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Abstract

Background. The Jeddah retinopathy of prematurity (JED-ROP) algorithm, which is more specific to the population in Saudi Arabia, was established to decrease the number of infants screened without missing type 1 ROP cases. Methods. The data reviewed were birth weight (BW), gestational age (GA), weekly postnatal weight gain (PWG), and relevant perinatal risk factors. The sensitivities and specificities for detecting type 1 ROP were calculated. Results. Of the 502 infants included in the study, 148 developed ROP. The JED-ROP algorithm demonstrated 100% sensitivity and 38.9% specificity for recommending the screening of infants with GA ≤30 weeks and BW <1501 g and blood transfused <6 weeks and/or 3-week PWG <100 g in the type 1 ROP group. Conclusion. The JED-ROP algorithm can reduce the number of infants requiring ROP screening by 35.7% without missing type 1 ROP. The algorithm can be an adjunct to current national screening guidelines.

Keywords

birth weight postnatal preterm weight gain Saudi Arabia

Introduction

Retinopathy of prematurity (ROP) is a vasoproliferative disease characterized by the formation of abnormal blood vessels in the retinas of preterm infants.¹ If treatment is not administered, ROP can cause detachment of the retina and lifelong loss of vision. Screening for ROP facilitates timely detection in infants who need treatment and hence substantially decreases the chances of severe visual damage.²

In Saudi Arabia, ROP incidence ranges from 23% to 56%.^3-7 Preterm infants that are primarily at highest risk for ROP are those with very low birth weight (BW, approximately ≤1500 g) or gestational age (GA, those born at or before 30 weeks).⁸ Accordingly, an infant who is smaller or younger at birth is expected to have a greater likelihood of developing ROP.⁹ Previous studies demonstrated that other ROP risk factors develop during the postnatal period, which contribute to higher ROP incidences. These factors included mechanical ventilation use, blood transfusion, and poor weight gain (WG) in the initial weeks of life.^3,5

Slow postnatal WG (PWG) is an additional factor contributing to ROP occurrence,¹⁰ and acts as an indirect indicator of low serum insulin-like growth factor 1 (IGF-1) levels.^11,12 These developments result in compromised vascular endothelial growth factor (VEGF) signaling, angiogenesis, and retinal vascular growth, which lead to ROP.^13,14 Several algorithms were established to enhance ROP prediction sensitivity.¹⁵ Developed countries established the weight, IGF, neonatal ROP (WINROP) algorithm, and other models such as the Postnatal Growth and ROP study (G-ROP) and Colorado-ROP model (CO-ROP) to improve ROP detection through effective screening.^16-18 The WINROP algorithm incorporates PWG to detect early ROP indications to reduce the number of infants to be screened.^11,12 The CO-ROP model incorporates BW, GA, and 1-month PWG; when validated in the USA in a large multicenter cohort, it demonstrated 96.6% sensitivity for predicting the risk of developing severe ROP.¹⁹ A study in India by Thomas et al²⁰ revealed that 3 different algorithms namely WINROP, CHOP-ROP, and ROPscore demonstrated low sensitivity of between 54% and 85% for detecting type 1 ROP. The Alexandria ROP (Alex-ROP) is another algorithm based on the same concept as the CO-ROP but with different WG cut-offs. The Alex-ROP was established in Egypt to study the influence of PWG for improving ROP prediction to 100% sensitivity in a developing country.²¹ Lastly, the ROPScore algorithm developed in Brazil incorporates mechanical ventilation use and blood transfusion need, where an area under the curve (AUC) of 0.88 predicts severe ROP.²²

The ROP development risk factors that are the most significant are GA and BW, but current cut-offs tend to lead to over-screening. Given the differences in neonatal care quality and disease characteristics in developing countries, we aimed to establish a new algorithm that is better suited to developing countries generally and to Saudi Arabia specifically to decrease the number of infants who undergo screening for ROP to be used as a complementary tool along with Saudi national ROP screening guidelines. The influence of different post-natal risk factors was examined to assess the most important predictors involved in screening ROP in a preterm infant cohort at 2 tertiary hospitals in Jeddah, Saudi Arabia.

