Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population
Purpose. Four weight-gain-based algorithms are compared for the prediction of type 1 ROP in an Australian cohort: the weight, insulin-like growth factor, neonatal retinopathy of prematurity (WINROP) algorithm, the Children’s Hospital of Philadelphia Retinopathy of Prematurity (CHOPROP), the Colorado...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Ophthalmology |
| Online Access: | http://dx.doi.org/10.1155/2023/8406287 |
| _version_ | 1797692864385253376 |
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| author | Alexander Bremner Li Yen Chan Courtney Jones Shaheen P. Shah |
| author_facet | Alexander Bremner Li Yen Chan Courtney Jones Shaheen P. Shah |
| author_sort | Alexander Bremner |
| collection | DOAJ |
| description | Purpose. Four weight-gain-based algorithms are compared for the prediction of type 1 ROP in an Australian cohort: the weight, insulin-like growth factor, neonatal retinopathy of prematurity (WINROP) algorithm, the Children’s Hospital of Philadelphia Retinopathy of Prematurity (CHOPROP), the Colorado Retinopathy of Prematurity (CO-ROP) algorithm, and the postnatal growth, retinopathy of prematurity (G-ROP) algorithm. Methods. A four-year retrospective cohort analysis of infants screened for ROP in a tertiary neonatal intensive care unit in Brisbane, Australia. The main outcome measures were sensitivities, specificities, and positive and negative predictive values. Results. 531 infants were included (mean gestational age 28 + 3). 24 infants (4.5%) developed type 1 ROP. The sensitivities, specificities, and negative predictive values, respectively, for type 1 ROP (95% confidence intervals) were for WINROP 83.3% (61.1–93.3%), 52.3% (47.8–56.7%), and 98.4% (96.1–99.4%); for CHOPROP 100% (86.2–100%), 46.0% (41.7–50,3%), and 100% (98.4–100%); for CO-ROP 100% (86.2–100%), 32.0% (28.0%–36.1%), and 100% (98.3–100%); and for G-ROP 100% (86.2–100%), 28.2% (24.5–32.3%), and 100% (97.4–100%). Of the five infants with persistent nontype 1 ROP that underwent treatment, only CO-ROP was able to successfully identify all. Conclusions. CHOPROP, CO-ROP, and G-ROP performed well in this Australian population. CHOPROP, CO-ROP, and G-ROP would reduce the number of infants requiring examinations by 43.9%, 30.5%, and 26.9%, respectively, compared to current ROP screening guidelines. Weight-gain-based algorithms would be a useful adjunct to the current ROP screening. |
| first_indexed | 2024-03-12T02:34:50Z |
| format | Article |
| id | doaj.art-609a152d35d3488fbc045c0aad896456 |
| institution | Directory Open Access Journal |
| issn | 2090-0058 |
| language | English |
| last_indexed | 2024-03-12T02:34:50Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Ophthalmology |
| spelling | doaj.art-609a152d35d3488fbc045c0aad8964562023-09-05T00:00:01ZengHindawi LimitedJournal of Ophthalmology2090-00582023-01-01202310.1155/2023/8406287Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian PopulationAlexander Bremner0Li Yen Chan1Courtney Jones2Shaheen P. Shah3University of SydneyMater Mother’s Hospital BrisbaneMater Mother’s Hospital BrisbaneMater Mother’s Hospital BrisbanePurpose. Four weight-gain-based algorithms are compared for the prediction of type 1 ROP in an Australian cohort: the weight, insulin-like growth factor, neonatal retinopathy of prematurity (WINROP) algorithm, the Children’s Hospital of Philadelphia Retinopathy of Prematurity (CHOPROP), the Colorado Retinopathy of Prematurity (CO-ROP) algorithm, and the postnatal growth, retinopathy of prematurity (G-ROP) algorithm. Methods. A four-year retrospective cohort analysis of infants screened for ROP in a tertiary neonatal intensive care unit in Brisbane, Australia. The main outcome measures were sensitivities, specificities, and positive and negative predictive values. Results. 531 infants were included (mean gestational age 28 + 3). 24 infants (4.5%) developed type 1 ROP. The sensitivities, specificities, and negative predictive values, respectively, for type 1 ROP (95% confidence intervals) were for WINROP 83.3% (61.1–93.3%), 52.3% (47.8–56.7%), and 98.4% (96.1–99.4%); for CHOPROP 100% (86.2–100%), 46.0% (41.7–50,3%), and 100% (98.4–100%); for CO-ROP 100% (86.2–100%), 32.0% (28.0%–36.1%), and 100% (98.3–100%); and for G-ROP 100% (86.2–100%), 28.2% (24.5–32.3%), and 100% (97.4–100%). Of the five infants with persistent nontype 1 ROP that underwent treatment, only CO-ROP was able to successfully identify all. Conclusions. CHOPROP, CO-ROP, and G-ROP performed well in this Australian population. CHOPROP, CO-ROP, and G-ROP would reduce the number of infants requiring examinations by 43.9%, 30.5%, and 26.9%, respectively, compared to current ROP screening guidelines. Weight-gain-based algorithms would be a useful adjunct to the current ROP screening.http://dx.doi.org/10.1155/2023/8406287 |
| spellingShingle | Alexander Bremner Li Yen Chan Courtney Jones Shaheen P. Shah Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population Journal of Ophthalmology |
| title | Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population |
| title_full | Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population |
| title_fullStr | Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population |
| title_full_unstemmed | Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population |
| title_short | Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population |
| title_sort | comparison of weight gain based prediction models for retinopathy of prematurity in an australian population |
| url | http://dx.doi.org/10.1155/2023/8406287 |
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