|Year : 2018 | Volume
| Issue : 1 | Page : 110-113
WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm infants
Gaurav Sanghi1, Anil Narang2, Sunny Narula3, Mangat R Dogra4
1 Department of Vitreo-Retina, Sangam Netralaya, Mohali, Punjab, India
2 Department of Neonatology, Chaitanya Hospital, Chandigarh, India
3 Department of Neonatology, Cosmo Hospital, Mohali, Punjab, India
4 Department of Vitreo-Retina, Advanced Eye Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India
|Date of Submission||12-Jun-2017|
|Date of Acceptance||28-Sep-2017|
|Date of Web Publication||28-Dec-2017|
Dr. Gaurav Sanghi
Department of Vitreo-Retina, Sangam Netralaya, SCO 669, Sector 70, Mohali - 160 071, Punjab
Source of Support: None, Conflict of Interest: None
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.
Keywords: Developing country, low birth weight, retinopathy of prematurity, weight gain and retinopathy of prematurity, WINROP
|How to cite this article:|
Sanghi G, Narang A, Narula S, Dogra MR. WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm infants. Indian J Ophthalmol 2018;66:110-3
|How to cite this URL:|
Sanghi G, Narang A, Narula S, Dogra MR. WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm infants. Indian J Ophthalmol [serial online] 2018 [cited 2019 Jun 17];66:110-3. Available from: http://www.ijo.in/text.asp?2018/66/1/110/221786
Retinopathy of prematurity (ROP) is a potentially blinding disorder affecting the developing retina of premature infants. The major risk factors for developing ROP are low gestational age, low birth weight, and oxygen supplementation., In 2010, an estimated 14·9 million babies (uncertainty range: 12·3–18·1 million) were born preterm. This amounts to 11.1% of all live births worldwide. The screening guidelines formulated by the National Neonatology Forum suggest that infants born before 34 weeks of gestation or below 1750 g birth weight must be screened for ROP. Infants between 1750 and 2000 g should also be screened if risk factors for ROP are present. The large number of premature births, need for repeated ROP screening examinations, and paucity of experienced ROP surgeons poses a considerable challenge in India. Given this scenario, we need to look at alternative and innovative strategies for detecting ROP. Poor postnatal weight gain during the first few weeks of life is a strong predictor for the development of sight-threatening ROP  (Type 1 ROP requiring treatment according to the early treatment for ROP study). Various algorithms such as WINROP, CHOP-ROP, and ROP score use a weight gain predictive model for the occurrence of treatable ROP. These algorithms have demonstrated promise in accurately predicting ROP and reducing the number of ROP screening examinations in developed countries. However, these models have not been tested in Indian premature infants. The online monitoring tool, WINROP (https://winrop.com/), developed in Gothenburg, Sweden, is based on longitudinal weekly weight measurements and indicates an alarm if the infant is likely to develop Type 1 ROP requiring treatment., The present study evaluates the efficacy of WINROP algorithm in predicting ROP in a cohort of Indian premature infants.
| Methods|| |
The use of WINROP algorithm requires that the infant's gestational age is from 23 to 32 gestational weeks at birth, weekly weight measurements, and physiological weight gain of <450 g/week. Infants with hydrocephalus must be excluded due to nonphysiological weight gain. Final ROP outcome data should be available. Data of preterm infants, who met the above criterion, born and managed at two urban Level II neonatal intensive care units (NICU) of North India between June 2014 and August 2016 was entered into WINROP (https://winrop.com/). Oxygen supplementation was monitored and oxygen saturation targeted between 90% and 95% in both NICUs. ROP screenings were started at 4 weeks of birth, by a single experienced ROP surgeon (GS). Infants with birth weight ≤2000 g or gestational age ≤34 weeks were screened for ROP based on Indian ROP screening criterion. ROP was classified according to revised International Classification of ROP. All treatments were done according to the ETROP study guidelines. ROP screening was continued till treatment was required or complete vascularization of retina occurred. Data entered into the WINROP algorithm included birth weight, gestational age, and weekly weight measurements from birth until an alarm is signaled or a postmenstrual age of 35–36 weeks. Based on weekly weight measurements, WINROP signals either an alarm or no alarm. An alarm means that an infant is at risk for developing Type 1 ROP (requiring treatment according to ETROP guidelines). Based on actual ROP outcome, the sensitivity and specificity of WINROP alarm in predicting Type 1 ROP was calculated. The prevalence of type 1 ROP in the study cohort was further used to calculate the negative and positive predictive values of WINROP. 95% confidence intervals (CIs) were calculated.
