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An algorithm for identifying and classifying cerebral palsy in young children

Kuban, Karl C.K.
Allred, Elizabeth N.
O'Shea, Michael A.
Paneth, Nigel
Pagano, Marcello
Leviton, Alan
ELGAN Study Cerebral Palsy-Algorithm Group
Bream, Richard
Adair, Robin
Miller, Alice
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Abstract

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.

Source

J Pediatr. 2008 Oct;153(4):466-72. Epub 2008 Jun 2. Link to article on publisher's site

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DOI
10.1016/j.jpeds.2008.04.013
PubMed ID
18534210
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Notes

Richard Bream, Robin Adair, and Alice Miller are members of the ELGAN Study Cerebral Palsy-Algorithm Group.

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