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Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort

BACKGROUND/AIMS: Retinopathy of prematurity (ROP) is currently diagnosed through repeated eye examinations to find the low percentage of infants that fulfil treatment criteria to reduce vision loss. A prediction model for severe ROP requiring treatment that might sensitively and specifically identif...

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Autores principales: Pivodic, Aldina, E.H. Smith, Lois, Hård, Anna-Lena, Löfqvist, Chatarina, Almeida, Ana Catarina, Al-Hawasi, Abbas, Larsson, Eva, Lundgren, Pia, Sunnqvist, Birgitta, Tornqvist, Kristina, Wallin, Agneta, Holmstrom, Gerd, Gränse, Lotta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359565/
https://www.ncbi.nlm.nih.gov/pubmed/35277395
http://dx.doi.org/10.1136/bjophthalmol-2021-320738
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author Pivodic, Aldina
E.H. Smith, Lois
Hård, Anna-Lena
Löfqvist, Chatarina
Almeida, Ana Catarina
Al-Hawasi, Abbas
Larsson, Eva
Lundgren, Pia
Sunnqvist, Birgitta
Tornqvist, Kristina
Wallin, Agneta
Holmstrom, Gerd
Gränse, Lotta
author_facet Pivodic, Aldina
E.H. Smith, Lois
Hård, Anna-Lena
Löfqvist, Chatarina
Almeida, Ana Catarina
Al-Hawasi, Abbas
Larsson, Eva
Lundgren, Pia
Sunnqvist, Birgitta
Tornqvist, Kristina
Wallin, Agneta
Holmstrom, Gerd
Gränse, Lotta
author_sort Pivodic, Aldina
collection PubMed
description BACKGROUND/AIMS: Retinopathy of prematurity (ROP) is currently diagnosed through repeated eye examinations to find the low percentage of infants that fulfil treatment criteria to reduce vision loss. A prediction model for severe ROP requiring treatment that might sensitively and specifically identify infants that develop severe ROP, DIGIROP-Birth, was developed using birth characteristics. DIGIROP-Screen additionally incorporates first signs of ROP in different models over time. The aim was to validate DIGIROP-Birth, DIGIROP-Screen and their decision support tool on a contemporary Swedish cohort. METHODS: Data were retrieved from the Swedish national registry for ROP (2018–2019) and two Swedish regions (2020), including 1082 infants born at gestational age (GA) 24 to <31 weeks. The predictors were GA at birth, sex, standardised birth weight and age at the first sign of ROP. The outcome was ROP treatment. Sensitivity, specificity and area under the receiver operating characteristic curve (AUC) with 95% CI were described. RESULTS: For DIGIROP-Birth, the AUC was 0.93 (95% CI 0.90 to 0.95); for DIGIROP-Screen, it ranged between 0.93 and 0.97. The specificity was 49.9% (95% CI 46.7 to 53.0) and the sensitivity was 96.5% (95% CI 87.9 to 99.6) for the tool applied at birth. For DIGIROP-Screen, the cumulative specificity ranged between 50.0% and 78.7%. One infant with Beckwith-Wiedemann syndrome who fulfilled criteria for ROP treatment and had no missed/incomplete examinations was incorrectly flagged as not needing screening. CONCLUSIONS: DIGIROP-Birth and DIGIROP-Screen showed high predictive ability in a contemporary Swedish cohort. At birth, 50% of the infants born at 24 to <31 weeks of gestation were predicted to have low risk of severe ROP and could potentially be released from ROP screening examinations. All routinely screened treated infants, excluding those screened for clinical indications of severe illness, were correctly flagged as needing ROP screening.
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spelling pubmed-103595652023-07-22 Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort Pivodic, Aldina E.H. Smith, Lois Hård, Anna-Lena Löfqvist, Chatarina Almeida, Ana Catarina Al-Hawasi, Abbas Larsson, Eva Lundgren, Pia Sunnqvist, Birgitta Tornqvist, Kristina Wallin, Agneta Holmstrom, Gerd Gränse, Lotta Br J Ophthalmol Clinical Science BACKGROUND/AIMS: Retinopathy of prematurity (ROP) is currently diagnosed through repeated eye examinations to find the low percentage of infants that fulfil treatment criteria to reduce vision loss. A prediction model for severe ROP requiring treatment that might sensitively and specifically identify infants that develop severe ROP, DIGIROP-Birth, was developed using birth characteristics. DIGIROP-Screen additionally incorporates first signs of ROP in different models over time. The aim was to validate DIGIROP-Birth, DIGIROP-Screen and their decision support tool on a contemporary Swedish cohort. METHODS: Data were retrieved from the Swedish national registry for ROP (2018–2019) and two Swedish regions (2020), including 1082 infants born at gestational age (GA) 24 to <31 weeks. The predictors were GA at birth, sex, standardised birth weight and age at the first sign of ROP. The outcome was ROP treatment. Sensitivity, specificity and area under the receiver operating characteristic curve (AUC) with 95% CI were described. RESULTS: For DIGIROP-Birth, the AUC was 0.93 (95% CI 0.90 to 0.95); for DIGIROP-Screen, it ranged between 0.93 and 0.97. The specificity was 49.9% (95% CI 46.7 to 53.0) and the sensitivity was 96.5% (95% CI 87.9 to 99.6) for the tool applied at birth. For DIGIROP-Screen, the cumulative specificity ranged between 50.0% and 78.7%. One infant with Beckwith-Wiedemann syndrome who fulfilled criteria for ROP treatment and had no missed/incomplete examinations was incorrectly flagged as not needing screening. CONCLUSIONS: DIGIROP-Birth and DIGIROP-Screen showed high predictive ability in a contemporary Swedish cohort. At birth, 50% of the infants born at 24 to <31 weeks of gestation were predicted to have low risk of severe ROP and could potentially be released from ROP screening examinations. All routinely screened treated infants, excluding those screened for clinical indications of severe illness, were correctly flagged as needing ROP screening. BMJ Publishing Group 2023-08 2022-03-11 /pmc/articles/PMC10359565/ /pubmed/35277395 http://dx.doi.org/10.1136/bjophthalmol-2021-320738 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Clinical Science
Pivodic, Aldina
E.H. Smith, Lois
Hård, Anna-Lena
Löfqvist, Chatarina
Almeida, Ana Catarina
Al-Hawasi, Abbas
Larsson, Eva
Lundgren, Pia
Sunnqvist, Birgitta
Tornqvist, Kristina
Wallin, Agneta
Holmstrom, Gerd
Gränse, Lotta
Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort
title Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort
title_full Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort
title_fullStr Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort
title_full_unstemmed Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort
title_short Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort
title_sort validation of digirop models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary swedish cohort
topic Clinical Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359565/
https://www.ncbi.nlm.nih.gov/pubmed/35277395
http://dx.doi.org/10.1136/bjophthalmol-2021-320738
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