September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
A Predictive Model of Retinopathy of Prematurity Risk and Severity
Author Affiliations & Notes
  • Leah Owen
    Ophthalmology and Visual sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah, United States
  • Margaux Morrisson
    Ophthalmology and Visual sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah, United States
  • Bradley Yoder
    Division of Neonatology, University of Utah, SLC, Utah, United States
  • Margaret M DeAngelis
    Ophthalmology and Visual sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah, United States
  • Footnotes
    Commercial Relationships   Leah Owen, None; Margaux Morrisson, None; Bradley Yoder, None; Margaret DeAngelis, None
  • Footnotes
    Support  Internal start up funds through the University of Utah.
Investigative Ophthalmology & Visual Science September 2016, Vol.57, No Pagination Specified. doi:
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      Leah Owen, Margaux Morrisson, Bradley Yoder, Margaret M DeAngelis; A Predictive Model of Retinopathy of Prematurity Risk and Severity. Invest. Ophthalmol. Vis. Sci. 201657(12):.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose : Retinopathy of prematurity (ROP) is a blinding morbidity of preterm infants and currently represents the leading cause of childhood blindness worldwide. Though we have elucidated demographic and environmental risk factors that predispose to development of ROP, we lack a predictive model that integrates these factors to better identify infants at risk. We hypothesize that analysis of the statistical interaction between maternal, infant, and environmental factors will provide a mechanism to better predict ROP development and severity on an individual basis.

Methods : We performed a prospective analysis of preterm infants born at 30 weeks gestational age or less, admitted to the University of Utah neonatal intensive care unit from 2010-2015. The primary outcome measure was presence of ROP. Secondary outcome measures were type 1 ROP receiving laser treatment and severe ROP defined as stage 3 ROP or greater, any zone in the worse eye. Univariate analysis was performed to show which multiple demographic and environmental factors correlated with ROP phenotype in the literature as depicted in figure 1. A stepwise regression analysis was then used to determine the most predictive model for each outcome measure.

Results : We identified a total of 546 infants meeting our inclusion criteria. We found that the overall incidence of ROP was 59%, which decreased to 10% for severe ROP, and 6% for type 1 ROP. Univariate analysis, as demonstrated in figure 1, found a statistically significant relationship between a number of factors and our ROP outcome measures. However, stepwise regression analysis found that the most predictive model of overall ROP risk included gestational age, number of days on a ventilator, and number of days on oxygen therapy. There was no such predictive model found for type 1 ROP. Severe ROP was best predicted by gestational age, number of days with oxygen supplementation, and need for any surgery.

Conclusions : ROP is a blinding condition with significant clinical importance. Our current screening guidelines do not sufficiently allow for infant specific risk assessment. We analyzed the contribution of significant maternal, infant, and environmental factors to develop a predictive model for ROP development and severity. Our analysis is the first integrated statistical modeling, accounting for multiple sources of risk, and allowing for individual assessment.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

 

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