The Journal of Pediatrics
Volume 153, Issue 4 , Pages 451-452, October 2008

The Challenge of Cerebral Palsy Classification: The ELGAN Study

  • Pasquale J. Accardo, MD

      Affiliations

    • Division of General Pediatrics, Virginia Commonwealth University School of Medicine, Richmond, Virginia
    • Corresponding Author InformationReprint requests: Pasquale J. Accardo, MD, Virginia Commonwealth University Medical Center, Division of General Pediatrics, 9000 Stony Point Parkway, Second Floor, PO Box 980304, Richmond, VA 23235
    • ,
  • Alexander H. Hoon Jr, MD, MPH

      Affiliations

    • Johns Hopkins University School of Medicine, Phelps Center for Cerebral Palsy and Neurodevelopmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland

Article Outline

Abbreviations: CP, Cerebral palsy, ELGAN, Extremely Low Gestational Age Newborns [study]

 

“When the blind men had felt the elephant, the raja went to each of them and said to each, ‘Well, blind man, have you seen the elephant? Tell me, what sort of thing is an elephant?’”1

See related article, p 466

Cerebral palsy (CP) is a group of motor-impairment syndromes secondary to a wide range of genetic and acquired disorders of early brain development. Like the elephant, CP can be variably described based on many characteristics, including functional impairment, neurologic findings, etiology, imaging, and topography. The wide range of classification systems, lack of consensus on optimal treatment, and plethora of medical, rehabilitative, and complementary-alternative approaches to management reflect the state of the science.2

Before Part H of Public Law 99-457 (Education of the Handicapped Act)3 funded early intervention services for infants and toddlers in 1986, it was necessary to establish a diagnosis of CP to provide physical therapy for young children with motor impairment. As developmental support services became more readily available for young children with even mild motor delays, a formal determination of CP was no longer required to provide eligibility for services. This change was welcomed by families and clinicians alike, with the caveat that some infants “outgrew” the diagnosis but later manifested other neurologic disabilities.4

However, the early identification of CP remains important to evaluate risk factors, early treatment, and later management during childhood. The subtypes of CP have long been recognized to have distinctive associations with various neurodevelopmental disorders.5 In terms of anticipatory guidance, early identification of the specific type of CP has significant implications for the diagnosis and treatment of a wide array of associated neurologic disabilities. Pediatricians managing children receiving early interventions for motor delay need to discriminate among (benign cerebral) hypotonia, developmental coordination disorder, mild motor delay within the broad range of normal, and various types of CP. The prognosis and management for these entities differ greatly.

Recent data from the United States suggests an increasing prevalence of CP. In a multisite, population-based developmental disabilities network, the reported average prevalence of CP has increased from the relatively stable 2 cases per 1000 to 3.6 cases per 1000, with bilateral spastic forms being the most common.6 From an etiologic perspective, there remain frequent associations with maternal infection, preterm birth, and multiple births,7 as well as with hypoxic and genetic risk factors.8

The ELGAN (Extremely Low Gestational Age Newborns) study is a multicenter project involving 14 institutions following infants born between 23 and 27 weeks of gestation to identify characteristics and exposures that increase the risk for neurologic disorders. In addition to the study reported here, important results from the ELGAN study include the development of CD-ROM training tools for performing structured neurologic examinations,9 the description of factors associated with treatment of hypotension,10 and the reliable assessment of brain abnormalities using ultrasonography.11

The CP classification algorithm reported in the present study12 involved 120 2-year-old children with CP, drawn from a cohort of 1056 survivors who received a neurologic examination. The study's overall goal was to create a topographic classification system to improve the ability to compare findings across studies. A stand-alone, multimedia training video/CD-ROM was used in reliability training for a standardized neurologic examination. From the results of this examination, 7 items from the upper extremities and 5 items from the lower extremities were used to create topographical subtypes, including diparesis (found in 31% of the subjects), hemiparesis (in 17%), and quadriparesis (in 52%). As others also have reported, children with quadriparesis had more severe associated impairments, including cognitive disability and risk for autism, as well as microcephaly.

