An Algorithm for Identifying and Classifying Cerebral Palsy in Young Children

Cerebral palsy (CP) is a group of non-progressive permanent disorders of movement and posture following damage to the developing fetal or infant brain. It is often accompanied by other neurodevelopmental disorders. Infants born before 28 weeks gestation have a 50-fold elevated risk when compared with infants born at term, with a prevalence between 6% and 26%.
At present, there is no published operational identification or classification of CP that can be used and replicated by clinicians across settings. Some experts have recommended classifying CP primarily on the basis of the degree of severity of gross motor function, while minimizing or eliminating classic topography-based categorization of CP types. In response, an American group conducted a study to develop an algorithm on the basis of data obtained with a reliable, standardized, neurological examination and to report the prevalence of CP subtypes (diparesis, hemiparesis, and quadriparesis). Such an algorithm has the advantage to be replicable by other investigators.
This study was conducted in a cohort of 2-year-old children born before 28 weeks gestation and enrolled in the ELGAN (Extremely Low Gestational Age Newborns) study.
Of the 1056 children examined (88% of children enrolled in the ELGAN study), 11.4% (120) were given an algorithm-based classification of CP. Of these children, 31% had diparesis, 17% had hemiparesis, and 52% had quadriparesis. Children with quadriparesis were 9 times more likely than children with diparesis (76% versus 8%) to be highly impaired and 5 times more likely than children with diparesis to be microcephalic (43% versus 8%). They were more than twice as likely as children with diparesis to have a score <70 on the BSID-II (Bayley Scale of Infant Development) mental scale (75% versus 34%) and had the highest rate of the Modified Checklist for Autism in Toddlers positivity (76%) compared with children with diparesis (30%) and children without CP (18%).
Noteworthy, extremely premature children involved in the study who did not have CP also had substantial rates of neurodevelopmental dysfunction as measured with the BSID-II and a higher than expected rate of microcephaly, although less notably than in children with CP.
This algorithm was created to assist researchers who study CP to have some measure of comparability of CP phenotypes. In fact, from a population perspective, most children who have CP in the ELGAN study were identified and almost all children who do not have the diagnosis at 2 years were excluded from the diagnosis. However, this algorithm is not ready to be used clinically. Since the algorithm targeted young children, certain CP forms (e.g. choreoathetosis) which often do not manifest until a later age were not evaluated.

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