An Algorithm for Identifying and Classifying Cerebral Palsy in Young Children

Objective

To develop an algorithm on the basis of data obtained with a reliable, standardized neurological examination and report the prevalence of cerebral palsy (CP) subtypes (diparesis, hemiparesis, and quadriparesis) in a cohort of 2-year-old children born before 28 weeks gestation.

Study design

We compared children with CP subtypes on extent of handicap and frequency of microcephaly, cognitive impairment, and screening positive for autism.

Results

Of the 1056 children examined, 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 more 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 mental scale of the BSID-II (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%).

Conclusion

We developed an algorithm that classifies CP subtypes, which should permit comparison among studies. Extent of gross motor dysfunction and rates of co-morbidities are highest in children with quadriparesis and lowest in children with diparesis.

Abbreviations: CP, Cerebral palsy, ELGAN, Extremely Low Gestational Age Newborns, GMFCS, Gross Motor Functional Classification Scale, M-CHAT, Modified Checklist for Autism in Toddlers, MDI, Mental scale of the BSID-II, PDI, Motor scales of the BSID-II