| Results|| |
One hundred and ninety-nine infants were screened for ROP during the study period. Eight-seven infants who had a gestational age of ≥32 weeks were not eligible as WINROP algorithm requires that the infant's gestational age is from 23 to 32 weeks at birth. Of the remaining 112 infants with gestational age <32 weeks (eligible for WINROP), 36 with missing longitudinal weight data and 6 who did not complete ROP screening were excluded from the study. A substantial number of these were babies discharged early from NICU where successive weight data after discharge was not always possible. Remaining seventy infants with complete weight data and ROP outcome were enrolled in the study. None of these infants had nonphysiological weight gain. All included infants completed their final ROP screening follow-up. The median birth weight was 1075 g (range: 630–1610 g), and median gestational age was 28 weeks and 5 days(range: 245/7 - 316/7 weeks). [Table 1] describes the overall demographic profile and ROP outcome of all infants screened during this period.
|Table 1: Profile of infants screened and completing retinopathy of prematurity screening during the study period*|
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Of the seventy infants enrolled in WINROP, 31 (44.28%) developed Type 1 ROP, 12 (17.14%) had Type 2 ROP, and 27 (38.57%) had no ROP [Table 2].
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% and negative predictive value (NPV) was 83.3% [Table 3]. WINROP did not signal an alarm in three infants treated for Type 1 ROP. One of these infants was treated for aggressive posterior ROP (APROP) and two infants for high-risk prethreshold Stage 2 ROP in zone 2. The infant developing APROP was on a ventilator for 2 weeks, developed thrombocytopenia, and required blood transfusion.
|Table 3: Sensitivity, specificity, predictive values and likelihood ratio of WINROP alarm|
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The median time to alarm was 2 weeks from birth (range: at birth to 5 weeks). Forty-eight (92.3%) of the 52 alarms went off in the first 3 weeks of birth (i.e., before first screening at 4 weeks age), 3 alarms (5.7%) coincided with first screening at 4 weeks age, and 1 alarm (1.9%) occurred after first screening date. The median time from alarm to treatment for Type 1 ROP was 6 weeks (range: 2–11 weeks) [Figure 1] and [Figure 2].
|Figure 1: Distribution of infants in relation to the timing of alarm from birth|
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|Figure 2: Distribution of infants in relation to the weeks elapsed from alarm to treatment|
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| Discussion|| |
In the present study, the sensitivity of the alarm for Type 1 ROP was 90.32%. This is comparable to results from other developing countries, majority of which show sensitivity ranging from 80% to 90% [Table 4].,,,,,,,,,, In contrast, data from Sweden and the USA show a sensitivity of 100%., This difference may be due to diverse populations and NICU protocols. The alarm missed an infant with APROP in the present study. This infant had prolonged oxygenation and thrombocytopenia which have been reported as risk factors for APROP in the Indian subcontinent., It is likely that due to a complicated clinical course, APROP was missed by the alarm.
The overall specificity was low due to high false-positive rate. A recent resetting of the WINROP alarm leads to a modest increase in the specificity of the alarm. Due to low specificity/high false-positive rate, we need to do ROP screening as usual for infants with positive alarm.
Alarm was triggered at birth in five infants. Recent studies suggest low birth weight as an independent predictor of ROP outcome. However, only one of the five infants with positive alarm at birth in the present study developed treatable ROP. Most alarms (92.3%) occurred before first screening (3 weeks) and 5.7% at first screening (4 weeks). With more experience, we may be able to decrease the number of examinations for infants with no alarm till first 4 weeks.
The present study had certain limitations. First, the observer was not masked to the outcome of the WINROP alarm. This could introduce bias. Second, WINROP is not currently available for infants ≥32 weeks of gestation. This has implications in Indian scenario, where older and heavier premature infants can develop severe ROP. Further, WINROP misses the older premature infants (≥32 weeks) who are small for gestational age, sick, and likely to develop ROP. Inferences may not be accurate for large for gestational age infants >1500 g at birth. Third, the overall specificity of WINROP alarm is low due to a high false-positive rate. The positive predictive value was low at 53.84%. Hence, infants with positive alarm need ROP screening as usual. The NPV was modest at 83.3%. A negative result is useful in identifying those babies that are at lower risk for progression to ROP. An ideal scenario would need a NPV of 100% so as to confidently reduce the ROP screening examination for infants with no alarm. Finally, our study is limited by a small sample size. Longitudinal weight data were not available for a significant proportion of infants. A study involving a larger number of infants and addressing the pitfalls of the present study is required.
| Conclusion|| |
This is the first study from India using a weight gain-based algorithm for prediction of ROP. The sensitivity of WINROP algorithm in detecting Type 1 ROP was 90.32% and specificity was 38.46%. The results of the study are comparable to data from previous studies. Population-specific tweaking of algorithm may improve result and practical utility for ophthalmologists and neonatologists.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Palmer EA, Flynn JT, Hardy RJ, Phelps DL, Phillips CL, Schaffer DB, et al.
