A Simple and Interpretable Severe Intraventricular Hemorrhage Prediction Model for Extremely Low Birth Weight Infants Using Machine Learning
Abstract Severe intraventricular hemorrhage (sIVH) is a catastrophic event with serious neurocognitive impairment in preterm infants. Because sIVH is a complex multifactorial disease, determining which patients require special attention to prevent sIVH is challenging. This study aimed to evaluate an easy interpretable decision-tree model to identify extremely preterm infants with a higher risk of severe intraventricular hemorrhage. All infants admitted to a single-center tertiary intensive care unit in São Paulo, Brazil, from 2012 to 2017, with a birth weight less than 1000 grams and at least one cranial ultrasound after three days of life were included. The association of risk factors with sIVH was assessed using logistic regression. Univariate analysis, stepwise logistic regression, correlation matrix, Boruta, and XGBoost were used to select features. In this single-center, retrospective cohort of 190 extremely low birth weight infants, the mean gestational age was 27.5 (2.2) weeks and the mean birth weight was 748 (161) grams. A total of forty-two newborns (22.1%) developed severe intraventricular hemorrhage. Machine learning tools identified three features (pH, base excess, and gestational age) that predict severe intraventricular hemorrhage with an AUC of 0.857. Low pH levels appear to be a key factor in identifying the great majority of cases that require additional attention. Conclusions: We suggest a simple and interpretable decision-tree model to promptly identify extremely low birth weight infants at the highest risk of severe intraventricular hemorrhage.
