A clinical prediction rule for the severity of congenital diaphragmatic hernias in newborns.

Background: Congenital diaphragmatic hernia (CDH) is a condition with a highly variable outcome. Some infants have a relatively mild disease process, whereas others have significant pulmonary hypoplasia and hypertension. Identifying high-risk infants postnatally may allow for targeted therapy.

Methods: Data were obtained on 2202 infants from the Congenital Diaphragmatic Hernia Study Group database from January 2007 to October 2011. Using binary baseline predictors generated from birth weight, 5-minute Apgar score, congenital heart anomalies, and chromosome anomalies, as well as echocardiographic evidence of pulmonary hypertension, a clinical prediction rule was developed on a randomly selected subset of the data by using a backward selection algorithm. An integer-based clinical prediction rule was created. The performance of the model was validated by using the remaining data in terms of calibration and discrimination.

Results: The final model included the following predictors: very low birth weight, absent or low 5-minute Apgar score, presence of chromosomal or major cardiac anomaly, and suprasystemic pulmonary hypertension. This model discriminated between a population at high risk of death (∼50%) intermediate risk (∼20%), or low risk (<10%). The model performed well, with a C statistic of 0.806 in the derivation set and 0.769 in the validation set and good calibration (Hosmer-Lemeshow test, P = .2).

Conclusions: A simple, generalizable scoring system was developed for CDH that can be calculated rapidly at the bedside. Using this model, intermediate- and high-risk infants could be selected for transfer to high-volume centers while infants at highest risk could be considered for advanced medical therapies.