Prediction of severe retinopathy of prematurity using the screening algorithm WINROP in preterm infants

Retrospective cohort evaluation of postnatal growth and retinopathy of prematurity (G-ROP) criteria in a tertiary centre in Turkey

PURPOSE

The postnatal growth and retinopathy of prematurity (G-ROP) study has proposed a new model to increase the effectiveness of screening retinopathy of prematurity (ROP). The present study aimed to evaluate the effectiveness of the G-ROP model in a tertiary centre in Turkey.

METHODS

The medical records of infants screened for ROP in our hospital between January 2018 and December 2022 were reviewed retrospectively. Babies with a documented ROP result and regular body weight measurements up to the 40th day of life were included in the study, and the G-ROP model was applied. The sensitivity of the G-ROP prediction model in detecting treated ROP, Type 1 ROP, Type 2 ROP, and low-grade ROP and the reduction in the number of babies to be screened by applying the model were calculated.

RESULTS

The G-ROP model was applied to a total of 242 infants. While 194 babies were determined for screening, 22 of them were treated. The sensitivity to predict treated ROP was 100%, and the specificity was 21.8%. The model successfully predicted all cases of Type 1 ROP in the cohort, while the sensitivity was 90.9% for Type 2 ROP and 90.7% for low-grade ROP. The G-ROP model reduced the number of infants requiring screening by 19.8% in our study.

CONCLUSIONS

The G-ROP model was successfully validated in our cohort in detecting treated ROP and Type 1 ROP, reducing the number of infants requiring screening by approximately 1 in 5.

Validation of three weight gain-based algorithms as a screening tool to detect retinopathy of prematurity: A multicenter study

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.

The Male to Female Ratio in Treatment-Warranted Retinopathy of Prematurity: A Systematic Review and Meta-analysis

Importance

Literature and anecdotal evidence suggest a relationship between male sex and retinopathy of prematurity (ROP). It is not known whether a difference, if present, is sex-related pathophysiologic predisposition or sex difference in meeting ROP screening criteria.

Objective

To evaluate the association of sex with the development of treatment-warranted ROP.

Data Sources

PubMed, Embase, and Web of Science databases were searched from 2000 to 2022. The search strategy used keywords including retinopathy of prematurity or ROP or retrolental fibroplasia and treatment or anti-VEGF or bevacizumab or ranibizumab or aflibercept or conbercept or laser or cryotherapy and gender or sex or male or female and medical subject headings terms.

Study Selection

All studies reporting on treatment with anti-vascular endothelial growth factor, laser photocoagulation, and/or cryotherapy for ROP were identified. Studies reporting sex distribution in the treatment group were included in the meta-analysis. Exclusion criteria included case reports, case series of fewer than 10 treated patients, systematic reviews, conference abstracts, letters to the editor, animal studies, and non-English records.

Data Extraction and Synthesis

Two reviewers independently screened and extracted the data following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The proportions of treated male and female infants were combined using random-effects meta-analysis.

Main Outcomes and Measures

Numbers and percentages of male and female infants treated for ROP.

Results

Of 11 368 identified studies, 316 met inclusion criteria, yielding a total of 31 026 treated patients. A higher percentage of male infants were treated for ROP (55% [95% CI, 0.54%-0.55%]), with low heterogeneity between studies (I2 = 34%; P < .001). Thirty-eight studies reported sex distribution in the screened population (170 053 patients; 92 612 [53%] male vs 77 441 [47%] female). There was no significant difference in the odds of receiving treatment between screened male and female infants (pooled odds ratio, 1.04 [95% CI, 0.91-1.18]; P = .67).

Conclusions and Relevance

More male infants are treated for ROP than female infants. This could be due to a known relative pathophysiological fragility of preterm male infants in addition to a difference in ROP screening rates, with more male infants meeting the criteria than female infants. These findings have implications for future studies and may prompt more careful clinical monitoring of male neonates.

Validation of the Postnatal Growth and Retinopathy of Prematurity Screening Criteria in a Taiwanese Cohort

PURPOSE

To validate the performance of Postnatal Growth and Retinopathy of Prematurity (G-ROP) screening criteria in a Taiwanese cohort.

DESIGN

Screening evaluation with retrospective data.

METHOD

Premature infants who underwent retinopathy of prematurity (ROP) screening between January 2015 and April 2019 at a tertiary hospital were examined. Infants with known final ROP results and complete longitudinal weight records were included. G-ROP screening criteria, both original and simplified (G-ROP 180 g), were applied as the prediction model for type 1 ROP; sensitivity and specificity were analyzed. The reduction in the number of infants requiring ROP screening and the number of funduscopic examinations were calculated.

