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Bronchopulmonary dysplasia predicted at birth by artificial intelligence

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Verder, H, Heiring, C, Ramanathan, R, Scoutaris, N, Verder, P, Jessen, TE, Höskuldsson, A, Bender, L, Dahl, M, Eschen, C, Fenger-Grøn, J, Reinholdt, J, Smedegaard, H & Schousboe, P 2021, 'Bronchopulmonary dysplasia predicted at birth by artificial intelligence', Acta paediatrica, bind 110, nr. 2, s. 503-509. https://doi.org/10.1111/apa.15438

APA

Verder, H., Heiring, C., Ramanathan, R., Scoutaris, N., Verder, P., Jessen, T. E., Höskuldsson, A., Bender, L., Dahl, M., Eschen, C., Fenger-Grøn, J., Reinholdt, J., Smedegaard, H., & Schousboe, P. (2021). Bronchopulmonary dysplasia predicted at birth by artificial intelligence. Acta paediatrica, 110(2), 503-509. https://doi.org/10.1111/apa.15438

CBE

Verder H, Heiring C, Ramanathan R, Scoutaris N, Verder P, Jessen TE, Höskuldsson A, Bender L, Dahl M, Eschen C, Fenger-Grøn J, Reinholdt J, Smedegaard H, Schousboe P. 2021. Bronchopulmonary dysplasia predicted at birth by artificial intelligence. Acta paediatrica. 110(2):503-509. https://doi.org/10.1111/apa.15438

MLA

Vancouver

Verder H, Heiring C, Ramanathan R, Scoutaris N, Verder P, Jessen TE o.a. Bronchopulmonary dysplasia predicted at birth by artificial intelligence. Acta paediatrica. 2021 feb;110(2):503-509. https://doi.org/10.1111/apa.15438

Author

Verder, Henrik ; Heiring, Christian ; Ramanathan, Rangasamy ; Scoutaris, Nikolaos ; Verder, Povl ; Jessen, Torben E ; Höskuldsson, Agnar ; Bender, Lars ; Dahl, Marianne ; Eschen, Christian ; Fenger-Grøn, Jesper ; Reinholdt, Jes ; Smedegaard, Heidi ; Schousboe, Peter. / Bronchopulmonary dysplasia predicted at birth by artificial intelligence. I: Acta paediatrica. 2021 ; Bind 110, Nr. 2. s. 503-509.

Bibtex

@article{e7729d982f5e4204ab8d0ec20709ce75,
title = "Bronchopulmonary dysplasia predicted at birth by artificial intelligence",
abstract = "AIM: To develop a fast bedside test for prediction and early targeted intervention of bronchopulmonary dysplasia (BPD) to improve the outcome.METHODS: In a multicentre study of preterm infants with gestational age 24-31 weeks, clinical data present at birth were combined with spectral data of gastric aspirate samples taken at birth and analysed using artificial intelligence. The study was designed to develop an algorithm to predict development of BPD. The BPD definition used was the consensus definition of the US National Institutes of Health: Requirement of supplemental oxygen for at least 28 days with subsequent assessment at 36 weeks postmenstrual age.RESULTS: Twenty-six (43%) of the 61 included infants developed BPD. Spectral data analysis of the gastric aspirates identified the most important wave numbers for classification and surfactant treatment, and birth weight and gestational age were the most important predictive clinical data. By combining these data, the resulting algorithm for early diagnosis of BPD had a sensitivity of 88% and a specificity of 91%.CONCLUSION: A point-of-care test to predict subsequent development of BPD at birth has been developed using a new software algorithm allowing early targeted intervention of BPD which could improve the outcome.",
keywords = "Bronchopulmonary dysplasia, chorioamnionitis, respiratory distress syndrome, spectroscopy, surfactant, bronchopulmonary dysplasia",
author = "Henrik Verder and Christian Heiring and Rangasamy Ramanathan and Nikolaos Scoutaris and Povl Verder and Jessen, {Torben E} and Agnar H{\"o}skuldsson and Lars Bender and Marianne Dahl and Christian Eschen and Jesper Fenger-Gr{\o}n and Jes Reinholdt and Heidi Smedegaard and Peter Schousboe",
note = "{\textcopyright} 2020 The Authors. Acta Paediatrica published by John Wiley & Sons Ltd on behalf of Foundation Acta Paediatrica.",
year = "2021",
month = feb,
doi = "10.1111/apa.15438",
language = "English",
volume = "110",
pages = "503--509",
journal = "Acta paediatrica",
issn = "1651-2227",
publisher = "Wiley-Blackwell Publishing Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Bronchopulmonary dysplasia predicted at birth by artificial intelligence

