Abstract
PURPOSE: Children's Hospital of Philadelphia retinopathy of prematurity (CHOP ROP) model can be used to predict ROP, a leading cause of childhood blindness, using risk factors such as postnatal weight gain, birth weight (BW), and gestation age (GA). The purpose of this study was to determine the usefulness of the CHOP ROP for the prediction of treatable ROP. METHODS: This was a prospective observational study. Babies <34 weeks of GA, BW <2000 grams, and GA 34-36 weeks with risk factors such as respiratory distress syndrome (RDS) were included; ROP screening, follow-up, and treatment were performed based on national guidelines. The average daily postnatal weight gain was measured, and the CHOP nomogram was plotted. Babies were categorized as high risk or low risk based on the "CHOP" alarm. The sensitivity and specificity of the CHOP ROP for the detection of treatable ROP were determined. In case of poor sensitivity, a new cutoff alarm level was planned using logistic regression analysis. RESULTS: Of 62 screened infants, 23 infants did not fulfill the criteria of the CHOP algorithm and were excluded. Thus, in the study on 39 infants, the predictive model with an alarm level of 0.014 had 100% specificity and 20% sensitivity. With the "new" alarm level (cutoff) of 0.0003, the CHOP nomogram could detect all the infants who developed treatable ROP, that is, sensitivity increased to 100% but specificity decreased to 10.5%. CONCLUSION: The CHOP ROP model with a cutoff point (0.014) performed poorly in predicting severe ROP in the study. Thus, there is a need to develop inclusive and more sensitive tailor-made algorithms.