RESUMO
AIM: To investigate the relation between autonomic regulation, measured using heart rate variability (HRV), body weight and degree of prematurity in infants. Further to assess utility to include body weight in a machine learning-based sepsis prediction algorithm. METHODS: Longitudinal cohort study including 378 infants hospitalised in two neonatal intensive care units. Continuous vital sign data collection was performed prospectively from the time of NICU admission to discharge. Clinically relevant events were annotated retrospectively. HRV described using sample entropy of inter-beat intervals and assessed for its correlation with body weight measurements and age. Weight values were then added to a machine learning-based algorithm for neonatal sepsis detection. RESULTS: Sample entropy showed a positive correlation with increasing body weight and postconceptual age. Very low birth weight infants exhibited significantly lower HRV compared to infants with a birth weight >1500 g. This persisted when reaching similar weight and at the same postconceptual age. Adding body weight measures improved the algorithm's ability to predict sepsis in the overall population. CONCLUSION: We revealed a positive correlation of HRV with increasing body weight and maturation in infants. Restricted HRV, proven helpful in detecting acute events such as neonatal sepsis, might reflect prolonged impaired development of autonomic control.
