Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning

Abstract We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn care, utilising the UK National Neonatal R...

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Autores principales:	Sam F. Greenbury, Kayleigh Ougham, Jinyi Wu, Cheryl Battersby, Chris Gale, Neena Modi, Elsa D. Angelini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/57c4b1daaf4645168fb16db8a186d777
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spelling	oai:doaj.org-article:57c4b1daaf4645168fb16db8a186d7772021-12-02T14:24:56ZIdentification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning10.1038/s41598-021-85878-z2045-2322https://doaj.org/article/57c4b1daaf4645168fb16db8a186d7772021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85878-zhttps://doaj.org/toc/2045-2322Abstract We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn care, utilising the UK National Neonatal Research Database (NNRD). We performed clustering on time-series data of daily nutritional intakes for very preterm infants born at a gestational age less than 32 weeks (n = 45,679) over a six-year period. This revealed 46 nutritional clusters heterogeneous in size, showing common interpretable clinical practices alongside rarer approaches. Nutritional clusters with similar admission profiles revealed associations between nutritional practice, geographical location and outcomes. We show how nutritional subgroups may be regarded as distinct interventions and tested for associations with measurable outcomes. We illustrate the potential for identifying relationships between nutritional practice and outcomes with two examples, discharge weight and bronchopulmonary dysplasia (BPD). We identify the well-known effect of formula milk on greater discharge weight as well as support for the plausible, but insufficiently evidenced view that human milk is protective against BPD. Our framework highlights the potential of agnostic machine learning approaches to deliver clinical practice insights and generate hypotheses using routine data.Sam F. GreenburyKayleigh OughamJinyi WuCheryl BattersbyChris GaleNeena ModiElsa D. AngeliniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sam F. Greenbury Kayleigh Ougham Jinyi Wu Cheryl Battersby Chris Gale Neena Modi Elsa D. Angelini Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
description	Abstract We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn care, utilising the UK National Neonatal Research Database (NNRD). We performed clustering on time-series data of daily nutritional intakes for very preterm infants born at a gestational age less than 32 weeks (n = 45,679) over a six-year period. This revealed 46 nutritional clusters heterogeneous in size, showing common interpretable clinical practices alongside rarer approaches. Nutritional clusters with similar admission profiles revealed associations between nutritional practice, geographical location and outcomes. We show how nutritional subgroups may be regarded as distinct interventions and tested for associations with measurable outcomes. We illustrate the potential for identifying relationships between nutritional practice and outcomes with two examples, discharge weight and bronchopulmonary dysplasia (BPD). We identify the well-known effect of formula milk on greater discharge weight as well as support for the plausible, but insufficiently evidenced view that human milk is protective against BPD. Our framework highlights the potential of agnostic machine learning approaches to deliver clinical practice insights and generate hypotheses using routine data.
format	article
author	Sam F. Greenbury Kayleigh Ougham Jinyi Wu Cheryl Battersby Chris Gale Neena Modi Elsa D. Angelini
author_facet	Sam F. Greenbury Kayleigh Ougham Jinyi Wu Cheryl Battersby Chris Gale Neena Modi Elsa D. Angelini
author_sort	Sam F. Greenbury
title	Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
title_short	Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
title_full	Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
title_fullStr	Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
title_full_unstemmed	Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
title_sort	identification of variation in nutritional practice in neonatal units in england and association with clinical outcomes using agnostic machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/57c4b1daaf4645168fb16db8a186d777
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Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning

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