Prevalence and Risk Factors of Retinopathy of Prematurity in Iran

Purpose: The present study aimed to evaluate the frequency and risk factors of retinopathy of prematurity (ROP) among Iranian infants. Methods: A retrospective cohort study was conducted on infants who had undergone screening for ROP at Farabi Eye Hospital, between March 2016 and March 2017. Data were analyzed based on the presence of extreme prematurity (gestational age ≤ 28 weeks), extremely low-birth-weight (≤ 1000 g), and multiplegestation (MG) infants. Results: The prevalence of ROP was 27.28% (n = 543) among all screened infants, 74.4% for extremely preterm (EP) infants, 77.5% for extremely low birth weight (ELBW) babies, and 27.25% for infants from MG pregnancies. On multivariate analysis, gestational age, birth weight, and history of transfusion (P < 0.0001, P < 0.0001, and P = 0.04, respectively) were found to be significantly associated with ROP. More advanced stages of ROP (P < 0.0001) were observed in EP and ELBW infants. Birth weight (P = 0.088), history of transfusion (P = 0.066), and intubation (P = 0.053) were not associated with increased risk of ROP in EP infants, while gestational age (P = 0.037) and history of transfusion (P = 0.040) were significant risk factors for ROP in ELBW infants. Gestational age (P < 0.001) and birth weight (P = 0.001) were significantly associated with ROP in infants from MG pregnancies in multivariate analysis. Conclusion: ROP remains a commonly encountered disease, especially in ELBW and EP infants. The history of transfusion may have a role in stratifying the risk for ROP and guiding future screening guidelines

Prevalence and risk factors of retinopathy of prematurity in Iran: a systematic review and meta-analysis

Abstract Background Retinopathy of prematurity (ROP) refers to the developmental disorder of the retina in premature infants and is one of the most serious and most dangerous complications in premature infants. The prevalence of ROP in Iran is different in various parts of Iran and its prevalence is reported to be 1–70% in different regions. This study aims to determine the prevalence and risk factors of ROP in Iran. Methods This review article was conducted based on the preferred reporting items for systematic review and meta-analysis (PRISMA) protocols. To find literature about ROP in Iran, a comprehensive search was done using MeSH keywords in several online databases such as PubMed, Ovid, Science Direct, EMBASE, Web of Science, CINAHL, EBSCO, Magiran, Iranmedex, SID, Medlib, IranDoc, as well as the Google Scholar search engine until May 2017. Comprehensive Meta-analysis Software (CMA) Version 2 was used for data analysis. Results According to 42 studies including 18,000 premature infants, the prevalence of ROP was reported to be 23.5% (95% CI: 20.4–26.8) in Iran. The prevalence of ROP stages 1, 2, 3, 4 and 5 was 7.9% (95% CI: 5.3–11.5), 9.7% (95% CI: 6.1–15.3), 2.8% (95% CI: 1.6–4.9), 2.9% (95% CI: 1.9–4.5) and 3.6% (95% CI: 2.4–5.2), respectively. The prevalence of ROP in Iranian girls and boys premature infants was 18.3% (95% CI: 12.8–25.4) and 18.9% (95% CI: 11.9–28.5), respectively. The lowest prevalence of ROP was in the West of Iran (12.3% [95% CI: 7.6–19.1]), while the highest prevalence was associated with the Center of Iran (24.9% [95% CI: 21.8–28.4]). The prevalence of ROP is increasing according to the year of study, and this relationship is not significant (p = 0.181). The significant risk factors for ROP were small gestational age (p < 0.001), low birth weight (p < 0.001), septicemia (p = 0.021), respiratory distress syndrome (p = 0.036), intraventricular hemorrhage (p = 0.005), continuous positive pressure ventilation (p = 0.023), saturation above 50% (p = 0.023), apnea (p = 0.002), frequency and duration of blood transfusion, oxygen therapy and phototherapy (p < 0.05), whereas pre-eclampsia decreased the prevalence of ROP (p = 0.014). Conclusion Considering the high prevalence of ROP in Iran, screening and close supervision by experienced ophthalmologists to diagnose and treat the common complications of pre-maturity and prevent visual impairment or blindness is necessary

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

Abstract The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies