Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates
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 pred...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-01141-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849326013406773248 |
|---|---|
| author | Elif Keles Syed Yaseen Ali Pia Wintermark Pieter Annaert Floris Groenendaal Suzan Şahin Mehmet Yekta Öncel Didem Armangil Esin Koc Malcolm R. Battin Alistair J. Gunn Adam Frymoyer Valerie Chock Djalila Mekahli John van den Anker Anne Smits Karel Allegaert Ulas Bagci |
| author_facet | Elif Keles Syed Yaseen Ali Pia Wintermark Pieter Annaert Floris Groenendaal Suzan Şahin Mehmet Yekta Öncel Didem Armangil Esin Koc Malcolm R. Battin Alistair J. Gunn Adam Frymoyer Valerie Chock Djalila Mekahli John van den Anker Anne Smits Karel Allegaert Ulas Bagci |
| author_sort | Elif Keles |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ed42b05b191e43288734fb1bb2db2e1f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ed42b05b191e43288734fb1bb2db2e1f2025-08-20T03:48:15ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-01141-9Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonatesElif Keles0Syed Yaseen Ali1Pia Wintermark2Pieter Annaert3Floris Groenendaal4Suzan Şahin5Mehmet Yekta Öncel6Didem Armangil7Esin Koc8Malcolm R. Battin9Alistair J. Gunn10Adam Frymoyer11Valerie Chock12Djalila Mekahli13John van den Anker14Anne Smits15Karel Allegaert16Ulas Bagci17Department of Radiology, Feinberg School of Medicine, Northwestern UniversityDepartment of Radiology, Feinberg School of Medicine, Northwestern UniversityDivision of Newborn Medicine, Department of Pediatrics, Montreal Children’s Hospital, Research Institute of the McGill University Health Centre, McGill UniversityDepartment of Pharmaceutical and Pharmacological Sciences, KU LeuvenDepartment of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, and Utrecht UniversityDepartment of Neonatology, Faculty of Medicine, Izmir Demokrasi UniversityDepartment of Neonatology, Faculty of Medicine, İzmir Katip Çelebi UniversityNeonatal Intensive Care Unit, Koru HospitalDepartment of Neonatology, Faculty of Medicine, Gazi UniversityNewborn Service, Auckland City Hospital, Health New ZealandDepartment of Physiology, University of AucklandNeonatal and Developmental Medicine, Stanford University School of MedicineNeonatal and Developmental Medicine, Stanford University School of MedicineDepartment of Development and Regeneration, KU LeuvenDivision of Clinical Pharmacology, Children’s National HospitalDepartment of Development and Regeneration, KU LeuvenDepartment of Pharmaceutical and Pharmacological Sciences, KU LeuvenDepartment of Radiology, Feinberg School of Medicine, Northwestern UniversityAbstract 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.https://doi.org/10.1038/s41598-025-01141-9 |
| spellingShingle | Elif Keles Syed Yaseen Ali Pia Wintermark Pieter Annaert Floris Groenendaal Suzan Şahin Mehmet Yekta Öncel Didem Armangil Esin Koc Malcolm R. Battin Alistair J. Gunn Adam Frymoyer Valerie Chock Djalila Mekahli John van den Anker Anne Smits Karel Allegaert Ulas Bagci Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates Scientific Reports |
| title | Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates |
| title_full | Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates |
| title_fullStr | Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates |
| title_full_unstemmed | Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates |
| title_short | Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates |
| title_sort | machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates |
| url | https://doi.org/10.1038/s41598-025-01141-9 |
| work_keys_str_mv | AT elifkeles machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT syedyaseenali machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT piawintermark machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT pieterannaert machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT florisgroenendaal machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT suzansahin machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT mehmetyektaoncel machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT didemarmangil machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT esinkoc machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT malcolmrbattin machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT alistairjgunn machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT adamfrymoyer machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT valeriechock machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT djalilamekahli machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT johnvandenanker machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT annesmits machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT karelallegaert machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates AT ulasbagci machinelearningbasedclinicaldecisiontooltopredictacutekidneyinjuryandsurvivalintherapeutichypothermiatreatedneonates |