Related Experiment Video
Updated: Oct 5, 2026

Preterm EEG: A Multimodal Neurophysiological Protocol
Published on: February 18, 2012
Enabling evidence generation for extremely preterm neonates: the use of neonatal real-world data
Ryan Kilpatrick1,2, Rachel G Greenberg3,4, Danielle Boyce5
1Department of Pediatrics, Tufts University School of Medicine, Boston, MA, USA. ryan.kilpatrick1@tuftsmedicine.org.
Abstract:
Extremely low gestational age neonates (ELGANs), born before 28 weeks of gestation, face the greatest burden of neonatal mortality and morbidity, yet remain among the most underserved populations in clinical research. Most medications administered in the neonatal intensive care unit lack adequate safety, efficacy, and dosing data, and no major therapeutic advances have improved preterm survival since the introduction of surfactant in the early 1990s. Real-world data (RWD) presents a promising opportunity to bridge these evidence gaps. ELGANs are ideal candidates for RWD research given the extraordinary density and granularity of data generated during prolonged neonatal intensive care unit stays, ethical and practical barriers to traditional clinical trials, and wide variations in clinical practice. In this review, we examine how ELGANs may benefit from RWD, describe the current landscape of common data models and their neonatal limitations, propose a minimum neonatal-specific variable set, and explore the roles of key stakeholders in building RWD infrastructure. We further highlight the regulatory applications of RWD including external controls, target trial emulation, pharmacovigilance, and natural history studies. Realizing the potential of RWD for ELGANs requires coordinated investment from clinicians, informaticians, regulators, and families to improve outcomes for the most vulnerable patients in medicine. IMPACT: ELGANs are an ideal population for RWD research given the extraordinary density of longitudinal NICU data, ethical and practical barriers to traditional randomized trials, and wide variations in evidence-based clinical practice. Current common data models were not designed with neonatal complexity in mind, lacking native support for maternal-infant linkage, continuous physiologic data, weight-based medication dosing, and long-term neurodevelopmental follow-up. A coordinated, multi-stakeholder framework involving neonatologists, bedside caregivers, informaticians, regulators, and families is essential to building the RWD infrastructure needed to improve outcomes for ELGANs.