Materials and Methods

This retrospective cohort study included preterm infants who had been admitted to the neonatal intensive care units (NICU) of 2 tertiary hospitals in Jeddah [King Abdulaziz Medical City, National Guard Hospital (NGH) and King Abdulaziz University Hospital (KAUH)]. During the study period, preterm infants underwent ROP screening from January 2015 to September 2021 at KAUH (n = 303) and between March 2015 and September 2021 at NGH (n = 199). The inclusion criteria were based on current American Academy of Pediatrics (AAP), American Academy of Ophthalmology, and American Association for Pediatric Ophthalmology and Strabismus guidelines for ROP screening, which recommend screening all preterm infants with BW ≤1500 g and/or GA ≤30 weeks.⁸ The screening due time for first examination began when the infant was 4 weeks old or 31 weeks corrected GA, whichever came later.⁸

A sample size of 139 was calculated using a single calculation proportion formula by considering that the prevalence of 10% of patients are treatable ROP and to estimate the expected proportion with 5% absolute precision and 95% confidence.²³ To increase statistical power, >500 participants were included in the study. Our cohort contained 502 infants. The infants’ medical records were reviewed and their demographic data were collected, which included gender, birth date, BW, GA, and relevant associated comorbidities (intraventricular hemorrhage [IVH], sepsis, necrotizing enterocolitis [NEC], bronchopulmonary dysplasia, respiratory distress syndrome, mechanical ventilation need, and blood transfusion). Infants were diagnosed with sepsis when it was either proven by positive blood culture or a clinical diagnosis of sepsis with positive sepsis markers. Bronchopulmonary dysplasia diagnosis included infants who required mechanical ventilation and oxygen therapy at corrected GA 36 weeks.²⁴ IVH was diagnosed based on the Papille classification of IVH.²⁵ Any preterm infant in our study who was diagnosed with NEC had stage IIA or worse as per the Bells classification.²⁶ The infants were weighed daily and their weekly WG was recorded until the infant was discharged, if available. The weekly WG was calculated relative to the initial BW. We also documented data on the ROP stage and zone, plus disease (absence or presence), and ROP treatment type, if given. The ROP stage and zone were described based on the International Classification of ROP (ICROP).²⁷ The infants were classified into 3 groups based on the worst ROP grade noted during screening: Type 1 ROP, any-stage ROP, and no ROP. The ROP was continuously evaluated until complete regression and maturation, which may reach 1 year of age, to check for complications. Infants that met the Early Treatment of ROP (ETROP) definition of type 1 ROP required treatment with either intravitreal injection of anti-VEGF agents or with laser based on the guidelines of the ETROP and BEAT-ROP studies.^28,29

Ethical Approval

The hospital research ethics committees granted this study ethical clearance (JED-20-427780-86181 and KAUH No. 313/1-20). The study was conducted according to the ethical standards of the 1964 Helsinki Declaration. As this was a retrospective study, the need for informed consent was waived.

Statistical Methodology

The data were analyzed using IBM SPSS version 23 (IBM Corp., Armonk, NY, USA). The variable characteristics were defined by simple descriptive statistics, where categorical and nominal variables are reported as counts and percentages, and continuous variables are presented as the means and standard deviations. The new JED-ROP algorithm was introduced using the binary logistics regression model, with the conditions of: BW ≤1500 g and GA ≤30 weeks and (blood transfusion need by end of week 6 = Yes or 3-week WG ≤100 g). The chi-square test was used to establish the relationships between categorical variables. Percentages were used to express specificity, sensitivity, prevalence of disease, accuracy, and positive and negative predictive values. The accuracy, sensitivity, and specificity confidence intervals were exact Clopper-Pearson confidence intervals. One-way analysis of variance (ANOVA) was used to compare >2 group means, and the post hoc test was the least significant difference (LSD). Normal distribution assumption was used when conducting the tests. Otherwise, an alternative to the LSD was the Games-Howell procedure for multiple groups. A receiver operating characteristic (ROC) curve was used to assess the performance of the classification scheme, where we classified the infants according to 1 variable with 2 categories. The null hypothesis rejection criterion was a conventional P < .05.

Results

All 502 infants were screened as they met the inclusion criteria. Among the infants, 255 were male (50.8%), 335 (66.7%) were delivered by caesarean section, and 167 (33.3%) were born via spontaneous vaginal delivery (SVD). The mean GA was 28.55 ± 2.4 weeks and the mean BW was 1107 ± 314 g.

