New prediction model may better identify infants at risk of severe ROP

This study's authors report on their development of a simple equation that uses birth weight, gestational age and weight gain rate to predict the risk of severe retinopathy of prematurity (ROP) more accurately than current ROP screening guidelines. Because they devised a simple equation, the authors were able to create paper nomograms, which make it very easy to apply the model clinically. This research could lead to new screening guidelines that cut in half the number of babies that need to be examined for ROP. Much larger studies need to be done before the model can be used in practice.

While the current use of birth weight and gestational age cut-off levels is very effective at detecting infants who will develop severe ROP, less than 5 percent of infants examined actually end up requiring treatment. In recent years, researchers have studied the role of insulin-like growth factor 1 (IGF-1) in the development of ROP and have shown that slow rises in IGF-1 early in life are associated with the later development of severe ROP. Using a rather complex computer algorithm, their results suggest that postnatal growth, which is closely correlated with IGF-1, can be used to predict severe ROP more accurately. The current study confirms these findings and takes the idea a step further using a simpler statistical approach.

The authors conducted a secondary analysis of prospective data from the Premature Infants in Need of Transfusion (PINT) study, which included 367 infants with a birth weight of less than 1,000 grams. They used multivariate logistic regression to predict severe ROP (stage 3 or treatment).

Sixty-seven (18.3 percent) infants had severe ROP. The predictive model the authors developed, PINT-ROP, includes gestational age, birth weight and daily weight gain rate. Run weekly, an alarm indicating the need for eye examinations occurred when the predicted probability of severe ROP was greater than 0.085. This identified 66 of 67 infants with severe ROP, with a sensitivity of 99 percent, and all 33 infants requiring treatment. Median alarm-to-outcome time was 10.8 weeks (range, 1.9 to 17.6 weeks). Thirty percent (110 infants) did not trigger an alarm. The authors developed nomograms to determine risk of severe ROP by birth weight, gestational age and postnatal weight gain.

They conclude that use of the PINT-ROP model in this high-risk cohort could have reduced the need for examinations by 30 percent, while still identifying all infants requiring laser surgery but missing one infant with severe ROP. The say that use of the model has the potential to reduce the ROP examination burden, enable better health care resource allocation and identify early infants who may benefit from preventive interventions, such as IGF-1 supplementation and intensive nutritional management. Additional studies are required to determine whether including larger-birth weight, lower-risk infants would reduce examinations further and to validate the prediction model and nomograms.