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Using cluster analysis to describe phenotypical heterogeneity in extremely preterm infants: a retrospective
Theodore Dassios1,2, Emma E Williams3, Christopher Harris3,2
1Department of Women and Children's Health, School of Life Sciences, Faculty of Life Science and Medicine, King's College London, London, UK theodore.dassios@kcl.ac.uk.
Objective:
To use cluster analysis to identify discrete phenotypic groups of extremely preterm infants.
Design:
Secondary analysis of a retrospective whole population study.
Setting:
All neonatal units in England between 2014 and 2019.
Participants:
Infants live-born at less than 28 weeks of gestation and admitted to a neonatal unit.
Interventions:
K-means cluster analysis was performed with the gestational age, Apgar score at 5 min and duration of mechanical ventilation as input variables.
Primary And Secondary Outcome Measures:
Bronchopulmonary dysplasia, discharge on home oxygen, intraventricular haemorrhage, death before discharge from neonatal care.
Results:
Ten thousand one hundred and ninety-seven infants (53% male) were classified into four clusters: Cluster 1 contained infants with intermediate gestation and duration of ventilation and had an intermediate mortality and incidence of bronchopulmonary dysplasia. Cluster 2 contained infants with the highest gestation, a shorter duration of ventilation and the lowest mortality. Cluster 3 contained infants with the lowest Apgar score and highest mortality and incidence of intraventricular haemorrhage. Cluster 4 contained infants with the lowest gestation, longest duration of ventilation and highest incidence of bronchopulmonary dysplasia.
Conclusion:
Clinical parameters can classify extremely preterm infants into discrete phenotypic groups with differing subsequent neonatal outcomes.
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