In this section
@article{Greenbury:2021:10.1038/s41598-021-85878-z,
author = {Greenbury, SF and Ougham, K and Wu, J and Battersby, C and Gale, C and Modi, N and Angelini, ED},
doi = {10.1038/s41598-021-85878-z},
journal = {Scientific Reports},
title = {Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning},
url = {http://dx.doi.org/10.1038/s41598-021-85878-z},
volume = {11},
year = {2021}
}
TY - JOUR
AB - 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.
AU - Greenbury,SF
AU - Ougham,K
AU - Wu,J
AU - Battersby,C
AU - Gale,C
AU - Modi,N
AU - Angelini,ED
DO - 10.1038/s41598-021-85878-z
PY - 2021///
SN - 2045-2322
TI - Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning
T2 - Scientific Reports
UR - http://dx.doi.org/10.1038/s41598-021-85878-z
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000636270400005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/88476
VL - 11
ER -
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