As the blind men must blend their perceptions to fully describe the elephant, so must we combine tools to classify children with CP. The next step will be to link these results to imaging. Although conventional imaging cannot identify injury in specific neurons, it can reliably classify cases into white matter lesions, cortical and deep gray lesions, or brain maldevelopments.13, 14 Furthermore, diffusion tensor imaging can now pinpoint injury in specific white matter pathways in preterm children with CP.15 The incorporation of quantitative imaging findings into an algorithmic approach should further improve the precision of classification.

The long-term goals in CP are to improve prevention and to develop neuroprotective strategies in the neonatal period and effective interventions during childhood. Findings from the ELGAN study will continue to focus the research on improving classification and will encourage general pediatricians to examine children with CP from multiple perspectives.

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References 

  1. Jainism and Buddhism. Udana 68-69: Parable of the Blind Men and the Elephant.
  2. Sanger TD, Delgado MR, Gaebler-Spira D, Hallett M, Mink JW. Task Force on Childhood Motor Disorders (Classification and definition of disorders causing hypertonia in childhood). Pediatrics. 2003;111:e89–e97
  3. Education of the Handicapped Act Amendments. Pubic Law no. 99-457 (1986).)
  4. Nelson NB, Ellenberg JH. Children who “outgrew” cerebral palsy. Pediatrics. 1982;69:529–536
  5. Crothers B, Paine RS. The Natural History of Cerebral Palsy. Cambridge, MA: Harvard University Press; 1959;
  6. Yeargin-Allsopp M, Van Naarden Braun K, Doernberg NS, Benedict RE, Kirby RS, Durkin MS. Prevalence of cerebral palsy in 8-year-old children in three areas of the United States in 2002: a multisite collaboration. Pediatrics. 2008;121:547–554
  7. Bax M, Tydeman C, Flodmark O. Clinical and MRI correlates of cerebral palsy: the European Cerebral Palsy Study. JAMA. 2006;296:1602–1608
  8. Keogh JM, Badawi N. The origins of cerebral palsy. Curr Opin Neurol. 2006;19:129–134
  9. Kuban KC, O'Shea M, Allred E, Leviton A, Gilmore H, DuPlessis A, et al. Video and CD-ROM as a training tool for performing neurologic examinations of 1-year-old children in a multicenter epidemiologic study. J Child Neurol. 2005;20:829–831
  10. Laughon M, Bose C, Allred E, O'Shea TM, Van Marter LJ, Bednarek F ELGAN Study Investigators. Factors associated with treatment for hypotension in extremely low gestational age newborns during the first postnatal week. Pediatrics. 2007;119:396–397
  11. Kuban K, Adler I, Allred EN, Batton D, Bezinque S, Betz BW, et al. Observer variability assessing US scans of the preterm brain: the ELGAN study. Pediatr Radiol. 2007;37:1201–1208
  12. Kuban KCK, Allred EN, O'Shea M, Paneth N, Pagano M, Leviton A. An algorithm for identifying and classifying cerebral palsy in young children. J Pediatr. 2008;153:466–472
  13. Accardo J, Kammann H, Hoon AH. Neuroimaging in cerebral palsy. J Pediatr. 2004;145:S19–S27
  14. Krägeloh-Mann I, Horber V. The role of magnetic resonance imaging in elucidating the pathogenesis of cerebral palsy: a systematic review. Dev Med Child Neurol. 2007;49:144–151
  15. Nagae LM, Hoon AH, Stashinko E, Lin D, Zhang W, Levey E, et al. Diffusion tensor imaging in children with periventricular leukomalacia: variability of injuries to white matter tracts. AJNR Am J Neuroradiol. 2007;28:1213–1222

PII: S0022-3476(08)00474-5

doi:10.1016/j.jpeds.2008.05.046

Refers to article:

  • An Algorithm for Identifying and Classifying Cerebral Palsy in Young Children , 03 June 2008

    Karl C.K. Kuban, Elizabeth N. Allred, Michael O'Shea, Nigel Paneth, Marcello Pagano, Alan Leviton, ELGAN Study Cerebral Palsy-Algorithm Group
    The Journal of Pediatrics October 2008 (Vol. 153, Issue 4, Pages 466-472.e1)

The Journal of Pediatrics
Volume 153, Issue 4 , Pages 451-452, October 2008

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