Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage
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ijms-25-10304.pdf
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1.69 MB
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Checksum (MD5)
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Author(s)
Vignolle, Gabriel A.
Bauerstätter, Priska
Schönthaler, Silvia
Nöhammer, Christa
Olischar, Monika
Berger, Angelika
Kasprian, Gregor
Langs, Georg
Vierlinger, Klemens
Goeral, Katharina
Date Issued
September 25, 2024
Journal
International Journal of Molecular Sciences
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Vignolle, G.A.; Bauerstätter, P.; Schönthaler, S.; Nöhammer, C.; Olischar, M.; Berger, A.; Kasprian, G.; Langs, G.; Vierlinger, K.; Goeral, K. Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage. Int. J. Mol. Sci. 2024, 25, 10304.
Version
Final published version
Abstract
Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques—including statistical, regularization, deep learning, decision trees, and Bayesian methods—were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.3390/ijms251910304
