Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables.

Abstract

Prematurity is the strongest predictor of bronchopulmonary dysplasia (BPD). Most previous studies investigated additional risk factors by conventional statistics, while the few studies applying artificial intelligence, and specifically machine learning (ML), for this purpose were mainly targeted to the predictive ability of specific interventions. This study aimed to apply ML to identify, among routinely collected data, variables predictive of BPD, and to compare these variables with those identified through conventional statistics. Very preterm infants were recruited; antenatal, perinatal, and postnatal clinical data were collected. A BPD prediction model was built using conventional statistics, and nine supervised ML algorithms were applied for the same purpose: the results of the best-performing model were described and compared with those of conventional statistics. Both conventional statistics and ML identified the degree of immaturity (low gestational age and/or birth weight), need for mechanical ventilation, and absent or reversed end diastolic flow (AREDF) in the umbilical arteries as risk factors for BPD. Each of the two approaches also identified additional potentially predictive clinical variables. ML algorithms might be useful to integrate conventional statistics in identifying novel risk factors, in addition to prematurity, for the development of BPD in very preterm infants. Specifically, the identification of AREDF status as an independent risk factor for BPD by both conventional statistics and ML highlights the opportunity to include detailed antenatal information in clinical predictive models for neonatal diseases.