ROP was detected in 148 infants (29.5%), of which the majority had stage 1 ROP (58.1%, n = 86). Stage 2 and 3 ROP was detected in 39 infants (26.4%) and 22 infants (14.9%), respectively, while 1 infant (0.7%) had stage 4 ROP. Among the infants with ROP, 3 (2%) had zone 1 involvement and 86 (58%) had zone 2 involvement in one or both eyes. Of the ROP patients, 106 (21.1%) regressed spontaneously and 42 (8.3%) of the total study group required treatment. The most common treatment was by laser in 31 patients (73.8%) in one or both eyes. Five infants (11.9%) needed treatment with anti-VEGF agents (bevacizumab [Avastin] or aflibercept [Eylea]), 4 patients (9.5%) needed both laser and anti-VEGF treatment, and 2 patients (4.8%) needed combined laser and anti-VEGF and surgery.

The mean 1-month WG was 137.9 ± 105.3 g, 193.8 ± 135.6 g, and 284.3 ± 182.8 g in the type 1 ROP, any-stage ROP, and no ROP group, respectively. As expected, PWG was lowest in the type 1 ROP group as compared with the any-stage ROP and no ROP groups (P ≤ .001). Table 1 depicts the difference in patient demographics, characteristics, and risk factors based on ROP severity. Analysis of the weekly PWG in infants who developed type 1 ROP using multiple logistic regression models determined that BW (P < .001), followed by need for blood transfusion by week 6 (P = .006), were the most significant predictors. Additionally, BW (P < .001), followed by blood transfusion need by week 6 (P = .017) and GA (P = .024), were the most significant predictors of any-stage ROP. The WG cut-off was determined at 100 g for the mean WG at day 21 (D21), which yielded the highest specificity in our cohort. The AUC was highest when the GA and BW were combined with PWG and/or blood transfusion by 6 weeks. We followed the standard cut-off of GA ≤30 weeks and BW ≤1500 g as per AAP 2013 guidelines. The proposed JED-ROP algorithm recommended ROP screening for infants with GA ≤30 weeks and BW ≤1500 g and average PWG D21 <100 g and/or blood transfusion by 6 weeks.

Table 1.

Infants’ Demographic Data and Risk Factors Based on ROP Severity.

Variable		Total infants	Any-stage vs type 1 ROP				P
			No ROP	Any-stage ROP		Type 1 ROP
			N = 354	N = 107		N = 41
			Mean ± SDMedian (range)	Mean ± SDMedian (range)		Mean ± SDMedian (range)
GA (weeks)		502	29.18 ± 2.3^A 29.0 (24.0 to 41.0)	27.45 ± 1.7^B 28.0 (23.0 to 30.0)		25.90 ± 2.1^C 26.0 (23.0 to 32.0)	<.001^a,c
Length of NICU stay (days)		500	63.60 ± 114.5^A 45.0 (4.0 to 1536.0)	79.41 ± 40.4^A,B 72.0 (17.0 to 229.0)		111.17 ± 40.7^B 110.0 (9.0 to 254.0)	.009^a,c
BW (g)		502	1188.90 ± 299.8^A 1190.0 (570.0 to 3130.0)	968.31 ± 259.8^B 950.0 (550.0 to 2060.0)		759.51 ± 179.4^C 740.0 (510.0 to 1355.0)	<.001^a,d
3-week WG (postnatal age)		487	148.12 ± 124.5^A 130.0 (−180.0 to 605.0)	95.12 ± 110.1^B 83.0 (−214.0 to 439.0)		63.44 ± 83.9^B 54.0 (−116.0 to 264.0)	<.001^a,d
4-week WG (postnatal age)		473	284.29 ± 182.8^A 280.0 (−188.0 to 1290.0)	193.83 ± 135.6^B 195.0 (−65.0 to 528.0)		137.93 ± 105.3^C 118.0 (−25.0 to 477.0)	<.001^a,d
Mean WG from 3 to 4 weeks (postnatal age)		488	215.79 ± 143.0^A 202.5 (−160.0 to 882.5)	142.08 ± 120.3^B 141.0 (−214.0 to 483.5)		100.68 ± 90.0^B 75.0 (−47.5 to 370.5)	<.001^a,d
	Number/total	Number (%)	Number (%)	Number (%)
Male	255/502	185 (72.5%)	50 (19.6%)	20 (7.8%)	0.583
IVH		76/390	45 (59.2%)	21 (27.6%)		10 (13.2%)	.018^b
Sepsis		146/390	101 (69.2%)	33 (22.6%)		12 (8.2%)	.491
NEC		35/390	22 (62.9%)	11 (31.4%)		2 (5.7%)	.359
BPD		19/389	10 (52.6%)	4 (21.1%)		5 (26.3%)	.005^b
RDS		330/330	232 (70.3%)	73 (22.1%)		25 (7.6%)	.232
Mechanical ventilation need by end of week 2		353/400	242 (68.6%)	85 (24.1%)		26 (7.4%)	.004^b
Blood transfusion need by end of week 2		168/502	82 (48.8%)	54 (32.1%)		32 (19.0%)	<.001^b
Blood transfusion need by end of week 6		270/502	156 (57.8%)	75 (27.8%)		39 (14.4%)	<.001^b

