OBJECTIVE: To develop a predictive model capable of identifying which premature infants have the greatest probability of presenting bronchopulmonary dysplasia (BPD), based on assessment at the end of their first week of life. METHODS: Data were collected retrospectively from January 1998 to July 2001, and prospectively from August 2001 to July 2003. All children born at the institution with gestational age < 34 weeks and birth weight < 1,500 g were included. The principal risk factors for BPD were subjected to univariate analysis followed by logistic regression. Significant variables were used to construct a formula to calculate the probability of BPD. The model was calibrated and its discriminative power assessed using receiver operating characteristic (ROC) curves. Between August 2003 and July 2005 the model was then applied to a different population for validation. RESULTS: The sample comprised 247 children, of whom 68 developed BPD, classified as follows: mild = 35 (51.4%), moderate = 20 (29.4%) and severe = 8 (11.7 %). Four variables maintained significance with relation to BPD: gestational age < 30 weeks, persistent ductus arteriosus, mechanical ventilation > 2 days and loss of > 15% of birth weight on the seventh day of life. Where patients exhibited all of these variables, the model had a 93.7% probability of being correct. The model was further validated when using another sample of 61 newborns; similar figures were obtained. CONCLUSIONS: At the end of the first week of life, the predictive model developed from our population was capable of identifying newborn infants at increased risk of developing BPD with a high degree of sensitivity.
Bronchopulmonary dysplasia; predictive model; chronic neonatal lung disease; mechanical ventilation; prematurity
