Applications of Network Physiology in Neonatology

Every year, an estimated 15 million babies are born preterm, that is, before 37 weeks of gestation. This number is rising in all countries and currently represents more than 1 in 10 babies, affecting families all over the world. During the last decades, the survival rate of prematurely born neonates has steadily increased, mainly as a result of medical and technical progress in neonatal intensive care. The very preterm infants, which represent up to 10% of the preterm infants in the EU, remain at risk for adverse outcome and neurodevelopmental disability. These maladaptive outcomes have a severe effect on the children's quality of life and a huge economic impact on society. In order to reduce this burden and improve neonatal care in general, appropriate tools need to be developed to identify the neonates with a higher risk of adverse outcomes. Brain damage in neonates is complex, since brain abnormality is caused by a complex combination of destructive and developmental mechanisms rather than a mere summation of different brain lesions. In addition, brain abnormality has to be studied over time, since some infants normalize over time while others remain abnormal. In general, brain damage often results from maladaptive perfusion of the brain, since both hypo- and hyperperfusion can cause significant neurologic injury. In a properly functioning brain, there are various regulation mechanisms in place to keep cerebral blood flow (CBF) within the accepted range to support brain function. Modeling the status of these CBF regulation mechanisms and the maturation thereof will allow clinicians to identify neonates at risk for brain injury in an early stage, allowing therapeutic intervention. In clinical practice, the neonatal brain can be monitored using a combination of two signal modalities: electroencephalography (EEG) to measure brain activity and near-infrared spectroscopy (NIRS) to measure cerebral tissue oxygenation. Both modalities can be measured continuously at the bedside in a non-invasive way. The diagnostic value of both modalities has been studied heavily over the last years. The automated integration of both modalities, together with numerous systemic variables allows to create a coordinated overview of the major components of CBF regulation. In this thesis, we developed a computational model for this automated analysis, and validated this model in various clinical settings. The first part of this thesis focuses on a univariate analysis. Such an analysis largely corresponds to how medical doctors currently use signals in clinical practice. Two anesthetic approaches were compared during non-cardiac-major surgery: a sedative-agent based versus an anesthetic-based strategy. The anesthetic-based strategy resulted in adequate anesthesia, as indicated by the decrease in heart rate variability, and the shift in sympathovagal balance, in combination with increased cerebral oxygenation and decreased brain activity. The sedative-based strategy, on the other hand, resulted in alarmingly low cerebral tissue oxygenation and increased EEG power, which might indicate the conscious experience of pain. In the second part, we study a new methodology to integrate EEG and NIRS measurements. From a physiological point of view, both measurements are tightly coupled, since increases in brain activity trigger consequent changes in CBF. We quantified the coupling between both signals and observed that neonates with a small injury had lower coupling values, suggesting a less functional regulation of CBF. Brain abnormalities thus alter CBF regulation and this change in regulation can be captured using signal processing techniques. The third and final part extends the bivariate analysis to a multivariate analysis. Instead of focusing on one component of CBF regulation, we integrated various regulation mechanisms in one straightforward, visual model, which we call the neurocardiovascular graph. This model was studied in three different clinical settings. In a first study, we analyzed the model during immediate transition after birth, and observed that heart rate and arterial saturation significantly coupled with cerebral tissue oxygenation, and that this coupling changes with age. Our second study indicated that the model reflects the dynamics of propofol, a frequently used anesthetic. Propofol administration destroyed all interaction, which consequently restored to baseline. In the third and final study, the neonate's clinical condition and the surgical and anesthesiological approach were observed to affect neonatal physiology and CBF regulation mechanisms at different levels. In summary, the neurocardiovascular graph provides a new way to look at the effect of drugs, surgery and perioperative management on the neonate. This new direction of monitoring could assist clinicians in making patient-specific decisions, aiming to prevent brain injury and impaired neurodevelopmental outcome.

Publication year:2021

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