Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates

Therapeutic hypothermia (TH) significantly reduces mortality and morbidities in neonates with Neonatal Encephalopathy (NE). NE may result in neonatal death and multisystem organ impairment, including acute kidney injury (AKI). Our study aimed to utilize machine learning (ML) methods to predict the outcome of TH-treated NE neonates developing AKI and death during TH. In this retrospective multinational study, 1149 TH-treated NE neonates and 801 controls were included. AKI was classified using KDIGO neonatal criteria based on serum creatinine measurements. The ML model incorporated gestational age, birth weight, postnatal age, and serum creatinine values. The algorithm used all these covariates to predict one of five outcomes: survival with/without AKI, mortality with/without AKI, and hospitalized non-NE controls. The XGBoost model achieved an AUC of 95% and an accuracy of 75.08% in predicting AKI and survival, surpassing other ML classifiers that demonstrated accuracy levels ranging from 54% to 65%. To our knowledge this is the first ML model trained on multicenter, multinational data specifically aimed at predicting neonates' AKI, death, and survival within the first three days. Our ML scoring systems' code and user interface are freely available ( https://github.com/NUBagciLab/Therapeutic-Hypothermia-Outcome-Classification , https://thprediction.streamlit.app/ ). This tool has potential to support neonatologists to personalize therapies, and to optimize pharmacotherapy for renally cleared drugs.

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Publication Details

Journal
Scientific Reports
Published
2025-05-19
DOI
https://doi.org/10.1038/s41598-025-01141-9
Citations
6
Primary Topic
Neonatal and fetal brain pathology
Type
article
Field-Weighted Citation Impact
20.96

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article

Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates

Malcolm Battin, Ulaş Bağcı, Valerie Y. Chock, Esin Koç et al.
6 citations
Scientific Reports
Neonatal and fetal brain pathology
20.96
article

Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates

Malcolm Battin, Ulaş Bağcı, Valerie Y. Chock, Esin Koç, Didem Armangil, Pieter Annaert, Alistair Jan Gunn, John van den Anker, Mehmet Yekta Öncel, Anne Smits, Djalila Mekahli, Pia Wintermark, Suzan Şahin, Elif Keleş, Adam Frymoyer, Karel Allegaert, Floris Groenendaal, S. Ali
article en
6 citations

Abstract

Therapeutic hypothermia (TH) significantly reduces mortality and morbidities in neonates with Neonatal Encephalopathy (NE). NE may result in neonatal death and multisystem organ impairment, including acute kidney injury (AKI). Our study aimed to utilize machine learning (ML) methods to predict the outcome of TH-treated NE neonates developing AKI and death during TH. In this retrospective multinational study, 1149 TH-treated NE neonates and 801 controls were included. AKI was classified using KDIGO neonatal criteria based on serum creatinine measurements. The ML model incorporated gestational age, birth weight, postnatal age, and serum creatinine values. The algorithm used all these covariates to predict one of five outcomes: survival with/without AKI, mortality with/without AKI, and hospitalized non-NE controls. The XGBoost model achieved an AUC of 95% and an accuracy of 75.08% in predicting AKI and survival, surpassing other ML classifiers that demonstrated accuracy levels ranging from 54% to 65%. To our knowledge this is the first ML model trained on multicenter, multinational data specifically aimed at predicting neonates' AKI, death, and survival within the first three days. Our ML scoring systems' code and user interface are freely available ( https://github.com/NUBagciLab/Therapeutic-Hypothermia-Outcome-Classification , https://thprediction.streamlit.app/ ). This tool has potential to support neonatologists to personalize therapies, and to optimize pharmacotherapy for renally cleared drugs.

Scientific ReportsVol. 15(1)
Northwestern University (US), Izmir University (TR), Children's National (US), University of Auckland (NZ), Utrecht University (NL), Izmir Kâtip Çelebi University (TR), Erasmus MC (NL), University Medical Center Utrecht (NL), Memorial Ankara Hospital (TR), Wilhelmina Children's Hospital (NL), Gazi University (TR), Stanford University (US), KU Leuven (BE)
Fonds Wetenschappelijk Onderzoek, Vlaamse regering, National Institutes of Health, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases
Good health and well-being
Openalex Percentile: Top 1%
Neonatal and fetal brain pathology
20.96
DOIOpen Access PDFOpenAlex
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