Abbreviations: BW, birth weight; BPD, bronchopulmonary dysplasia; GA, gestational age; IVH, intraventricular hemorrhage; RDS, respiratory distress syndrome; ROP, retinopathy of prematurity; NEC, necrotizing enterocolitis; NICU, neonatal intensive care unit; N/A, not available.

Significant using chi-square test at <.05 level.

Significant using ANOVA at .05 level.

Least significant difference post hoc test.

Games-Howell post hoc test.

Superscript capital letters ^A,B,C indicate post hoc multiple pairing summary indicator. Identical letters indicate the same statistical measure.

The JED-ROP model demonstrated 100% sensitivity and 38.9% specificity for type 1 ROP (Table 2). The only other 2 algorithms that demonstrated 100% sensitivity for type 1 ROP were the WINROP and ROP score. However, specificity was rather low. As applied to this cohort of infants, the JED-ROP model demonstrated 89.2% sensitivity and 46.1% specificity for any-stage ROP (Table 3). The receiver operating characteristic analyses for detecting type 1 ROP and any-stage ROP across the different algorithms are depicted in Figures 1 and 2, respectively. The application of the algorithm would have decreased the number of screenings by 35.7% (179/502) compared to the conventional guidelines alone and existing algorithms (WINROP, CO-ROP, Alex-ROP, ROPScore).

Table 2.

Comparison of Type 1 ROP via JED-ROP to Other Algorithms.

Type 1 ROP	WINROP alarm % (95% CI)	ROPScore alarm % (95% CI)	CO-ROP % (95% CI)	Hg CO-ROP % (95% CI)	Alex-ROP % (95% CI)	Hg Alex-ROP % (95% CI)	JED-ROP % (95% CI)
Sensitivity	100.0% (86.8%-100.0%)	100.0% (86.8%-100.0%)	90.0% (76.3%-97.2%)	80.0% (64.4%-90.95%)	85.7% (71.5%-94.6%)	59.5% (43.3%-74.4%)	100.0% (91.6%-100.0%)
Specificity	29.4% (24.7%-34.4%)	3.0% (1.5%-5.2%)	25.7% (21.7%-30.1%)	36.9% (32.4%-41.6%)	42.4% (37.8%-47.05%)	74.1% (69.8%-78.1%)	38.9% (34.4%-43.5%)
Disease prevalence*	6.8% (4.5%-9.9%)	6.6% (4.3%-9.5%)	8.3% (6.0%-11.1%)	8.3% (6.0%-11.1%)	8.4% (6.1%-11.1%)	8.4% (6.1%-11.1%)	8.4% (6.1%-11.1%)
Positive predictive value*	9.4% (8.9%-10.0%)	6.75% (6.6%-6.9%)	9.9% (8.9%-10.95%)	10.3% (8.8%-11.9%)	12.0% (10.5%-13.6%)	17.4% (13.5%-22.0%)	13.0% (12.2%-13.9%)
Negative predictive value*	100.0% (95.6%-100.0%)	100.0% (67.9%-100.0%)	96.6% (91.7%-98.65%)	95.35% (91.6%-97.5%)	97.0% (93.9%-98.6%)	95.25% (93.3%-96.7%)	100.0% (97.4%-100.0%)
Accuracy*	34.2% (29.45%-39.2%)	9.3% (6.7%-12.65%)	31.1% (26.95%-35.4%)	40.5% (36.1%-45.0%)	46.0% (41.6%-50.5%)	72.9% (68.8%-76.75%)	44.0% (39.6%-48.5%)

Abbreviations: Alex-ROP, Alexandria ROP algorithm; CI, confidence interval; CO-ROP, Colorado-ROP model; Hg CO-ROP, high-grade CO-ROP model; Hg Alex-ROP, high-grade Alex-ROP algorithm; JED-ROP, Jeddah ROP algorithm; ROP, retinopathy of prematurity; WINROP, weight, IGF, neonatal ROP algorithm.

