Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage
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 interven...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | https://hdl.handle.net/1721.1/157315 |
| _version_ | 1824458430538055680 |
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| author | Vignolle, Gabriel A. Bauerstätter, Priska Schönthaler, Silvia Nöhammer, Christa Olischar, Monika Berger, Angelika Kasprian, Gregor Langs, Georg Vierlinger, Klemens Goeral, Katharina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Vignolle, Gabriel A. Bauerstätter, Priska Schönthaler, Silvia Nöhammer, Christa Olischar, Monika Berger, Angelika Kasprian, Gregor Langs, Georg Vierlinger, Klemens Goeral, Katharina |
| author_sort | Vignolle, Gabriel A. |
| collection | MIT |
| description | 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. |
| first_indexed | 2025-02-19T04:25:46Z |
| format | Article |
| id | mit-1721.1/157315 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2025-02-19T04:25:46Z |
| publishDate | 2024 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1573152025-01-01T04:29:04Z Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage Vignolle, Gabriel A. Bauerstätter, Priska Schönthaler, Silvia Nöhammer, Christa Olischar, Monika Berger, Angelika Kasprian, Gregor Langs, Georg Vierlinger, Klemens Goeral, Katharina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. 2024-10-15T19:22:20Z 2024-10-15T19:22:20Z 2024-09-25 2024-10-15T12:52:58Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/157315 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. PUBLISHER_CC http://dx.doi.org/10.3390/ijms251910304 International Journal of Molecular Sciences Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Vignolle, Gabriel A. Bauerstätter, Priska Schönthaler, Silvia Nöhammer, Christa Olischar, Monika Berger, Angelika Kasprian, Gregor Langs, Georg Vierlinger, Klemens Goeral, Katharina Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage |
| title | Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage |
| title_full | Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage |
| title_fullStr | Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage |
| title_full_unstemmed | Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage |
| title_short | Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage |
| title_sort | predicting outcomes of preterm neonates post intraventricular hemorrhage |
| url | https://hdl.handle.net/1721.1/157315 |
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