Exploratory study of non-invasive, high-resolution functional macular imaging in subjects with diabetic retinopathy

AIM: To evaluate the predicting efficacy of severe retinopathy of prematurity (ROP) by the WINROP Algorithm (http://winrop.com) in Southern China. METHODS: All preterm infants with the gestational age (GA) less than 32wk were included from February 2015 to January 2019. Their ROP screening results and serial postnatal body weight were analyzed retrospectively. Weekly body weight was entered into and measured by the WINROP system. The outcomes were analyzed and the sensitivity, specificity, positive predictive value and negative predictive value were calculated. RESULTS: Totally 432 infants with a median GA of 30.0 (24.0-31.9)wk, and a median birth weight (BW) of 1360 (540-2700) g were included. Among these 432 infants, 50 were diagnosed as type 1 ROP but only 28 were identified by the WINROP algorithm. The sensitivity was 56% (28/50) and the negative predictive value (NPV) was 92% (252/274). However, for infants with BW <1000 g or GA <28wk, the sensitivity was 93.8% (15/16) and 93.3% (14/15), respectively. Meanwhile, with several postnatal complications added as additional risk factors, the sensitivity was increased to 96% (48/50). CONCLUSION: The sensitivity of the WINROP algorithm from the Southern Chinese cohort is not as high as that reported in developed countries. This algorithm is effective for detecting severe ROP from extremely small or preterm infants. Modification of the algorithm with additional risk factors could improve the predictive value for infants with a GA>28wk in China.