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Purpose: Screening guidelines for retinopathy of prematurity (ROP) are updated frequently to help clinicians identify infants at risk of type 1 ROP. This study aims to evaluate the accuracy of three different predictive algorithms--WINROP, ROPScore, and CO-ROP--in detecting ROP in preterm infants in a developing country. Methods: This retrospective study was conducted on 386 preterm infants from two centers between 2015 and 2021. Neonates with gestational age ≤30 weeks and/or birth weight ≤1500 g who underwent ROP screening were included. Results: One hundred twenty-three neonates (31.9%) developed ROP. The sensitivity to identify type 1 ROP was as follows: WINROP, 100%; ROPScore, 100%; and CO-ROP, 92.3%. The specificity was 28% for WINROP, 1.4% for ROPScore, and 19.3% for CO-ROP. CO-ROP missed two neonates with type 1 ROP. WINROP provided the best performance for type 1 ROP with an area under the curve score at 0.61. Conclusion: The sensitivity was at 100% for WINROP and ROPScore for type 1 ROP; however, specificity was quite low for both algorithms. Highly specific algorithms tailored to our population may serve as a useful adjunctive tool to detect preterm infants at risk of sight-threatening ROP. [ABSTRACT FROM AUTHOR]

Background. The aim of this study was to investigate the effectiveness of the Postnatal Growth and Retinopathy of Prematurity (G-ROP) and Colorado Retinopathy of Prematurity (CO-ROP) models in predicting the risk of Retinopathy of Prematurity (ROP) in preterm infants at a tertiary ROP diagnostic and treatment center. Methods. The G-ROP and CO-ROP models were applied to the study group using the data obtained. The sensitivity and specificity of both models were then calculated. Results. One hundred and twenty-six infants were included in the study. When the G-ROP model was applied to the study group, the model's sensitivity at detecting any stage ROP was 88.7%, while it was 93.3% for the treated group. The specificity of the model was 10.9% for any stage ROP, and 11.7% for the treated group. For the CO-ROP model in the same study group, the sensitivity at detecting any stage ROP was 87.3%, while it was 100% for the treated group. The CO-ROP model's specificity was 40% for any stage ROP, and 27.9% for the treated group. When cardiac pathology criteria were introduced to both models, the sensitivity of the G-ROP and CO-ROP models increased to 94.4% and 97.2%, respectively. Conclusions. It was found that the G-ROP and CO-ROP models are simple and effective models for predicting any degree of ROP development, but that they are unable to be 100% accurate. When the models were modified by introducing cardiac pathology criteria, it was observed that they began to produce more accurate results. Studies with larger groups are needed in order to assess the applicability of the modified criteria. [ABSTRACT FROM AUTHOR]

Purpose: Screening guidelines for retinopathy of prematurity (ROP) are updated frequently to help clinicians identify infants at risk of type 1 ROP. This study aims to evaluate the accuracy of three different predictive algorithms—WINROP, ROPScore, and CO-ROP—in detecting ROP in preterm infants in a developing country. Methods: This retrospective study was conducted on 386 preterm infants from two centers between 2015 and 2021. Neonates with gestational age ≤30 weeks and/or birth weight ≤1500 g who underwent ROP screening were included. Results: One hundred twenty-three neonates (31.9%) developed ROP. The sensitivity to identify type 1 ROP was as follows: WINROP, 100%; ROPScore, 100%; and CO-ROP, 92.3%. The specificity was 28% for WINROP, 1.4% for ROPScore, and 19.3% for CO-ROP. CO-ROP missed two neonates with type 1 ROP. WINROP provided the best performance for type 1 ROP with an area under the curve score at 0.61. Conclusion: The sensitivity was at 100% for WINROP and ROPScore for type 1 ROP; however, specificity was quite low for both algorithms. Highly specific algorithms tailored to our population may serve as a useful adjunctive tool to detect preterm infants at risk of sight-threatening ROP.

