential but is impractical using spreadsheet and manual data entry methods which have proven unmanageable and ineffective for performing sophisticated outcomes analysis for other refractive procedures.

Results:

We developed software algorithms to automatically capture preop and postop refractive, accommodative, topometric, aberrometric, tear function and inlay centration data from an array of diagnostic devices and electronic records. Real time data capture of large data sets combined with dynamic query capability enables surgeons to analyze relationships between preop and postop findings to understand factors or groups of factors that influence final visual outcomes. 467/700.

Conclusions:

We will demonstrate the functionality of a secure cloud based system that collects clinical data automatically with minimal manual data entry and protects surgeon and patient identities that provides the infrastructure for systematically improving clinical outcomes for SACIs by 1) identifying factors influencing visual acuity and; 2) delivering real time feedback to surgeons. Big data analytics demonstrate clinically important factors that positively impact visual results from SACI.

Financial Disclosure:

NONE

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