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Predicting Future Respiratory Hospitalizations in Extremely Premature Neonates Using Transcriptomic Data and Machine
Bryan G McOmber1, Lois Randolph1, Patrick Lang1
1Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Insights
Gene expression profiles in extremely preterm neonates can predict future respiratory hospitalizations. This finding may help identify high-risk infants for early intervention, improving long-term respiratory health outcomes.
Area of Science:
- Neonatal Medicine
- Genomics
- Computational Biology
Background:
- Extremely premature neonates face high risks of respiratory complications and hospitalizations.
- Early identification of high-risk infants is crucial for targeted preventive strategies.
- Transcriptomic data may enhance the prediction of respiratory outcomes beyond clinical factors.
Purpose of the Study:
- To investigate the predictive capability of early-life gene expression for respiratory hospitalizations in extremely preterm neonates within their first four years.
- To determine if transcriptomic profiles can identify infants at higher risk for respiratory morbidity.
Main Methods:
- Retrospective cohort study of 58 neonates born before 32 weeks' gestational age.
- Analysis of peripheral blood transcriptomic data collected on days 5, 14, and 28 of life.
- Development of random forest models to predict respiratory readmissions, with performance assessed by AUC, sensitivity, and specificity.
Main Results:
- Machine learning models using transcriptomic data achieved strong predictive performance (AUC = 0.90).
- Differential expression analysis identified 31 genes and 8 biological pathways associated with respiratory readmissions.
- Despite a small sample size, results indicate significant predictive power.
Conclusions:
- Early-life transcriptomic data and machine learning accurately predict respiratory rehospitalizations in extremely preterm infants.
- Identified gene signatures provide insights into biological mechanisms underlying chronic respiratory morbidity.
- Further validation in larger cohorts is necessary for clinical application.
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.