Values are dependent on disease prevalence.

Table 3.

Comparison of Any-Stage ROP via JED-ROP to That of Other Algorithms.

Any-stage ROP	WINROP alarm % (95% CI)	ROPScore alarm % (95% CI)	CO-ROP % (95% CI)	Hg CO-ROP % (95% CI)	Alex-ROP % (95% CI)	Hg Alex-ROP % (95% CI)	JED-ROP % (95% CI)
Sensitivity	88.6% (81.3%-93.8%)	100.0% (96.8%-100.0%)	93.0% (87.4%-96.6%)	83.8% (76.7%-89.5%)	80.4% (73.1%-86.5%)	44.6% (36.4%-53.0%)	89.2% (83.0%-93.7%)
Specificity	34.2% (28.5%-40.3%)	3.9% (2.0%-6.9%)	32.6% (27.6%-38.0%)	43.6% (38.3%-33.6%)	48.6% (43.3%-53.9%)	78.0% (73.3%-82.2%)	46.1% (40.8%-51.4%)
Disease prevalence*	30.0% (25.4%-34.9%)	28.8% (24.4%-33.5%)	30.0% (25.9%-34.4%)	29.3% (25.3%-33.6%)	29.5% (25.5%-33.7%)	29.5% (25.5%-33.6%)	29.5% (25.5%-33.7%)
Positive predictive value*	36.6% (34.1%-39.2%)	29.6% (29.1%-30.1%)	37.2% (35.2%-39.3%)	38.1% (35.4%-41.0%)	39.5% (36.5%-42.7%)	45.8% (39.3%-52.5%)	40.9% (38.2%-43.6%)
Negative predictive value*	87.5% (80.3%-92.3%)	100.0% (67.9%-100.0%)	91.5% (85.4%-95.2%)	86.6% (81.4%-90.6%)	85.6% (80.8%-89.3%)	77.1% (74.3%-79.7%)	91.1% (86.4%-94.3%)
Accuracy*	50.5% (45.4%-55.7%)	31.6% (27.0%-36.4%)	50.7% (46.1%-55.3%)	55.4% (50.8%-59.9%)	58.0% (53.5%-62.3%)	68.1% (63.9%-72.2%)	58.8% (54.3%-63.1%)

Values are dependent on disease prevalence.

Figure 1.

ROC curve for type 1 ROP plotting sensitivity against the specificity of different algorithms to compare and evaluate their performance.

Figure 2.

ROC curve for any-stage ROP plotting sensitivity against the specificity of different algorithms to compare and evaluate their performance.

Discussion

With improved neonatal care and higher survival rates, more infants in developing countries are being diagnosed with ROP.³⁰ Screening infants is an essential first step for ROP early detection and management to avert loss of vision.⁸ Screening protocols vary between countries, where affected infants demonstrate differing demographics and characteristics. Recently, the National Eye Health Program of the Saudi Ministry of Health released national guidelines related to the screening and treatment of ROP, where assessment for infants with GA ≤32 weeks and/or BW ≤1500 g was recommended.³¹ Although these tests exert a degree of stress on the infants,³² they are essential and are aimed at identifying infants with type 1 ROP to prevent blindness. Accordingly, algorithms such as the WINROP, ROPScore, CO-ROP, and Alex-ROP were established to aid neonatologists and ophthalmologists in identifying infants with ROP development risk, ideally with higher specificity, to avoid over-screening the vast majority of lower-risk infants.^21,33

When applied to preterm infants at 2 tertiary centers in Jeddah, the JED-ROP model demonstrated 100% sensitivity for type 1 ROP and 89.2% sensitivity for any-stage ROP. In comparison to the revised 2018 ROP screening guidelines, the JED-ROP algorithm would theoretically decrease the total number of infants who need screening for ROP by 35.7% without overlooking infants with type 1 ROP.⁸ To identify PWG screening criteria, we attempted to observe the weekly PWG and other significant risk factors based on logistic regressions from both hospitals to recommend cut-off points to improve the forecasting of type 1 and non-type 1 ROP development. Raffa and Aljohani³⁴ determined that a higher frequency and volume of red blood cell (RBC) transfusion were associated with increased risk of ROP development. The possible mechanisms underlying the complications associated with RBC transfusions in preterm infants include increased incidence of oxidative injury secondary to the increase in iron levels or the release of inflammatory mediators from stored blood products.³⁵ The week 3 WG was a significant predictor, which was in accordance with another study conducted in Egypt,³⁶ which determined that WG at week 4 (P < .001) followed by week 3 (p = .003) were significantly lower in patients with type 1 ROP compared to patients with any-stage ROP or no ROP. Clinical studies demonstrated an association exists between an infant’s low serum IGF-1 and poor PWG and the risk of developing more severe ROP.^11,37,38

