Title

An algorithm for identifying and classifying cerebral palsy in young children

UMMS Affiliation

Department of Pediatrics

Date

10-7-2008

Document Type

Article

Medical Subject Headings

*Algorithms; CD-ROM; Cerebral Palsy; Child, Preschool; Comorbidity; Hemiplegia; Humans; Microcephaly; Neurologic Examination; Prevalence; Quadriplegia

Disciplines

Pediatrics

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.

Rights and Permissions

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

Comments

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

Related Resources

Link to Article in PubMed