After NICU discharge: Feeding and growth of low-income urban preterm infants through the first year

BACKGROUND

Little is known about preterm infant feeding and growth in the outpatient setting, and there are no standardized post-hospital discharge feeding guidelines. This study aims to describe post-neonatal intensive care unit (NICU) discharge growth trajectories of very preterm (<32 weeks gestational age (GA)) and moderately preterm (32-34 0/7 weeks GA) infants managed by community providers and to determine the association between post-discharge feeding type and growth Z-scores and z-score changes through 12 months corrected age (CA).

METHODS

This retrospective cohort study included very preterm infants (n = 104) and moderately preterm infants (n = 109) born 2010-2014 and followed in community clinics for low-income, urban families. Infant home feeding and anthropometry were abstracted from medical records. Repeated measures analysis of variance calculated adjusted growth z-scores and z-score differences between 4 and 12 months CA. Linear regression models estimated associations between 4 months CA feeding type and 12 months CA anthropometry.

RESULTS

Moderately preterm infants on nutrient-enriched vs. standard term feeds at 4 months CA had significantly lower length z-scores at NICU discharge that persisted to 12 months CA (-0.04 (0.13) vs. 0.37 (0.21), respectively, P = .03), with comparable length z-score increase for both groups between 4 and 12 months CA. Very preterm infants' 4 months CA feeding type predicted 12 month CA body mass index z-scores (β=-0.66 (-1.28, -0.04)).

CONCLUSION

Community providers may manage preterm infant post-NICU discharge feeding in the context of growth. Further research is needed to explore modifiable drivers of infant feeding and socio-environmental factors that influence preterm infant growth trajectories.

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.