Early Maturation of Heart Rate Variability in Very Preterm Infants Depends on Neonatal Factors and Is Associated With...

Area of Science:

• Neonatal physiology and autonomic nervous system development.
• Application of machine learning in clinical research.
• Neurodevelopmental outcomes in preterm infants.

Background:

• The central autonomic network's maturation is crucial for preterm infant development.
• Heart rate variability (HRV) analysis offers insights into autonomic function.
• Previous machine learning models can estimate functional maturation age (FMA).

Purpose of the Study:

• To investigate deviations in FMA from post-menstrual age (PMA) using HRV in preterm infants.
• To determine associations between these FMA deviations (ΔHRV) and clinical factors.
• To explore the relationship between ΔHRV and neurodevelopmental outcomes at 2 years of age.

Main Methods:

• A multicenter prospective cohort study of 132 preterm infants (<30 weeks gestational age).
• Calculation of ΔHRV as the difference between PMA and FMA.
• Statistical analysis correlating ΔHRV with clinical complications and neurodevelopmental assessments (Ages and Stages Questionnaire - ASQ).

Main Results:

• Greater ΔHRV (weeks delay) was observed with lower gestational age (GA) and in infants with bronchopulmonary dysplasia or patent ductus arteriosus.
• ΔHRV was significantly associated with altered social skills at 2 years of age.
• No significant association was found between ΔHRV and the total ASQ score.

Conclusions:

• Early, non-invasive estimation of ΔHRV is influenced by GA and perinatal/postnatal complications.
• ΔHRV assessment shows potential for evaluating neurodevelopmental risk in preterm infants.
• This HRV-based metric provides a novel approach to monitoring preterm infant development.