BMJ Open. 2022 Feb 28;12(2):e056567. doi: 10.1136/bmjopen-2021-056567.
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.