Bronchopulmonary dysplasia predicted at birth by artificial intelligence.
Acta Paediatr
; 110(2): 503-509, 2021 02.
Article
em En
| MEDLINE
| ID: mdl-32569404
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Surfactantes Pulmonares
/
Displasia Broncopulmonar
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Female
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Humans
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Infant
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Newborn
/
Pregnancy
Idioma:
En
Revista:
Acta Paediatr
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Dinamarca