Neurodevelopmental Outcomes of Infants Born Prematurely

TLDR

Long‑term follow‑up of preterm infants is essential to clarify neurodevelopmental outcomes, yet methodological challenges and unclear functional significance of brain abnormalities limit definitive conclusions. The study seeks to refine analytic approaches and clarify outcome definitions in neurodevelopmental research on preterm infants. The authors propose using cluster analysis, structural equation modeling, growth‑curve analysis, developmental epidemiology, and risk‑index‑based background control to improve analyses. Consistent evidence shows preterm children have greater deficits in motor, visuomotor, cognitive, academic, language, executive, and behavioral domains than full‑term peers, and better assessment techniques are discussed.

Abstract

Long-term follow-up of infants born prematurely is necessary to determine neurodevelopmental outcomes, particularly with the expansion of interest from major disabilities to high prevalence/low severity dysfunctions. Models of pathogenesis include changes due to developmental disruptions and to injury, the magnitude and type of change influenced by the infant's age, and central nervous system recovery and reorganization. Alterations in neurogenesis, migration, myelination, cell death, and synaptogenesis occur even in the absence of insult. Despite increased knowledge regarding these processes, the functional significance of brain abnormalities is unclear. Because of methodologic problems in follow-up studies, it is difficult to characterize outcome definitively. Nonetheless, an acceptable degree of agreement across studies is found with regard to specific neurodevelopmental outcomes: motor/neurologic function, visuomotor integrative skills, IQ, academic achievement, language, executive function, and attention-deficit hyperactivity disorder/behavioral issues. In general, children born prematurely have more problems in these areas than do their normal birth weight counterparts. Suggestions for improved analyses and clarification of outcomes include use of cluster analysis, structural equation modeling, growth curve analysis, developmental epidemiologic approaches, and better control of background variables using risk indexes and factor scores. Better assessment techniques measuring functions documented to be at higher risk of problems are discussed.