RESULT

A total of 303 infants with documented ROP outcomes and complete weight gain records were examined. Of these, 103 infants developed ROP, of whom 29 developed type 1 ROP, whereas the other 200 did not develop ROP. For the detection of type 1 ROP, the sensitivity and specificity of the original G-ROP screening criteria were 96.6% and 42.3%, and 100% and 31%, for the simplified G-ROP 180 g model, respectively. The reduction in the number of infants requiring screening and funduscopic examinations was 32.6% and 33.5% for the original G-ROP criteria, and 28.1% and 23.2% for the G-ROP 180 g model, respectively.

CONCLUSION

Both the original G-ROP and G-ROP 180 g criteria attained high sensitivities in detecting type 1 ROP in the current Taiwanese cohort, with the G-ROP 180-g model outperforming the original one. Validation and modification may be required before applying G-ROP screening criteria to different populations.

The Use of Postnatal Weight Gain Algorithms to Predict Severe or Type 1 Retinopathy of Prematurity: A Systematic Review and Meta-analysis

IMPORTANCE

The currently recommended method for screening for retinopathy of prematurity (ROP) is binocular indirect ophthalmoscopy, which requires frequent eye examinations entailing a heavy clinical workload. Weight gain-based algorithms have the potential to minimize the need for binocular indirect ophthalmoscopy and have been evaluated in different setups with variable results to predict type 1 or severe ROP.

OBJECTIVE

To synthesize evidence regarding the ability of postnatal weight gain-based algorithms to predict type 1 or severe ROP.

DATA SOURCES

PubMed, MEDLINE, Embase, and the Cochrane Library databases were searched to identify studies published between January 2000 and August 2021.

STUDY SELECTION

Prospective and retrospective studies evaluating the ability of these algorithms to predict type 1 or severe ROP were included.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently extracted data. This meta-analysis was performed according to the Cochrane guidelines and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines.

MAIN OUTCOMES AND MEASURES

Ability of algorithms to predict type 1 or sever ROP was measured using statistical indices (pooled sensitivity, specificity, and summary area under the receiver operating characteristic curves, as well as pooled negative likelihood ratios and positive likelihood ratios and diagnostic odds ratios).

RESULTS

A total of 61 studies (>37 000 infants) were included in the meta-analysis. The pooled estimates for sensitivity and specificity, respectively, were 0.89 (95% CI, 0.85-0.92) and 0.57 (95% CI, 0.51-0.63) for WINROP (Weight, IGF-1 [insulinlike growth factor 1], Neonatal, ROP), 1.00 (95% CI, 0.88-1.00) and 0.60 (95% CI, 0.15-0.93) for G-ROP (Postnatal Growth and ROP), 0.95 (95% CI, 0.71-0.99) and 0.52 (95% CI, 0.36-0.68) for CHOP ROP (Children's Hospital of Philadelphia ROP), 0.99 (95% CI, 0.73-1.00) and 0.49 (95% CI, 0.03-0.74) for ROPScore, 0.98 (95% CI, 0.94-0.99) and 0.35 (95% CI, 0.22-0.51) for CO-ROP (Colorado ROP). The original PINT (Premature Infants in Need of Transfusion) ROP study reported a sensitivity of 0.98 (95% CI, 0.91-0.99) and a specificity of 0.36 (95% CI, 0.30-0.42). The pooled negative likelihood ratios were 0.19 (95% CI, 0.13-0.27) for WINROP, 0.0 (95% CI, 0.00-0.32) for G-ROP, 0.10 (95% CI, 0.02-0.53) for CHOP ROP, 0.03 (95% CI, 0.00-0.77) for ROPScore, and 0.07 (95% CI, 0.03-0.16) for CO-ROP. The pooled positive likelihood ratios were 2.1 (95% CI, 1.8-2.4) for WINROP, 2.5 (95% CI, 0.7-9.1) for G-ROP, 2.0 (95% CI, 1.5-2.6) for CHOP ROP, 1.9 (95% CI, 1.1-3.3) for ROPScore, and 1.5 (95% CI, 1.2-1.9) for CO-ROP.

