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Predicting healthcare outcomes in prematurely born infants using cluster analysis
Victoria MacBean1, Alan Lunt2,3, Simon B Drysdale4
1Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
Insights
Cluster analysis identified three groups of prematurely born infants with distinct respiratory outcomes. This approach aids in predicting infant respiratory morbidity after neonatal care.
Area of Science:
- Neonatal Medicine
- Pediatric Pulmonology
- Data Science in Healthcare
Background:
- Premature infants face significant respiratory risks post-neonatal care.
- Predicting these respiratory outcomes remains a clinical challenge.
Purpose of the Study:
- To test if cluster analysis can identify distinct groups of prematurely born infants based on respiratory outcomes.
- To explore the utility of baseline clinical data for outcome prediction.
Main Methods:
- Hierarchical agglomerative clustering was applied to baseline characteristics of 168 prematurely born infants.
- Gestational age and duration of mechanical ventilation were key classification factors.
- Viral lower respiratory tract infections (LRTIs) were tracked for one year in 151 infants.
Main Results:
- Three infant clusters emerged based on birth weight and mechanical ventilation duration.
- Cluster 1 (MV ≤5 days) showed low LRTI rates.
- Clusters 2 & 3 (MV ≥6 days) had significantly higher LRTI rates, with specific viral pathogen associations (RSV in Cluster 2, Rhinovirus in Cluster 3).
Conclusions:
- Readily available clinical data can classify prematurely born infants into distinct risk groups for respiratory morbidity.
- This clustering method offers a novel approach to predicting infant respiratory health trajectories.
Aims:
Prematurely born infants are at high risk of respiratory morbidity following neonatal unit discharge, though prediction of outcomes is challenging. We have tested the hypothesis that cluster analysis would identify discrete groups of prematurely born infants with differing respiratory outcomes during infancy.
Methods:
A total of 168 infants (median (IQR) gestational age 33 (31-34) weeks) were recruited in the neonatal period from consecutive births in a tertiary neonatal unit. The baseline characteristics of the infants were used to classify them into hierarchical agglomerative clusters. Rates of viral lower respiratory tract infections (LRTIs) were recorded for 151 infants in the first year after birth.
Results:
Infants could be classified according to birth weight and duration of neonatal invasive mechanical ventilation (MV) into three clusters. Cluster one (MV ≤5 days) had few LRTIs. Clusters two and three (both MV ≥6 days, but BW ≥or <882 g respectively), had significantly higher LRTI rates. Cluster two had a higher proportion of infants experiencing respiratory syncytial virus LRTIs (P = 0.01) and cluster three a higher proportion of rhinovirus LRTIs (P < 0.001) CONCLUSIONS: Readily available clinical data allowed classification of prematurely born infants into one of three distinct groups with differing subsequent respiratory morbidity in infancy.
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