PurposeTo evaluate the sensitivity and specificity of four predictive algorithms (G-ROP, CO-ROP, Alex-ROP, and ROPscore) for retinopathy of prematurity and compare their performances in the Chinese population.MethodsA retrospective study was conducted at two medical centers in China of infants born at Women's Hospital School of Medicine Zhejiang University and Yiwu Maternal and Child Health Hospital. A total of 1,634 infants who met the criteria and who were GA

To determine trends in retinopathy of prematurity (ROP) in a Colorado cohort between 2006 and 2017 and compare trends in risk factors between our cohort and statewide data. A retrospective cohort study was conducted by the use of records from two registry databases: 1) an academic center's ROP registry, and 2) vital statistics birth data from the Colorado Department of Public Health and Environment (CDPHE). ROP was categorized as severe (type 1 or type 2), low grade (not type 1 or type 2), or no ROP. Other variables included in the analyses were gestational age and birth weight at delivery, and infant mortality. Trends over time were evaluated for both registry databases using generalized linear models. In our ROP registry cohort of 1,267 eligible infants, 134 (10.6%) developed severe ROP and 279 (22%) developed low-grade ROP. We found no overall trend in severe ROP rates (p =.23), and a decreasing trend in rates of low-grade ROP (p <.01) over the study period. Trends in gestational age, birth weight, and mortality rates remained stable during the study period in both the ROP registry and the CDPHE cohorts. The rate of severe ROP in our ROP registry cohort did not change over time. There was evidence of a decreasing trend in low grade ROP during the 12-year study period that was not explained by a change in the primary ROP risk factors in either the ROP registry cohort or the Colorado statewide data. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the sensitivity and specificity of four predictive algorithms (G-ROP, CO-ROP, Alex-ROP, and ROPscore) for retinopathy of prematurity and compare their performances in the Chinese population., Methods: A retrospective study was conducted at two medical centers in China of infants born at Women's Hospital School of Medicine Zhejiang University and Yiwu Maternal and Child Health Hospital. A total of 1,634 infants who met the criteria and who were GA < 32 weeks or BW < 2,000 g according to Chinese guidelines for ROP screening were included. The ROP group was further grouped into severe ROP and mild ROP. The sensitivity and specificity of G-ROP, two simplified G-ROPs, CO-ROP, Alex-ROP, and ROPscore were analyzed., Results: Severe ROP and any ROP were identified in 25 and 399 of 1,634 infants, respectively. According to the criteria of different models, 844, 1,122, 1,122, and 587 infants were eligible in the G-ROP, CO-ROP, Alex-ROP, and ROPscore, respectively. G-ROP had 96.0% sensitivity and 35.0% specificity for severe ROP. For two simplified G-ROPs (180 g and 200 g models), similar sensitivity was showed with original G-ROP and they had specificity of 21.8% and 14.0%, respectively. The sensitivity and specificity of Co-ROP were 96% and 64.3% for severe ROP, while Alex-ROP only had sensitivity of 56.0% and specificity of 61.4% for severe ROP. ROPscore had a sensitivity of 91.3% and a specificity of 62.4% for severe ROP. In 546 infants who met all 4 models' inclusion criteria and included 23 infants with severe ROP, the validation outcomes showed the sensitivity of G-ROP, ROPscore, CO-ROP, and Alex-ROP for severe ROP was 95.6%, 91.3%, 100%, and 56.0%, and their specificity was 38.0%, 60.8%, 39.9%, and 52.9%, respectively., Conclusion: G-ROP, ROPscore, and CO-ROP had high sensitivity for severe ROP in the Chinese population, but both the sensitivity and specificity of Alex-ROP were low. CO-ROP (not high-grade CO-ROP) provided the best performance for severe ROP in a fair comparison. For further application, ROP screening models need to be adjusted by local populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Lu, Lv, Cen, Tao, Zhang, Zhang, Mao, Chen, Wu, Chen and Shen.)