Incidence and early course of retinopathy of prematurity. The Cryotherapy for Retinopathy of Prematurity Cooperative Group. Ophthalmology 1991;98:1628-40.
Kinsey VE, Arnold HJ, Kalina RE, Stern L, Stahlman M, Odell G, et al.
PaO2 levels and retrolental fibroplasia: A report of the cooperative study. Pediatrics 1977;60:655-68.
Blencowe H, Cousens S, Oestergaard MZ, Chou D, Moller AB, Narwal R, et al.
National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: A systematic analysis and implications. Lancet 2012;379:2162-72.
Hellström A, Ley D, Hansen-Pupp I, Niklasson A, Smith L, Löfqvist C, et al.
New insights into the development of retinopathy of prematurity – Importance of early weight gain. Acta Paediatr 2010;99:502-8.
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;121:1684-94.
Binenbaum G. Algorithms for the prediction of retinopathy of prematurity based on postnatal weight gain. Clin Perinatol 2013;40:261-70.
Löfqvist C, Hansen-Pupp I, Andersson E, Holm K, Smith LE, Ley D, et al.
Validation of a new retinopathy of prematurity screening method monitoring longitudinal postnatal weight and insulinlike growth factor I. Arch Ophthalmol 2009;127:622-7.
Wu C, Vanderveen DK, Hellström A, Löfqvist C, Smith LE. Longitudinal postnatal weight measurements for the prediction of retinopathy of prematurity. Arch Ophthalmol 2010;128:443-7.
International Committee for the Classification of Retinopathy of Prematurity. The international classification of retinopathy of prematurity revisited. Arch Ophthalmol 2005;123:991-9.
Hård AL, Löfqvist C, Fortes Filho JB, Procianoy RS, Smith L, Hellström A, et al.
Predicting proliferative retinopathy in a Brazilian population of preterm infants with the screening algorithm WINROP. Arch Ophthalmol 2010;128:1432-6.
Wu C, Löfqvist C, Smith LE, VanderVeen DK, Hellström A, WINROP Consortium. et al.
Importance of early postnatal weight gain for normal retinal angiogenesis in very preterm infants: A multicenter study analyzing weight velocity deviations for the prediction of retinopathy of prematurity. Arch Ophthalmol 2012;130:992-9.
Zepeda-Romero LC, Hård AL, Gomez-Ruiz LM, Gutierrez-Padilla JA, Angulo-Castellanos E, Barrera-de-Leon JC, et al.
Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Mexican population of preterm infants. Arch Ophthalmol 2012;130:720-3.
Choi JH, Löfqvist C, Hellström A, Heo H. Efficacy of the screening algorithm WINROP in a Korean population of preterm infants. JAMA Ophthalmol 2013;131:62-6.
Sun H, Kang W, Cheng X, Chen C, Xiong H, Guo J, et al.
The use of the WINROP screening algorithm for the prediction of retinopathy of prematurity in a Chinese population. Neonatology 2013;104:127-32.
Lundgren P, Stoltz Sjöström E, Domellöf M, Källen K, Holmström G, Hård AL, et al.
WINROP identifies severe retinopathy of prematurity at an early stage in a nation-based cohort of extremely preterm infants. PLoS One 2013;8:e73256.
Piyasena C, Dhaliwal C, Russell H, Hellstrom A, Löfqvist C, Stenson BJ, 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:F29-33.
Ko CH, Kuo HK, Chen CC, Chen FS, Chen YH, Huang HC, et al.
Using WINROP as an adjuvant screening tool for retinopathy of prematurity in Southern Taiwan. Am J Perinatol 2015;30:149-54.
Koçak N, Niyaz L, Ariturk N. Prediction of severe retinopathy of prematurity using the screening algorithm WINROP in preterm infants. J AAPOS 2016;20:486-9.
Shah PK, Narendran V, Kalpana N. Aggressive posterior retinopathy of prematurity in large preterm babies in South India. Arch Dis Child Fetal Neonatal Ed 2012;97:F371-5.
Vinekar A, Hegde K, Gilbert C, Braganza S, Pradeep M, Shetty R, et al.
Do platelets have a role in the pathogenesis of aggressive posterior retinopathy of prematurity? Retina 2010;30:S20-3.
Lundgren P, Stoltz Sjöström E, Domellöf M, Smith L, Wu C, VanderVeen D, et al.
The specificity of the WINROP algorithm can be significantly increased by reassessment of the WINROP alarm. Neonatology 2015;108:152-6.
Sanghi G, Dogra MR, Katoch D, Gupta A. Aggressive posterior retinopathy of prematurity in infants ≥1500 g birth weight. Indian J Ophthalmol 2014;62:254-7.
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[Table 1], [Table 2], [Table 3], [Table 4]