Plus Disease in Telemedicine Approaches to Evaluating Acute-Phase ROP (e-ROP) Study: Characteristics, Predictors, and

Qianqian Ellie Cheng1, Ebenezer Daniel2, Wei Pan2

  • 1School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania.

Ophthalmology
|January 29, 2019
PubMed

Insights

Plus disease, a severe form of retinopathy of prematurity, affects 10% of premature infants. Key predictors include gestational age, race, respiratory support, and weight gain, with image grading showing high accuracy.

Area of Science:

  • Ophthalmology
  • Neonatology
  • Medical Imaging

Background:

  • Retinopathy of prematurity (ROP) is a leading cause of visual impairment in premature infants.
  • Plus disease, characterized by abnormal blood vessels, is a critical indicator of severe ROP requiring treatment.

Purpose of the Study:

  • To characterize plus disease in premature infants within the Electronic Retinopathy of Prematurity (e-ROP) Study.
  • To identify predictors of plus disease development.
  • To evaluate the accuracy of digital image grading for detecting plus disease.

Main Methods:

  • Analysis of data from 13 North American centers, including premature infants with birth weight <1251 g.
  • Regular ophthalmologic examinations and wide-field digital imaging.
  • Independent image grading by masked readers, with clinical examination as the reference standard.

Main Results:

  • 10% of 1239 infants developed plus disease, primarily in the superior or inferior temporal quadrants.
  • Significant predictors included gestational age, race, respiratory support, weight gain, and specific ROP image findings (preplus/plus disease, stage 3 ROP, blot hemorrhage).
  • Image grading achieved 94% sensitivity and 81% specificity for detecting plus disease.

Conclusions:

  • Plus disease affects 10% of infants in the e-ROP cohort, with identifiable predictors.
  • Gestational age, race, respiratory support, weight gain, and specific ROP features are crucial predictors.
  • Nonphysician grading of digital images is a sensitive and specific method for detecting plus disease.
Abstract

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