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Pediatrics. 2011 Mar;127(3):e607-14. doi: 10.1542/peds.2010-2240. Epub 2011 Feb 14.

A clinical prediction model to stratify retinopathy of prematurity risk using postnatal weight gain.

Author information

  • 1Division of Ophthalmology, Children's Hospital of Philadelphia, 34th Street and Civic Center Boulevard, 9-MAIN, Philadelphia, PA 19104, USA. binenbaum@email.chop.edu

Abstract

OBJECTIVE:

To develop an efficient clinical prediction model that includes postnatal weight gain to identify infants at risk of developing severe retinopathy of prematurity (ROP). Under current birth weight (BW) and gestational age (GA) screening criteria, <5% of infants examined in countries with advanced neonatal care require treatment.

PATIENTS AND METHODS:

This study was a secondary analysis of prospective data from the Premature Infants in Need of Transfusion Study, which enrolled 451 infants with a BW < 1000 g at 10 centers. There were 367 infants who remained after excluding deaths (82) and missing weights (2). Multivariate logistic regression was used to predict severe ROP (stage 3 or treatment).

RESULTS:

Median BW was 800 g (445-995). There were 67 (18.3%) infants who had severe ROP. The model included GA, BW, and daily weight gain rate. Run weekly, an alarm that indicated need for eye examinations occurred when the predicted probability of severe ROP was >0.085. This identified 66 of 67 severe ROP infants (sensitivity of 99% [95% confidence interval: 94%-100%]), and all 33 infants requiring treatment. Median alarm-to-outcome time was 10.8 weeks (range: 1.9-17.6). There were 110 (30%) infants who had no alarm. Nomograms were developed to determine risk of severe ROP by BW, GA, and postnatal weight gain.

CONCLUSION:

In a high-risk cohort, a BW-GA-weight-gain model could have reduced the need for examinations by 30%, while still identifying all infants requiring laser surgery. Additional studies are required to determine whether including larger-BW, lower-risk infants would reduce examinations further and to validate the prediction model and nomograms before clinical use.

PMID:
21321036
[PubMed - indexed for MEDLINE]
PMCID:
PMC3065141
Free PMC Article
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