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Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage.

Gabriel A Vignolle1, Priska Bauerstätter1, Silvia Schönthaler1

  • 1Center for Health & Bioresources, Competence Unit Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria.

International Journal of Molecular Sciences
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

This study uses explainable machine learning and proteomics to predict posthemorrhagic ventricular dilatation (PHVD) in preterm neonates with intraventricular hemorrhage (IVH). It identified 41 protein markers and gestational age as predictors, aiding early detection and parental counseling.

Keywords:
biomarkerintensive careintraventricular hemorrhagemachine learningneonateposthemorrhagic hydrocephaluspredictionprematurityproteomicssurvival

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Area of Science:

  • Neonatal Medicine
  • Biomarker Discovery
  • Machine Learning in Healthcare

Background:

  • Intraventricular hemorrhage (IVH) in preterm neonates often leads to posthemorrhagic ventricular dilatation (PHVD), a serious complication impacting survival and long-term neurological outcomes.
  • Early PHVD detection is critical for timely intervention and informed parental counseling.
  • Current prediction methods for PHVD in preterm infants are limited, necessitating novel approaches.

Purpose of the Study:

  • To investigate the efficacy of explainable machine learning (ML) models utilizing targeted liquid biopsy proteomics data for predicting PHVD development and survival in preterm neonates with IVH.
  • To identify novel and known protein biomarkers associated with PHVD and survival.
  • To enhance clinical decision-making and parental counseling through reliable predictive tools.

Main Methods:

  • Prospective longitudinal cohort study analyzing 1109 liquid biopsy samples from 99 preterm neonates with IVH over 13 years.
  • Application of diverse explainable ML techniques (statistical, regularization, deep learning, decision trees, Bayesian) to predict PHVD and survival.
  • Targeted proteomic analysis of serum and urine samples using proximity extension assay to detect low-concentration proteins.

Main Results:

  • Identified 41 significant independent protein markers and gestational age at birth as predictors of PHVD development and survival, surpassing rigorous performance thresholds (AUC-ROC ≥0.7, sensitivity ≥0.6, selectivity ≥0.6).
  • Discovered both established biomarkers, such as neurofilament light chain (NEFL), and novel protein markers.
  • Developed over 1600 ML models, demonstrating the potential of proteomics and ML in neonatal outcome prediction.

Conclusions:

  • Targeted proteomics combined with explainable ML offers a promising avenue for early prediction of PHVD and survival in preterm neonates with IVH.
  • The identified protein markers and ML models can potentially improve clinical decision-making and parental support.
  • Further validation studies are necessary to translate these findings into routine clinical practice.