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 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.

Using cluster analysis to describe phenotypical heterogeneity in extremely preterm infants: a retrospective whole-population study

“…They also identified a relationship between nutritional practice and outcomes such as BPD, with provision of human milk being a protective factor against developing BPD. 30 Our classification could be clinically useful in further understanding the pathophysiology of extremely preterm morbidity and mortality and possibly in targeting selected subgroups of infants for specific interventions. For example, for cluster 4, such interventions might include administration of postnatal corticosteroids to assist earlier weaning from invasive respiratory support, 31 more gentle resuscitation methods, avoidance of mechanical ventilation by newer non-invasive techniques 32 and use of less invasive surfactant administration, which has been shown to reduce the incidence of BPD.…”

“…They also identified a relationship between nutritional practice and outcomes such as BPD, with provision of human milk being a protective factor against developing BPD. 30 …”

“…Greenbury et al performed clustering on daily nutritional intakes in premature infants of less than 32 weeks of gestation and reported different nutritional clusters, which were heterogeneous in size, with some showing common interpretable clinical practices. They also identified a relationship between nutritional practice and outcomes such as BPD, with provision of human milk being a protective factor against developing BPD 30…”

Application and potential of artificial intelligence in neonatal medicine

“…Deep learning approaches have been deployed in large clinical datasets to identify hidden data patterns. An example of such application is exploring the variation of neonatal nutritional practices and their association with clinical outcomes [6]. This could identify "optimal" nutritional practices as well as improve the understanding of the underlying pathophysiology and impact of nutritional practices on neonatal outcomes.…”

Incorporating parent, former patient and clinician perspectives in the design of a national UK double-cluster, randomised controlled trial addressing uncertainties in preterm nutrition

“…Currently in the UK less than 20% of very preterm babies receive any pasteurised donor milk and less than 40% receive any fortifier. 3 The uncertainty around optimal practice creates risks for patients, anxiety for parents, and confusion among staff. COLLABORATE offers a pragmatic response to these uncertainties.…”