AU - Verder, Henrik

AU - Heiring, Christian

AU - Ramanathan, Rangasamy

AU - Scoutaris, Nikolaos

AU - Verder, Povl

AU - Jessen, Torben E

AU - Höskuldsson, Agnar

AU - Bender, Lars

AU - Dahl, Marianne

AU - Eschen, Christian

AU - Fenger-Grøn, Jesper

AU - Reinholdt, Jes

AU - Smedegaard, Heidi

AU - Schousboe, Peter

N1 - © 2020 The Authors. Acta Paediatrica published by John Wiley & Sons Ltd on behalf of Foundation Acta Paediatrica.

PY - 2021/2

Y1 - 2021/2

N2 - AIM: To develop a fast bedside test for prediction and early targeted intervention of bronchopulmonary dysplasia (BPD) to improve the outcome.METHODS: In a multicentre study of preterm infants with gestational age 24-31 weeks, clinical data present at birth were combined with spectral data of gastric aspirate samples taken at birth and analysed using artificial intelligence. The study was designed to develop an algorithm to predict development of BPD. The BPD definition used was the consensus definition of the US National Institutes of Health: Requirement of supplemental oxygen for at least 28 days with subsequent assessment at 36 weeks postmenstrual age.RESULTS: Twenty-six (43%) of the 61 included infants developed BPD. Spectral data analysis of the gastric aspirates identified the most important wave numbers for classification and surfactant treatment, and birth weight and gestational age were the most important predictive clinical data. By combining these data, the resulting algorithm for early diagnosis of BPD had a sensitivity of 88% and a specificity of 91%.CONCLUSION: A point-of-care test to predict subsequent development of BPD at birth has been developed using a new software algorithm allowing early targeted intervention of BPD which could improve the outcome.

AB - AIM: To develop a fast bedside test for prediction and early targeted intervention of bronchopulmonary dysplasia (BPD) to improve the outcome.METHODS: In a multicentre study of preterm infants with gestational age 24-31 weeks, clinical data present at birth were combined with spectral data of gastric aspirate samples taken at birth and analysed using artificial intelligence. The study was designed to develop an algorithm to predict development of BPD. The BPD definition used was the consensus definition of the US National Institutes of Health: Requirement of supplemental oxygen for at least 28 days with subsequent assessment at 36 weeks postmenstrual age.RESULTS: Twenty-six (43%) of the 61 included infants developed BPD. Spectral data analysis of the gastric aspirates identified the most important wave numbers for classification and surfactant treatment, and birth weight and gestational age were the most important predictive clinical data. By combining these data, the resulting algorithm for early diagnosis of BPD had a sensitivity of 88% and a specificity of 91%.CONCLUSION: A point-of-care test to predict subsequent development of BPD at birth has been developed using a new software algorithm allowing early targeted intervention of BPD which could improve the outcome.

KW - Bronchopulmonary dysplasia

KW - chorioamnionitis

KW - respiratory distress syndrome

KW - spectroscopy

KW - surfactant

KW - bronchopulmonary dysplasia

UR - http://www.scopus.com/inward/record.url?scp=85088588965&partnerID=8YFLogxK

U2 - 10.1111/apa.15438

DO - 10.1111/apa.15438

M3 - Journal article

C2 - 32569404

VL - 110

SP - 503

EP - 509

JO - Acta paediatrica

JF - Acta paediatrica

SN - 1651-2227

IS - 2

ER -

ID: 60131547