CONCLUSIONS AND RELEVANCE

This study suggests that weight gain-based algorithms have adequate sensitivity and negative likelihood ratios to provide reasonable certainty in ruling out type 1 ROP or severe ROP. Given the implications of missing even a single case of severe ROP, algorithms with very high sensitivity (close to 100%) and low negative likelihood ratios (close to zero) need to be chosen to safely reduce the number of unnecessary examinations in infants at lower risk of severe ROP.

Validation of the DIGIROP-birth model in a Chinese cohort

Background

We aimed to validate the predictive performance of the DIGIROP-Birth model for identifying treatment-requiring retinopathy of prematurity (TR-ROP) in Chinese preterm infants to evaluate its generalizability across countries and races.

Methods

We retrospectively reviewed the medical records of preterm infants who were screened for retinopathy of prematurity (ROP) in a single Chinese hospital between June 2015 and August 2020. The predictive performance of the model for TR-ROP was assessed through the construction of a receiver-operating characteristic (ROC) curve and calculating the areas under the ROC curve (AUC), sensitivity, specificity, and positive and negative predictive values.

Results

Four hundred and forty-two infants (mean (SD) gestational age = 28.8 (1.3) weeks; mean (SD) birth weight = 1237.0 (236.9) g; 64.7% males) were included in the study. Analyses showed that the DIGIROP-Birth model demonstrated less satisfactory performance than previously reported in identifying infants with TR-ROP, with an area under the receiver-operating characteristic curve of 0.634 (95% confidence interval = 0.564–0.705). With a cutoff value of 0.0084, the DIGIROP-Birth model showed a sensitivity of 48/93 (51.6%), which increased to 89/93 (95.7%) after modification with the addition of postnatal risk factors. In infants with a gestational age < 28 weeks or birth weight < 1000 g, the DIGIROP-Birth model exhibited sensitivities of 36/39 (92.3%) and 20/23 (87.0%), respectively.

Conclusions

Although the predictive performance was less satisfactory in China than in developed countries, modification of the DIGIROP-Birth model with postnatal risk factors shows promise in improving its efficacy for TR-ROP. The model may also be effective in infants with a younger gestational age or with an extremely low birth weight.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-021-01952-0.

Use of an online screening algorithm - Weight, Insulin-derived growth factor 1, Neonatal Retinopathy of Prematurity (WINROP) for predicting retinopathy of prematurity in Indian preterm babies

Purpose:

Inopathy of prematurity (WINROP) Weight, insulin-derived growth factor 1, neonatal ROP algorithm is an online tool that has been validated as a predictor of retinopathy of prematurity (ROP) in various countries. The current study was designed to evaluate the predictive ability of WINROP algorithm (http://winrop.com) using postnatal weight gain in detecting Type 1 ROP in Indian babies.

Methods:

Prospective single centre observational study of 153 consecutive preterm babies who were eligible for screening for ROP as per the standard guidelines. Sixteen babies were excluded from the study because of various reasons. Thirty-five babies had gestational age ≥32 weeks and were ineligible for WINROP algorithm. Online WINROP algorithm was used for 102 babies with gestation at birth less than 32 weeks. The alarms triggered by WINROP were documented.

Results:

Laser treatment was done in 30 babies who developed Type 1 ROP. Of these, WINROP alarm was signaled in 24 babies and 6 babies developed ROP without any WINROP alarm. These babies had associated comorbidities like respiratory distress syndrome, patent ductus arteriosus, bacterial sepsis, and ventilatory support. WINROP alarm was significantly associated with Type 1 ROP (P < 0.001). The sensitivity of WINROP was 80% and specificity was 80.6% with a positive predictive value of 63.2% and negative predictive value of 90.6% in detecting Type 1 ROP. In the present study, no baby who was ineligible for WINROP developed Type 1 ROP.

Conclusion:

WINROP provides a novel online monitoring screening tool for identifying babies at risk of developing Type 1 ROP. In our cohort, none of the babies whose period of gestation was more than or equal to 32 weeks developed sight threatening Type 1 ROP. WINROP algorithm may also be useful in Indian population.

Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Saudi cohort of preterm infants

OBJECTIVES

To validate the web weight gain-based WINROP (weight, insulin-like growth factor I, neonatal, retinopathy of prematurity [ROP]) algorithm retrospectively to identify type 1 ROP in a Saudi cohort of premature infants.  Methods: The records of preterm infants (greater than 23 and less than 32 weeks gestation) born between August 2013 and October 2018, were reviewed. Birth weight, gestational age, and weekly weight measurements of the premature infants were entered online. Based on weekly weight gain, the WINROP algorithm alerted clinicians whether infants were at high-risk for vision‑threatening type 1 ROP. Sensitivity, specificity, positive and negative predictive values were calculated. Results: The median gestational age of the infants at birth was 28 weeks, with median birth weight at 1085 g. Of the 175 infants included in the study, 13 (7.4%) developed type 1 ROP. WINROP positive alarm was triggered in 70.9% (124/175) of all infants and 100% (13/13) of those treated for type 1 ROP. The specificity of the algorithm was 31.5%. Positive predictive values was 10.5% and negative was 100%. Conclusion: The general WINROP sensitivity in identifying type 1 ROP was 100% similar to that reported in developed countries; however, its specificity was low at 31.5%. Tweaking of the algorithm based on the population may increase the specificity and promote the practical utility of this non-invasive screening tool for ophthalmologists and neonatologists in this population.

Association between severe retinopathy of prematurity and postnatal weight gain in very low-birthweight infants at Chiang Mai University Hospital, Thailand

Background: Poor postnatal weight gain has been associated with low serum IGF-1, a key factor in the pathogenesis of retinopathy of prematurity (ROP).Aim: To investigate an association between relative weight gain (RWG) and severe ROP in very low-birthweight (VLBW) Thai infants.Methods: The medical records of VLBW infants who were admitted to the neonatal intensive care unit in Chiang Mai University Hospital from June 2014 to December 2016 and screened for ROP were reviewed. RWG and total calorie intake (TCI) in the 2nd, 4rth and 6th week of age were calculated and those with no ROP/mild ROP and severe ROP requiring laser treatment were compared.Results: The study included 139 VLBW infants, 24 (17.3%) of whom had ROP requiring laser treatment. Infants with severe ROP requiring laser treatment had a lower median birthweight (840 vs 1,195 g, p < 0.001) and median gestational age (GA) (27 vs 30 wk, p < 0.001) than those with no ROP/mild ROP. When RWG and TCI were compared, the infants with severe ROP requiring laser treatment had a lower RWG at the 2nd (p < 0.01) and 4th weeks of age (p < 0.05) and had a lower TCI at the 2nd week of age (p < 0.001) than those with no ROP/mild ROP. Multivariate logistic analysis demonstrated that GA <29.5 w (p < 0.01), hypotension (p < 0.05), RWG <2.9 g/kg/d (p < 0.05) and TCI <98.5 kcal/kg/day (p < 0.001) at the 2nd week of age were independent risk factors for severe ROP requiring laser treatment.Conclusions: Poor weight gain and low calorie intake at the 2nd week of age were associated with severe ROP requiring laser treatment in VLBW infants. Monitoring weight gain and calorie intake during this period are essential and may improve the outcome of ROP.Abbreviations: BPD, bronchopulmonary dysplasia; IVH, intraventricular haemorrhage; NEC, necrotising enterocolitis; PDA, patent ductus arteriosus; PRC, packed red cells; PVL, periventricular leucomalacia; RDS, respiratory distress syndrome; ROP, retinopathy of prematurity; RWG, relative weight gain; SGA, small for gestational age; TCI, total calorie intake; VLBW, very low birthweight.

Efficacy of WINROP as a Screening Tool for Retinopathy of Prematurity in the East Coast of Malaysia

Purpose

To evaluate the efficacy of the “weight, insulin-like growth factor 1, neonatal retinopathy of prematurity” (WINROP) algorithm in predicting retinopathy of prematurity (ROP) requiring treatment in Malaysia.

Participants

This was a retrospective study involving premature infants with gestational age less than 32 weeks treated from September 2016 to March 2019 in Hospital Universiti Sains Malaysia. Clinical diagnosis was made based on Early Treatment Retinopathy of Prematurity study. Participants’ weekly weight gain since birth was entered in the website (http://winrop.com), along with date of birth, gestational age and final clinical examination outcome. WINROP software signals an alarm if an infant is at high risk of developing ROP requiring treatment during weight data entry. By using the alarm status, the sensitivity and specificity of this algorithm for predicting ROP requiring treatment were obtained.

Results

Ninety-two infants were included in this study. An alarm was detected in 67 infants (72.8%). There were a total of 53 infants (54.6%) with no ROP, 15 (16.3%) of whom developed stage 1 ROP, 10 (10.8%) who developed stage 2 ROP and 14 infants (15.2%) who developed stage 3 ROP. In our study, WINROP sensitivity was 95.2% and specificity was 33.8%.

