Retrospective comparison between growth and retinopathy of prematurity model versus WINROP model

Almeida, Ana C.; Sandinha, Teresa; Azevedo, Rita; Brízido, Margarida; Figueiredo, Melissa Brigham de; Coelho, Constança; Teixeira, Susana

doi:10.1016/j.jcjo.2021.02.030

Cited by 10 publications

(11 citation statements)

References 27 publications

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“…The full text was read for eligibility, of which 61 were included in the final analysis. The 61 studies (>37 000 infants) included WINROP (n = 36), 6 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 G-ROP (n = 9), 7 , 42 , 56 , 57 , 58 , 59 , 60 , 61 PINT ROP (n = 1), 8 CHOP ROP (n = 6), 9 , 30 , 55 , 62 , 63 , 64 ROPScore (n = 5), 10 , 30 , 55 , 65 , 66 and CO-ROP (n = 4). 11 , 67 , 68 , …”

Section: Resultsmentioning

confidence: 99%

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The Use of Postnatal Weight Gain Algorithms to Predict Severe or Type 1 Retinopathy of Prematurity

et al. 2021

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Key Points Question Do postnatal weight gain–based algorithms have the potential to identify infants with type 1 retinopathy of prematurity (ROP) or severe ROP? Findings This systematic review and meta-analysis that included 61 studies (>37 000 infants) found that weight gain–based algorithms have adequate sensitivity, ranging from 0.89 to 1.00, and negative likelihood ratios (<0.2). However, specificity and positive likelihood ratios were inadequate. Meaning This study suggests that weight gain–based algorithms have adequate sensitivity and negative likelihood ratios and provide reasonable certainty that type 1 ROP or severe ROP is unlikely to develop (ie, the algorithm is useful for ruling out the disease).

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Section: Resultsmentioning

confidence: 99%

“… 57 When validated outside the North American cohort, the sensitivity was 1.00 in a small cohort of infants from Japan, 56 the UK, and Egypt 60 ; however, it decreased to approximately 0.91 in a Turkish and Portuguese cohort. 42 , 58 …”

Section: Discussionmentioning

confidence: 99%

The Use of Postnatal Weight Gain Algorithms to Predict Severe or Type 1 Retinopathy of Prematurity

et al. 2021

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“…We also minimized the burden of examination by applying these criteria. Considering that the G-ROP criteria constitute a new screening method for ROP, many researchers in the field have begun to examine their own populations using these criteria [14][15][16][17][18][23][24][25][26][27][28]. Table 4 summarizes the main findings and validation outcomes of previously published articles on this topic, as compared with those of the current study.…”

Section: Discussionmentioning

confidence: 93%

Validation of the Postnatal Growth and Retinopathy of Prematurity (G-ROP) screening criteria

Fadakar¹,

Abbas²,

Soltani³

et al. 2022

Med Hypothesis Discov Innov Ophthalmol

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Background: Retinopathy of prematurity (ROP) is a leading cause of irreversible blindness in infants. The Postnatal Growth and ROP (G-ROP) study proposed new screening criteria for ROP. This study aimed to validate the G-ROP screening criteria in a group of Iranian premature infants who were treated in the neonatal intensive care unit (NICU) for at least 40 days. Methods: In this retrospective study, we extracted the data pertaining to infants admitted to the NICU from January 2020 to December 2021. We screened all the included infants for ROP based on the Iranian national screening criteria. We applied the G-ROP criteria to our study population, and if no criterion was met, the infant was exempted from ROP screening. We determined the sensitivity and specificity of the G-ROP guidelines for ROP detection, along with its capacity for predicting the requirement for ROP treatment. Moreover, we compared the G-ROP guidelines with the Iranian and North American guidelines for ROP screening. Results: A total of 166 premature infants with complete datasets were included: 130 had ROP, of whom 61 were treated. There were 109 female infants (65.7%). The mean (standard deviation [SD]) birth weight and gestational age were 1080 (256) g and 28.28 (1.97) weeks, respectively. Applying the G-ROP criteria, 127 of 130 infants with ROP were identified (sensitivity, 97.69%; 95% confidence interval [CI], 95.11% – 100%), and of 36 infants without ROP, three were correctly excluded (specificity, 8.33%; 95% CI, 0% – 17.36%). The G-ROP criteria did not fail to identify infants who required treatment for ROP (sensitivity, 100%; 95% CI, 98.29 – 100) and had a specificity of 8.69% (95% CI, 2.04% – 15.34%). Although the Iranian and North American criteria had 100% sensitivity for infants with any stage of ROP, they could not detect infants without ROP (0% specificity). Conclusions: The G-ROP screening criteria had a sensitivity of 100% in identifying infants requiring treatment for ROP in our high-risk group; however, specificity was not sufficiently high. Further studies with larger numbers of referred infants could confirm a decrease in the burden of retinal examinations using these criteria.

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“…These figures might be compared with the ones obtained in a validation study of G-ROP and WINROP using the same cohort, predicting type I ROP for infants born at GA <30 weeks; sensitivity was 100% for both G-ROP and WINROP and specificity 9% and 7%, respectively. 35 The strength of this study is the access to the Swedish Contemporary Validation Cohort consecutively followed between years 2018 and 2020. Continuous validation is a prerequisite for a prediction model to be useful in clinics.…”

Section: Clinical Sciencementioning

confidence: 99%

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

et al. 2022

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Background/AimsRetinopathy 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.MethodsData 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.ResultsFor 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.ConclusionsDIGIROP-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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Retrospective comparison between growth and retinopathy of prematurity model versus WINROP model

Cited by 10 publications

References 27 publications

The Use of Postnatal Weight Gain Algorithms to Predict Severe or Type 1 Retinopathy of Prematurity

The Use of Postnatal Weight Gain Algorithms to Predict Severe or Type 1 Retinopathy of Prematurity

Validation of the Postnatal Growth and Retinopathy of Prematurity (G-ROP) screening criteria

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

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