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Multivariate Models to Diagnose Early Referral-Warranted Retinopathy of Prematurity With Handheld Optical Coherence Tomography.
Legocki, Alex T; Lee, Aaron Y; Ding, Leona; Moshiri, Yasman; Zepeda, Emily M; Gillette, Thomas B; Grant, Laura E; Shariff, Ayesha; Touch, Phanith; Lee, Cecilia S; Tarczy-Hornoch, Kristina; Cabrera, Michelle T.
Affiliation
  • Legocki AT; Department of Ophthalmology, University of Washington, Seattle, WA, USA.
  • Lee AY; Department of Ophthalmology, University of Washington, Seattle, WA, USA.
  • Ding L; The Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
  • Moshiri Y; Department of Ophthalmology, University of Washington, Seattle, WA, USA.
  • Zepeda EM; Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL, USA.
  • Gillette TB; Department of Ophthalmology, Dean McGee Eye Institute, University of Oklahoma, Oklahoma City, OK, USA.
  • Grant LE; Department of Ophthalmology, University of South Florida Eye Institute, Tampa, FL, USA.
  • Shariff A; Department of Ophthalmology, Millman-Derr Center for Eye Care, Rochester Hills, MI, USA.
  • Touch P; Department of Ophthalmology, New Mexico Veterans Affairs Medical Center, University of New Mexico, Albuquerque, NM, USA.
  • Lee CS; Department of Ophthalmology, University of Washington, Seattle, WA, USA.
  • Tarczy-Hornoch K; Department of Ophthalmology, University of Washington, Seattle, WA, USA.
  • Cabrera MT; The Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
Transl Vis Sci Technol ; 12(5): 26, 2023 05 01.
Article in En | MEDLINE | ID: mdl-37223917
ABSTRACT

Purpose:

The purpose of this study was to create multivariate models predicting early referral-warranted retinopathy of prematurity (ROP) using non-contact handheld spectral-domain optical coherence tomography (OCT) and demographic data.

Methods:

Between July 2015 and February 2018, infants ≤1500 grams birth weight or ≤30 weeks gestational age from 2 academic neonatal intensive care units were eligible for this study. Infants were excluded if they were too unstable to participate in ophthalmologic examination (2), had inadequate image quality (20), or received prior ROP treatment (2). Multivariate models were created using demographic variables and imaging findings to identify early referral-warranted ROP (referral-warranted ROP and/or pre-plus disease) by routine indirect ophthalmoscopy.

Results:

A total of 167 imaging sessions of 71 infants (45% male infants, gestational age 28.2+/-2.8 weeks, and birth weight 995.6+/-292.0 grams) were included. Twelve of 71 infants (17%) developed early referral-warranted ROP. The area under the receiver operating characteristic curve (AUC) was 0.94 for the generalized linear mixed model (sensitivity = 95.5% and specificity = 80.7%) and 0.83 for the machine learning model (sensitivity = 91.7% and specificity = 77.8%). The strongest variables in both models were birth weight, image-based Vitreous Opacity Ratio (an estimate of opacity density), vessel elevation, and hyporeflective vessels. A model using only birth weight and gestational age yielded an AUC of 0.68 (sensitivity = 77.3% and specificity = 63.4%), and a model using only imaging biomarkers yielded 0.88 (sensitivity = 81.8% and specificity = 84.8%).

Conclusions:

A generalized linear mixed model containing handheld OCT biomarkers can identify early referral-warranted ROP. Machine learning produced a less optimal model. Translational Relevance With further validation, this work may lead to a better-tolerated ROP screening tool.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Retinopathy of Prematurity Type of study: Diagnostic_studies / Prognostic_studies Limits: Female / Humans / Infant / Male / Newborn Language: En Journal: Transl Vis Sci Technol Year: 2023 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Retinopathy of Prematurity Type of study: Diagnostic_studies / Prognostic_studies Limits: Female / Humans / Infant / Male / Newborn Language: En Journal: Transl Vis Sci Technol Year: 2023 Document type: Article Affiliation country: Estados Unidos