In American journal of physiology. Lung cellular and molecular physiology
BACKGROUND : Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning BPD.
OBJECTIVE : Develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD.
METHODS : Secondary analysis of whole blood microarray data from 97 very low birth weight neonates day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned into a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared to gestational age or birthweight.
RESULTS : Neonates with BPD (n=62) exhibited a lower median gestational age (26.0 weeks vs. 30.0 weeks p<0.01) and birthweight (800 grams vs 1,280 grams, p<0.01) compared to non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birthweight were 87.8% and 87.2%, respectively. The machine learning models revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were most associated with BPD.
CONCLUSIONS : A derived 5-gene whole blood signature can accurately predict BPD in the first week of life.
Moreira Alvaro, Tovar Miriam, Smith Alisha M, Lee Grace C, Meunier Justin A, Cheema Zoya, Moreira Axel, Winter Caitlyn, Mustafa Shamimunisa B, Seidner Steven R, Findley Tina Oak, Garcia Joe G N, Thébaud Bernard, Kwinta Przemko, Ahuja Sunil K
2022-Dec-06
artificial intelligence, bronchopulmonary dysplasia, prediction, preterm neonate, transcriptome