In comparison to the JED-ROP, other available WG screening models were validated with low sensitivity and specificity. The WINROP algorithm was validated between countries with varying sensitivity and specificity.^39-43 Moreover, inserting dates online to capture infants with severe ROP risk is tedious and time-consuming. The ROPScore algorithm incorporates mechanical ventilation use and blood transfusion need and recorded sensitivity of 100% for forecasting severe ROP in Brazil.²² WINROP and ROPScore^11,22 demonstrated 100% sensitivity in our cohort; however, the 2 algorithms demonstrated specificity as low as 29.4% and 3%, respectively, which resulted in high false positives. Therefore, they may not be suitable for application in our cohort. In the present cohort, the CO-ROP demonstrated only 90% sensitivity and 25.7% specificity, which potentially decreased the number of infants who would need ROP assessment by 35.7% as compared to that reported by Cao et al,¹⁷ where it was reduced by 23.7%. One explanation could be the differences in parenteral nutrition and neonatal protocols across countries. Another possible explanation is the different ethnicities included in these studies. Despite demonstrating higher sensitivity and specificity in the Egyptian cohort (100% sensitivity and 50.5% specificity),²¹ the Alex-ROP and high-grade (hg) Alex-ROP did not perform well in our cohort, where they demonstrated 85.7% and 59.5% sensitivity and 42.4% and 74.1% specificity, respectively. This is due to the fact that the missed treatable cases in our cohort had higher WG than the proposed cutoffs used in the Alex-ROP and Hg Alex-ROP algorithms.³⁶

The main strength of our study is that it is, to our knowledge, the first study wherein an algorithm was tailored to our population. Additionally, we compared our results to those of the currently widely available algorithms established in the same cohort of infants at a specific time, which yielded a fair comparison. Including preterm infants with a high ROP risk profile from 2 tertiary care centers improved representation of the national at-risk neonatal population. Additionally, the dichotomous criteria facilitated the detection of at-risk patients without requiring the use of nomograms or plotting weights longitudinally in the busy daily NICU routine. Nevertheless, our study has a few limitations. Its retrospective nature may have affected the results, as the JED-ROP algorithm was designed primarily for prospective use. Moreover, the main drawback is the small number of infants in our sample with type 1 ROP. It is necessary to ensure that no infant with ROP will be missed, especially those requiring treatment. Hence, the JED-ROP algorithm requires nationwide validation with larger numbers of infants with severe ROP for better generalization.

Conclusions

We established a new algorithm (JED-ROP) to better represent our population, where the algorithm demonstrated the highest sensitivity and specificity as compared to other established algorithms. The JED-ROP algorithm suggests ROP screening for infants with BW ≤1500 g and GA ≤30 weeks and with blood transfusion at <week 6 and/or 3-week WG <100 g. The JED-ROP algorithm did not miss any type 1 ROP cases, and would have reduced the total ROP screening burden by 35.7%. Furthermore, the JED-ROP is a user-friendly tool that can be used easily as an addition to existing national ROP screening guidelines to detect at-risk patients. Our findings should be validated in both different aspects of the Kingdom and nationwide in large-cohort multi-institutional studies, which would be a step toward improving ROP screening in our country.

Footnotes

Author Contributions

All authors (L.R. , A.A, A,A. S.A., S.A., S.A., H.A,, H.A., M.A.) met the ICJME criteria for authorship. Each author have participated sufficiently in this work and take full responsibility for appropriate portions of the content.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Lina Raffa

Sara AlNowaiser

References

Sabri

Ells

Lee

Dutta

Vinekar

Retinopathy of prematurity: a global perspective and recent developments. Pediatrics. 2022;150(3):e2021053924.

Kellner

Lorenz

Stahl

Krohne

TU.

Retinopathy of prematurity - update on classification, screening, and therapy. Klin Monbl Augenheilkd. 2022;239(3):346-363. (in German)

Ahmedhussain

Khayyat

Aldhahwani

, et al. Retinopathy of prematurity: incidence and perinatal risk factors in a tertiary hospital in Saudi Arabia. J Clin Neonatol. 2021;10(1):31-36.

