Predicting Future Respiratory Hospitalizations in Extremely Premature Neonates Using Transcriptomic Data and Machine Learning

Abstract

Background: Extremely premature neonates are at increased risk for respiratory complications, often resulting in recurrent hospitalizations during early childhood. Early identification of preterm infants at highest risk of respiratory hospitalizations could enable targeted preventive interventions. While clinical and demographic factors offer some prognostic value, integrating transcriptomic data may improve predictive accuracy.

Objective: To determine whether early-life gene expression profiles can predict respiratory-related hospitalizations within the first four years of life in extremely preterm neonates.

Methods: We conducted a retrospective cohort study of 58 neonates born at <32 weeks' gestational age, using publicly available transcriptomic data from peripheral blood samples collected on days 5, 14, and 28 of life. Random forest models were trained to predict unplanned respiratory readmissions. Model performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC).

Results: All three models, built using transcriptomic data from days 5, 14, and 28, demonstrated strong predictive performance (AUC = 0.90), though confidence intervals were wide due to small sample size. We identified 31 genes and eight biological pathways that were differentially expressed between preterm neonates with and without subsequent respiratory readmissions.

Conclusions: Transcriptomic data from the neonatal period, combined with machine learning, accurately predicted respiratory-related rehospitalizations in extremely preterm neonates. The identified gene signatures offer insight into early biological disruptions that may predispose preterm neonates to chronic respiratory morbidity. Validation in larger, diverse cohorts is needed to support clinical translation.

Keywords: bioinformatics; bronchopulmonary dysplasia; machine learning; preterm infants; respiratory morbidity; transcriptomics.

Figure 1

Receiver operating characteristic (ROC) curves for random forest models predicting respiratory-related hospitalizations in very low gestational age preterm infants using transcriptomic data collected on days 5 (red), 14 (green), and 28 (blue) of life. Each panel shows sensitivity versus 1-specificity for the corresponding time point. Model performance metrics are annotated within each panel, including area under the curve (AUC) with 95% confidence intervals, sensitivity, and specificity. N = 58: Outcome is defined as at least one respiratory-related hospitalization within the first four years of life.

Figure 2

Longitudinal Expression Trajectories of 31 Predictive Genes Stratified by Respiratory-Related Hospitalization Status in Very Low Gestational Age Preterm Infants.

Figure 3

Longitudinal Expression Profiles of Selected Immune and Developmental Pathways Stratified by Respiratory-Related Hospitalization Status in Very Low Gestational Age Preterm Infants.