Predicting the effectiveness of drugs used for treating cardiovascular conditions in newborn infants.

Abstract

Cardiovascular support (CVS) treatment failure (TF) is associated with a poor prognosis in preterm infants. Medical charts of infants with a birth weight <1500 g who received either dopamine (Dp) or dobutamine (Db), were reviewed. Treatment response (TR) occurred if blood pressure increased >3rd centile for gestational age or superior vena cava flow was maintained >55 ml/kg/min, with decreased lactate or less negative base excess, without additional CVS. A predictive model of Dp and Db on TR was designed and the impact of TR on survival was analyzed. Sixty-six infants (median gestational age 27.3 weeks, median birth weight 864 g) received Dp (n = 44) or Db (n = 22). TR occurred in 59% of the cases treated with Dp and 31% with Db, p = 0.04. Machine learning identified a model that correctly labeled Db response in 90% of the cases and Dp response in 61.4%. Sixteen infants died (9% of the TR group, 39% of the TF group; p = 0.004). Brain or gut morbidity-free survival was observed in 52% vs 30% in the TR and TF groups, respectively (p = 0.08). New predictive models can anticipate Db but not Dp effectiveness in preterm infants. These algorithms may help the clinicians in the decision-making process. Failure of cardiovascular support treatment increases the risk of mortality in very low birth weight infants. A predictive model built with machine learning techniques can help anticipate treatment response to dobutamine with high accuracy. Predictive models based on artificial intelligence may guide the clinicians in the decision-making process.