Conclusion

WINROP is recommended as an initial screening tool for premature infants at risk of developing treatment-requiring ROP in Malaysia. It may help to alert clinicians managing severely ill infants when clinical examinations are less possible.

Prediction of severe retinopathy of prematurity using the weight gain, insulin-like growth factor 1, and neonatal retinopathy of prematurity algorithm in a Japanese population of preterm infants

PURPOSE

To retrospectively investigate the sensitivity and specificity of weight gain, insulin-like growth factor 1, and neonatal retinopathy of prematurity (WINROP) algorithm for the prediction of severe retinopathy of prematurity (ROP) in a Japanese population of preterm infants. The WINROP algorithm is a tool based on postnatal weight gain.

STUDY DESIGN

Retrospective cohort study.

METHODS

The medical records of preterm infants born between January 2011 and March 2017 were retrospectively reviewed. Infants born after 33 weeks of gestation were excluded based on the indications of the WINROP algorithm. Postnatal weight was recorded weekly on the WINROP system until postmenstrual week 36. The sensitivity and specificity of the WINROP algorithm were analyzed.

RESULTS

In total, 278 infants were included in this study. Based on the WINROP algorithm 110 of these infants were predicted to be at low risk for developing severe ROP and 105 did not develop severe ROP. Based on the WINROP algorithm 168 infants were predicted to be at high risk for developing severe ROP　and 27 developed severe ROP. Thus, the sensitivity of the WINROP algorithm was 84.4% and the specificity 42.7%.

CONCLUSION

The WINROP algorithm could be used for preterm infants (gestational age of <28 weeks) without a complicated hospital course. Modification of the algorithm will improve its sensitivity and specificity for the Japanese population.

Algorithm for predicting threshold retinopathy of prematurity is insufficient and fundus examinations are still needed before 31 weeks

AIM

We evaluated the weight, insulin-like growth factor-1, neonatal, retinopathy of prematurity (WINROP) algorithm for very premature infants.

METHOD

Infants born before 32 weeks who had undergone fundus examinations in the neonatal intensive care unit at the University Hospital of Nancy were included in this French retrospective cohort study from July 2012 to July 2016. We evaluated how well the WINROP software predicted threshold retinopathy of prematurity (ROP).

RESULTS

We studied 570 infants with a mean gestational age of 28.7 ± 1.8 weeks and a mean birth weight of 1110 ± 297 g: 28.1% had ROP and 1.2% had threshold ROP. The overall WINROP sensitivity was 57.1%, specificity was 46.0%, predictive positive value was 1.3% and predictive negative value was 98.9%. At more than 30 weeks of gestation or 1250 g, these figures rose to a respective specificity of 100% and 95.7% and respective predictive negative value of 100% and 100%. There were independent associations between the severity of ROP and the Apgar score at five minutes, the duration of oxygen therapy and non-invasive ventilation.

CONCLUSION

WINROP worked better on preterm infants born from 31 weeks onwards or weighing over 1250 g. Fundus examinations remain necessary for infants born earlier or lighter.

WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm infants

Purpose:

To determine the efficacy of the online monitoring tool, WINROP (https://winrop.com/) in detecting sight-threatening type 1 retinopathy of prematurity (ROP) in Indian preterm infants.

Methods:

Birth weight, gestational age, and weekly weight measurements of seventy preterm infants (<32 weeks gestation) born between June 2014 and August 2016 were entered into WINROP algorithm. Based on weekly weight gain, WINROP algorithm signaled an alarm to indicate that the infant is at risk for sight-threatening Type 1 ROP. ROP screening was done according to standard guidelines. The negative and positive predictive values were calculated using the sensitivity, specificity, and prevalence of ROP type 1 for the study group. 95% confidence interval (CI) was calculated.

Results:

Of the seventy infants enrolled in the study, 31 (44.28%) developed Type 1 ROP. WINROP alarm was signaled in 74.28% (52/70) of all infants and 90.32% (28/31) of infants treated for Type 1 ROP. The specificity was 38.46% (15/39). The positive predictive value was 53.84% (95% CI: 39.59–67.53) and negative predictive value was 83.3% (95% CI: 57.73–95.59).

Conclusion:

This is the first study from India using a weight gain-based algorithm for prediction of ROP. Overall sensitivity of WINROP algorithm in detecting Type 1 ROP was 90.32%. The overall specificity was 38.46%. Population-specific tweaking of algorithm may improve the result and practical utility for ophthalmologists and neonatologists.