Al-Qahtani

Al-Otaibi

Alabdulajabbar

, et al. Retinopathy of prematurity incidence and risk factors in a tertiary hospital in Riyadh, Saudi Arabia. Middle East Afr J Ophthalmol. 2019;26(4):235-239.

Badeeb

Raffa

AhmedHussain

, et al. Retinopathy of prematurity in Saudi Arabia: exploring maternal risk factors. Taiwan J Ophthalmol. 2021;11(4):359-366.

Mgharbil

Raffa

Alessa

Alamri

Screening premature infants for retinopathy of prematurity in a tertiary hospital in Saudi Arabia. Ann Saudi Med. 2020;40(2):87-93.

Waheeb

Alshehri

Incidence of retinopathy of prematurity at two tertiary centers in Jeddah, Saudi Arabia. Saudi J Ophthalmol. 2016;30(2):109-112.

Fierson

, American Academy of Pediatrics Section on Ophthalmology, American Academy of Ophthalmology, American Association for Pediatric Ophthalmology and Strabismus, American Association of Certified Orthoptists. Screening examination of premature infants for retinopathy of prematurity. Pediatrics. 2018;142(6):e20183061.

Thomas

Shah

Canning

Harrison

Lee

Dow

KE.

Retinopathy of prematurity: risk factors and variability in Canadian neonatal intensive care units. J Neonatal Perinatal Med. 2015;8(3):207-214.

10.

Chaves-Samaniego

Muñoz Hoyos

García Serrano

JL.

New evidence on the protector effect of weight gain in retinopathy of prematurity. An Pediatr (Engl Ed). 2021;95(2):78-85.

11.

Hellström

Hård

Engström

, et al. Early weight gain predicts retinopathy in preterm infants: new, simple, efficient approach to screening. Pediatrics. 2009;123(4):e638-e645.

12.

Löfqvist

Andersson

Sigurdsson

, et al. Longitudinal postnatal weight and insulin-like growth factor I measurements in the prediction of retinopathy of prematurity. Arch Ophthalmol. 2006;124(12):1711-1718.

13.

Hellstrom

Perruzzi

, et al. Low IGF-I suppresses VEGF-survival signaling in retinal endothelial cells: direct correlation with clinical retinopathy of prematurity. Proc Natl Acad Sci USA. 2001;98(10):5804-5808.

14.

Dai

Xiao

Wang

Neurovascular abnormalities in retinopathy of prematurity and emerging therapies. J Mol Med (Berl). 2022;100(6):817-828.

15.

Binenbaum

Algorithms for the prediction of retinopathy of prematurity based on postnatal weight gain. Clin Perinatol. 2013;40(2):261-270.

16.

Binenbaum

Bell

Donohue

, et al. Development of modified screening criteria for retinopathy of prematurity: primary results from the postnatal growth and retinopathy of prematurity study. JAMA Ophthalmol. 2018;136(9):1034-1040.

17.

Cao

Wagner

McCourt

, et al. The Colorado-retinopathy of prematurity model (CO-ROP): postnatal weight gain screening algorithm. J AAPOS. 2016;20(1):19-24.

18.

The Sahlgrenska Center for Pediatric Ophthalmology Research. Winrop. Accessed May 19, 2022. https://winrop.com/

19.

McCourt

Ying

Lynch

, et al. Validation of the Colorado retinopathy of prematurity screening model. JAMA Ophthalmol. 2018;136(4):409-416.

20.

Thomas

Madathil

Thukral

, et al. Diagnostic accuracy of WINROP, CHOP-ROP and ROPScore in detecting type 1 retinopathy of prematurity. Indian Pediatr. 2021;58(10):915-921.

21.

Ahmed

Badeeb

AA.

The Alexandria retinopathy of prematurity model (Alex-ROP): postnatal weight gain screening algorithm application in a developing country. Int J Ophthalmol. 2019;12(2):296-301.

22.

Eckert

Fortes Filho

Maia

Procianoy

RS.

A predictive score for retinopathy of prematurity in very low birth weight preterm infants. Eye (Lond). 2012;26(3):400-406.

23.

Dhand

Khatkar

. Statulator: an online statistical calculator. Sample size calculator for estimating proportion. 2014. Accessed May 6, 2023. http://statulator.com/SampleSize/ss1P.html

24.

Ehrenkranz

Walsh

Vohr

, et al. Validation of the National Institutes of Health consensus definition of bronchopulmonary dysplasia. Pediatrics. 2005;116(6):1353-1360.

