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

Application of WINROP to predict severe retinopathy of prematurity in a multi-ethnic cohort in Singapore

AIM

To investigate the validity of WINROP use in multi-ethnic population in a tertiary centre in Singapore.

METHODS

Birth weight, gestational age, and weekly weight measurements of four hundred two preterm infants (<32 weeks gestation) born between year 2011 and 2019 were entered into WINROP algorithm. Based on their weekly weight gain, WINROP algorithm would signal an alarm if the infant is at risk for type 1 ROP requiring treatment. The WINROP result is then compared with the ophthalmological findings. All the infants were screened based on the hospital ROP screening protocol. The negative predictive value, positive predictive value, sensitivity and specificity were calculated.

RESULTS

Among all the infants enrolled, 31 infants developed type 1 ROP. WINROP successfully signalled 23 out of 31 correctly. The calculated sensitivity was 74.2 % and specificity was 48.0 %. The positive predictive value was 10.6% and negative predictive value was 95.7%.

CONCLUSIONS

Our study showed that when WINROP is applied to our multi-ethnic population, it has a moderate sensitivity of 74.2% and a high predictive negative value of 95.7%. We would not recommend it to be used a solitary screening method but it can be used to better risk stratify preterm infants at risk of type 1 ROP, particularly in resource limited settings.

Validation of WINROP algorithm as screening tool of retinopathy of prematurity among Egyptian preterm neonates

BACKGROUND

Retinopathy of prematurity (ROP) is a leading cause of preventable childhood blindness worldwide. Proper screening for ROP can prevent loss of vision. WINROP (weight, insulin-like growth factor 1, neonatal, retinopathy of prematurity) is an online surveillance system based on gestational age, birth weight and weekly weight gain that can predict infants at risk of sight-threatening retinopathy of prematurity.

AIMS

To evaluate the diagnostic accuracy of WINROP algorithm in detecting sight-threatening ROP in Egyptian preterm neonates.

METHODS

Birth weight (BW), gestational age (GA) and weekly weight measurement of 365 preterm infants were prospectively entered into WINROP algorithm. Based on these inputs, the algorithm would output and a screening was performed as is standard. Sensitivity, specificity, and predictive values were calculated by comparing WINROP outcomes with ROP screening outcomes.

RESULTS

Of the infants included in the study the mean GA was ±31.24 and mean BW was ±1508.78. A high risk WINROP alarm was triggered in 62 infants of whom 16 infants develop type 1 or type 2 ROP. These infants had associated comorbidities including sepsis, Intraventricular hemorrhage (IVH), Necrotizing enterocolitis (NEC), history of transfusion of packed red blood cells (RBCS) and history of platelet transfusion. A low risk WINROP alarm was triggered in 303 infants of whom 15 infants developed type 1 or type 2ROP. WINROP showed a sensitivity of 51.6%, a specificity of 86.2%, a positive predictive value (PPV) of 52.8% and a negative predictive value (NPV) of 95% for detection of type 1 or type 2 ROP.

CONCLUSION

WINROP has low sensitivity and high specificity for detection of ROP. It may help in ROP prediction but can't be used alone. Modification of WINROP algorithm taking into account other risk factors may improve sensitivity and reduce number for ROP examination.

Retinopathy of Prematurity-Targeting Hypoxic and Redox Signaling Pathways

Retinopathy of prematurity (ROP) is a proliferative vascular ailment affecting the retina. It is the main risk factor for visual impairment and blindness in infants and young children worldwide. If left undiagnosed and untreated, it can progress to retinal detachment and severe visual impairment. Geographical variations in ROP epidemiology have emerged over recent decades, attributable to differing levels of care provided to preterm infants across countries and regions. Our understanding of the causes of ROP, screening, diagnosis, treatment, and associated risk factors continues to advance. This review article aims to present the pathophysiological mechanisms of ROP, including its treatment. Specifically, it delves into the latest cutting-edge treatment approaches targeting hypoxia and redox signaling pathways for this condition.

ASSESSMENT OF NEONATAL CARE STANDARD BY THE PREDICTIVE MODEL FOR RETINOPATHY OF PREMATURITY BASED ON WEIGHT GAIN

Care of extremely premature infants is in constant need for evaluation and progress. WINROP, a predictive model based on weight gain, has been developed to reduce the number of stressful examinations for retinopathy for prematurity. Validation studies of WINROP emphasize the difference of applicability in neonatal units of various practice. The aim of the study was to assess the standard of neonatal care by WINROP. Data on extremely premature infants were collected from medical records and entered in WINROP. High- and low-risk WINROP distribution and retinopathy of prematurity outcomes were analyzed. Fifty-four infants, gestational age ≤28 weeks, were included in the study after exclusion of weight related comorbidities. High risk was noted in 74% (n=40) of infants with 24% (n=13) developing retinopathy of prematurity requiring treatment. In low alarm group, there were 3 cases with severe disease. In conclusion, WINROP is not just a provider of predictive information on the severity of retinopathy of prematurity. High-risk alarm indicates the need of adjustment of nutritional strategies. Infants without pathological growth morbidities who develop severe retinopathy of prematurity in low-risk group point to other risk factors for retinopathy of prematurity to be evaluated and changed in future practice.

Prediction model to predict type 1 retinopathy of prematurity using gestational age and birth weight (PW-ROP)

PURPOSE

To develop a prediction model for type 1 retinopathy of prematurity (ROP) from an Asian population.

METHODS

This retrospective cohort study included 1043 premature infants who had ROP screening in a tertiary hospital in Hong Kong from year 2006 to 2018. The ROP prediction model was developed by multivariate logistic regression analyses on type 1 ROP. The cut-off value and the corresponding sensitivity and specificity were determined by receiver operating characteristic curve analysis. A validation group of 353 infants collected from another tertiary hospital in another region of Hong Kong from year 2014 to 2017 was used for external validation.

RESULTS

There were 1043 infants in the study group. The median gestational age (GA) was 30 weeks and 1 day and median birth weight (BW) was 1286 g. The prediction model required only GA and BW as parameters (prematurity-birth weight ROP (PW-ROP)). The area under curve value was 0.902. The sensitivity and specificity were 87.4% and 79.3%, respectively. Type 1 ROP developed in 0.9%, 17.4% and 50% of infants with PW-ROP scores<0, between 0 and <300, and ≥300 respectively (p<0.001). On external validation, our prediction model correctly predicted 95.8% of type 1 ROP (sensitivity=95.8%, specificity=74.8%) in the validation group.

CONCLUSION

The PW-ROP model is a simple model which could predict type 1 ROP with high sensitivity and specificity. Incorporating this model to ROP examination would help identify infants at risk for ROP treatment.

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.

Haemoglobin Levels in Early Life among Infants with and without Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a proliferative retinal vascular disorder attributed to an ischaemic stimulus in preterm infants. Haemoglobin, the main component for oxygen transportation, may be implicated in ROP development. This retrospective study compared the mean weekly haemoglobin levels between infants with and without ROP over the first six weeks of life. Premature infants of less than 32 weeks gestational age and less than 1.5 kg birth weight were grouped into age and birth weight-matched ROP cases and controls. Weekly mean haemoglobin levels were documented. An independent t-test was used to analyze the difference in mean haemoglobin levels between infants with ROP and infants without ROP. Adjustment for confounders was performed using one-way analysis of covariance. There was a statistically significant difference in adjusted mean haemoglobin levels between the ROP and non-ROP group during the first week of life (p = 0.038). No significant intergroup differences were observed at the other weeks. Haemoglobin monitoring during the first week of postnatal life may be useful to guide ROP screening in premature infants.