Sanghi Gaurav, Narang Anil, Narula Sunny, Dogra Mangat R
Department of Vitreo-Retina, Sangam Netralaya, Mohali, Punjab, India.
Department of Neonatology, Chaitanya Hospital, Chandigarh, India.
Indian J Ophthalmol. 2018 Jan;66(1):110-113. doi: 10.4103/ijo.IJO_486_17.
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.
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.
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).
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.
确定在线监测工具WINROP（https://winrop.com/）在检测印度早产儿威胁视力的1型视网膜病变（ROP）方面的效果。
将2014年6月至2016年8月出生的70名早产儿（孕周<32周）的出生体重、胎龄和每周体重测量值输入WINROP算法。根据每周体重增加情况，WINROP算法发出警报，表明婴儿有患威胁视力的1型ROP的风险。ROP筛查按照标准指南进行。使用研究组1型ROP的敏感性、特异性和患病率计算阴性和阳性预测值。计算95%置信区间（CI）。
在纳入研究的70名婴儿中，31名（44.28%）发生了1型ROP。所有婴儿中有74.28%（52/70）发出了WINROP警报，接受1型ROP治疗的婴儿中有90.32%（28/31）发出了警报。特异性为38.46%（15/39）。阳性预测值为53.84%（95%CI：39.59 - 67.53），阴性预测值为83.3%（95%CI：57.73 - 95.59）。
这是印度第一项使用基于体重增加算法预测ROP的研究。WINROP算法检测1型ROP的总体敏感性为90.32%。总体特异性为38.46%。对算法进行针对特定人群的调整可能会改善结果，并提高对眼科医生和新生儿科医生的实际效用。
