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

Abstract

ObjectiveTo use cluster analysis to identify discrete phenotypic groups of extremely preterm infants.DesignSecondary analysis of a retrospective whole population study.SettingAll neonatal units in England between 2014 and 2019.ParticipantsInfants live-born at less than 28 weeks of gestation and admitted to a neonatal unit.InterventionsK-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 measuresBronchopulmonary dysplasia, discharge on home oxygen, intraventricular haemorrhage, death before discharge from neonatal care.ResultsTen 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.ConclusionClinical parameters can classify extremely preterm infants into discrete phenotypic groups with differing subsequent neonatal outcomes.