Annette Majnemer, Bernard Rosenblatt, Patricia Riley; Predicting Outcome in High-Risk Newborns With a Neonatal Neurobehavioral Assessment. Am J Occup Ther 1994;48(8):723–732. https://doi.org/10.5014/ajot.48.8.723
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Objectives. Effective medical management and rehabilitation efforts in neonates at risk depend on early identification of underlying brain injury. The aim of this study was to determine the prognostic value of the Einstein Neonatal Neurobehavioral Assessment Scale (ENNAS) in high-risk neonates, and to compare its predictive validity at two stages in development (i.e., 1 and 3 years of age).
Method. Twenty-three healthy neonates (control group) and 51 high-risk neonates (high-risk group) were assessed at term and were followed longitudinally. At 1 and 3 years, subjects in both groups were evaluated in a blind fashion by a psychologist and a pediatric neurologist.
Results. Developmental delays became more apparent as high-risk newborns matured; the percentage of subjects with an abnormal Griffiths general quotient increased from 1 year (13.7%) to 3 years (39%). Analysis revealed that a normal neonatal performance on the ENNAS in high-risk subjects accurately predicted a favorable outcome at 1 year and 3 years of age. Although an abnormal ENNAS was not consistently associated with a poor outcome, the positive predictive value improved markedly from 1 year to 3 years of age.
Conclusion. The findings indicate that a normal neonatal neurobehavioral assessment is reassuring, as most of these children are free of neurodevelopmental sequelae at 3 years of age. For many persons, the ENNAS may provide early evidence of a pattern of brain injury that is manifested only as the child is challenged by more complex skill acquisition such as language, memory, and perceptual-motor tasks. The ability of this assessment to predict behavioral and academic skills at school age remains to be determined. Early identification of developmental deficits enables occupational therapists to direct infants to appropriate early intervention programs, thus optimizing their functional potential.
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