Multivariate Models to Diagnose Early Referral-Warranted Retinopathy of Prematurity With Handheld Optical Coherence Tomography
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Translational vision science & technology - 12(2023), 5 vom: 01. Mai, Seite 26 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Legocki, Alex T [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 26.05.2023 Date Revised 28.09.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1167/tvst.12.5.26 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM35726312X |
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| 100 | 1 | |a Legocki, Alex T |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multivariate Models to Diagnose Early Referral-Warranted Retinopathy of Prematurity With Handheld Optical Coherence Tomography |
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| 500 | |a Date Revised 28.09.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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%) | ||
| 520 | |a Conclusions: A generalized linear mixed model containing handheld OCT biomarkers can identify early referral-warranted ROP. Machine learning produced a less optimal model | ||
| 520 | |a Translational Relevance: With further validation, this work may lead to a better-tolerated ROP screening tool | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 700 | 1 | |a Lee, Aaron Y |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Leona |e verfasserin |4 aut | |
| 700 | 1 | |a Moshiri, Yasman |e verfasserin |4 aut | |
| 700 | 1 | |a Zepeda, Emily M |e verfasserin |4 aut | |
| 700 | 1 | |a Gillette, Thomas B |e verfasserin |4 aut | |
| 700 | 1 | |a Grant, Laura E |e verfasserin |4 aut | |
| 700 | 1 | |a Shariff, Ayesha |e verfasserin |4 aut | |
| 700 | 1 | |a Touch, Phanith |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Cecilia S |e verfasserin |4 aut | |
| 700 | 1 | |a Tarczy-Hornoch, Kristina |e verfasserin |4 aut | |
| 700 | 1 | |a Cabrera, Michelle T |e verfasserin |4 aut | |
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