Follow-up care and support to parents of premature children: Multidisciplinary versus regular follow-up care.

BACKGROUND: Preterm care involves clinical measures almost exclusively aimed at keeping the preterm alive and ready for discharge from hospital. Children are then enrolled in clinical follow-up care after this stressful period, but mental or specialised care for parents and child is often not embedded in the routine of a neonatal hospital ward and the family is then dependent on institutions for mental health or child health clinics, that is, regular care that is regionally scattered. This study aimed to investigate experiences of parents with regular care and compare them with parents visiting a fixed, specialised, multidisciplinary institute, outside the hospital walls, for preterm follow-up care. METHODS: A survey was conducted among 56 parents (regular follow-up care N = 26; multidisciplinary follow-up care N = 30) of children born prematurely. The survey consisted out of items like parents' experiences with follow-up care, like the organisation of care, supportive care, environmental support and experienced stress. RESULTS: Parents receiving multidisciplinary follow-up care reported higher levels of satisfaction with organisation of care (F = 5.45; p = 0.023) and supportive care (F = 11.69; p = 0.001) than parents receiving regular follow-up care. Also, it was found that the extent of support from the social environment alleviated stress but only in parents receiving regular follow-up care (ß = -0.47; p = 0.016). CONCLUSIONS: The current study findings indicate that experience with follow-up care is valued higher when receiving multidisciplinary care than regular care, and the degree in environmental support alleviates stress in parents receiving regular care.

A Preliminary Study of Correlates of Premature Birth and Their Influence on Cortisol Levels in Young Children.

OBJECTIVE: The HPA-axis is programmed during early infancy, but a lot is unknown about the programming of the HPA-axis in prematurely born or small for gestational age (SGA) children. Therefore, the aim of this preliminary study was to investigate the influence of prematurity and variables associated with birth on cortisol levels in young children. METHODS: Cortisol was measured in a cross-sectional design in 38 premature born participants (<37 weeks of gestation), aged between 3 - 9 years old. Correlates of prematurity (degree of prematurity and birth delivery route) were investigated in relationship with cortisol levels with regression analysis. RESULTS: Corrected for sex, delivery by C-section was associated with lower cortisol levels in the children (ß = -.42, p = .028), with an explained variance of 34%. CONCLUSION: Birth delivery route by C-section is associated with lowered (or flattened) cortisol levels in children born prematurely. This is clinically relevant and might have important implications, because an HPA-axis disturbance might lead to developmental problems later on in life. However, future research is necessary to investigate the underlying indications for performing a C-section, which will help to understand factors that influence the HPA-axis development in children born prematurely.

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. STUDY DESIGN: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. RESULTS: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. CONCLUSIONS: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.