25.

Papile

Burstein

Koffler

Incidence and evolution of subependymal and intraventricular hemorrhage: a study of infants with birth weights less than 1,500 gm. J Pediatr. 1978;92(4):529-534.

26.

Walsh

Kliegman

RM.

Necrotizing enterocolitis: treatment based on staging criteria. Pediatr Clin North Am. 1986;33(1):179-201.

27.

International Committee for the Classification of Retinopathy of Prematurity. The international classification of retinopathy of prematurity revisited. Arch Ophthalmol. 2005;123(7):991-999.

28.

Early Treatment for Retinopathy of Prematurity Cooperative Group. Revised indications for the treatment of retinopathy of prematurity: results of the early treatment for retinopathy of prematurity randomized trial. Arch Ophthalmol. 2003;121(12):1684-1694.

29.

Mintz-Hittner

Kennedy

Chuang

AZ.

Efficacy of intravitreal bevacizumab for stage 3+ retinopathy of prematurity. N Engl J Med. 2011;364(7):603-615.

30.

Park

Hwang

Chang

Lee

Park

WS.

Survival rate dependent variations in retinopathy of prematurity treatment rates in very low birth weight infants. Sci Rep. 2020;10(1):19401.

31.

Al Amro

Al Aql

Al Hajar

, et al. Practical guidelines for screening and treatment of retinopathy of prematurity in Saudi Arabia. Saudi J Ophthalmol. 2018;32(3):222-226.

32.

Bulut

Tarak Bozkurt

Arslanoglu

Oral ibuprofen versus oral paracetamol in pain management during screening for retinopathy of prematurity: a prospective observational study. J Perinat Neonatal Nurs. 2022;36(3):305-311.

33.

Athikarisamy

Desai

Patole

Rao

Simmer

Lam

GC.

The use of postnatal weight gain algorithms to predict severe or type 1 retinopathy of prematurity: a systematic review and meta-analysis. JAMA Netw Open. 2021;4(11):e2135879.

34.

Raffa

Aljohani

Evaluation of the effect of blood transfusion on retinopathy of prematurity at a tertiary care center in Western Saudi Arabia. Cureus. 2022;14(4):e24495.

35.

Hirano

Morinobu

Kim

, et al. Blood transfusion increases radical promoting non-transferrin bound iron in preterm infants. Arch Dis Child Fetal Neonatal Ed. 2001;84(3):F188-F193.

36.

Raffa

Akeely

Alariefy

Alharbi

Alkhateeb

Khan

Retrospective validation of the Alexandria retinopathy of prematurity model in preterm infants in Saudi Arabia. J Clin Neonatol. 2023;12(1):7-12.

37.

Binenbaum

Tomlinson

de Alba Campomanes

, et al. Validation of the postnatal growth and retinopathy of prematurity screening criteria. JAMA Ophthalmol. 2020;138(1):31-37.

38.

Jensen

Ying

Huang

Quinn

Binenbaum

Postnatal serum insulin-like growth factor I and retinopathy of prematurity. Retina. 2017;37(5):867-872.

39.

Kuo

Chen

, et al. Using WINROP as an adjuvant screening tool for retinopathy of prematurity in southern Taiwan. Am J Perinatol. 2015;30(2):149-154.

40.

Piyasena

Dhaliwal

Russell

, et al. Prediction of severe retinopathy of prematurity using the WINROP algorithm in a birth cohort in South East Scotland. Arch Dis Child Fetal Neonatal Ed. 2014;99(1):F29-F33.

41.

Raffa

Alessa

Alamri

Malaikah

RH.

Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Saudi cohort of preterm infants. Saudi Med J. 2020;41(6):622-627.

42.

Sanghi

Narang

Narula

Dogra

MR.

WINROP algorithm for prediction of sight threatening retinopathy of prematurity: initial experience in Indian preterm infants. Indian J Ophthalmol. 2018;66(1):110-113.

43.

Sun

Kang

Cheng

, et al. The use of the WINROP screening algorithm for the prediction of retinopathy of prematurity in a Chinese population. Neonatology. 2013;104(2):127-132.

Jeddah Retinopathy of Prematurity (JED-ROP) Screening Algorithm

Abstract

Keywords

Introduction

Materials and Methods

Ethical Approval

Statistical Methodology

Results

Discussion

Conclusions

Footnotes

Author Contributions

Declaration of Conflicting Interests

Funding

ORCID iDs

References