The Journal of Pediatrics
Volume 153, Issue 4 , Pages 466-472.e1, October 2008

An Algorithm for Identifying and Classifying Cerebral Palsy in Young Children

  • Karl C.K. Kuban, MD, SMEpi

      Affiliations

    • Division of Pediatric Neurology, Department of Pediatrics, Boston Medical Center, Boston University, Boston, MA
    • Corresponding Author InformationReprint requests: Karl Kuban, MD, One Boston Medical Center Place, Dowling 3 South, Boston, MA 02118
    • ,
  • Elizabeth N. Allred, MS

      Affiliations

    • Neuroepidemiology Unit, Department of Neurology, Children's Hospital Boston, Harvard University, Boston, MA
    • Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA
    • ,
  • Michael O'Shea, MD, MPH

      Affiliations

    • Department of Neonatology, Wake Forest University, Winston-Salem, NC
    • ,
  • Nigel Paneth, MD, MPH

      Affiliations

    • Michigan State University-Sparrow Medical Center, East Lansing, MI
    • ,
  • Marcello Pagano, PhD

      Affiliations

    • Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA
    • ,
  • Alan Leviton, MD

      Affiliations

    • Neuroepidemiology Unit, Department of Neurology, Children's Hospital Boston, Harvard University, Boston, MA
    • ,
  • ELGAN Study Cerebral Palsy-Algorithm Group

      Affiliations

    • List of members of ELGAN Study Cerebral Palsy-Algorithm Group available at www.jpeds.com.

Received 26 October 2007; received in revised form 20 February 2008; accepted 2 April 2008. published online 03 June 2008.

Article Outline

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

 

Cerebral palsy (CP) is a group of non-progressive permanent disorders of movement and posture that occur following damage to the developing fetal or infant brain. It is often accompanied by other neurodevelopmental disorders.1, 2, 3 CP occurs in 0.2% of live births, but infants born before 28 weeks gestation have a 50-fold elevated risk when compared with infants born at term,4 with a prevalence between 6% and 26%.5, 6, 7, 8, 9, 10, 11, 12

See editorial, p 451 and related article, p 473

Part of the variability in prevalence may be attributable to the lack of a published operational identification or classification of CP that can be used and replicated by clinicians across settings. Because of inconsistencies in identifying forms of CP, 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.13, 14, 15 In response, we created an algorithm to more reliably identify CP and topography-based subtypes of CP that could be replicated by others. The decision tree of the algorithm we developed models the way a seasoned pediatric neurology clinician might identify and classify CP.

We sought additional confirmation that the CP subtypes identified by our algorithm were distinctive with respect to the clinical severity of dysfunction and in the frequency of associated abnormal findings. We anticipated that children with quadriparesis would be most highly affected or have greater numbers of co-morbid conditions, followed by children with diparesis, children with hemiparesis, and children with no CP. Specifically, we sought to determine the extent to which children with different subtypes varied in: 1) their levels of dysfunction as assessed by the Gross Motor Functional Classification Scale (GMFCS) and 2) their frequency of microcephaly, cognitive impairment, and positive screening on the Modified Checklist for Autism in Toddlers (M-CHAT) at 2 years adjusted age.

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Methods 

ELGAN Study 

The Extremely Low Gestational Age Newborns (ELGAN) study was designed to identify characteristics and exposures that increase the risk of disorders of brain structure and function (including CP) in extremely low gestational age newborns. From 2002 to 2004, women delivering before 28 weeks gestation at 1 of 14 participating institutions in 11 cities in 5 states were asked to enroll in the study. Of the 1201 surviving newborns, 1056 (88%) underwent neurological examinations at 2 years of age and are the subject of this report. This study was approved by all involved institutional human studies review boards, and all families consented to participate in the study.

To standardize neurological examinations across all sites, a stand-alone, multimedia-training video/CD-ROM was developed,16 on the basis of elements of a standard neurological examination.17, 18, 19, 20 Use of the video-CD led to a reliability of 88% to 96% when examiner findings were compared with a gold standard assessment.16 Examiners also evaluated level of disability by using the GMFCS.14, 21, 22, 23 Neurological examiners remained largely unaware of the child's specific medical history, other than that the infant had extremely low gestational age at birth.

Certified examiners administered and scored the mental (MDI) and motor (PDI) scales of the BSID-II. Before testing, examiners were told only the child's age. After test completion, they were told the gestational age to adjust the MDI and PDI for the degree of prematurity. Of the 1056 children who were identified to have CP on the basis of the algorithm, 59 were considered not testable for the MDI and 76 for the PDI. We used the Vineland Adaptive Behavioral Composite scale as a proxy for the MDI for 39 children and the Vineland Motor Skills domain scale as a proxy for the PDI for 43 children. Caregivers of study participants completed the Modified Checklist for Autism in Toddlers (M-CHAT) screen survey.24

Neurological Examination Instrument 

The data collection form included 7 items in the upper extremities in 4 areas of motor function: motor strength (4 items), tone alteration (1 item), posture (1 item), and hand use (1 item). Two areas of function were evaluated in the lower extremities: strength (2 items) and tone (3 items). In our strength assessment, we use indirect measures of power, including the child's ability to push the chest up off the bed with the arms, support body weight on the legs, and lift and move arms and legs.

Algorithm Assumptions 

Assumption 1 

An algorithm that simplifies options is most useful. The range of presentations of topography-based classification of diparesis, quadriparesis, and hemiparesis can include partial forms. For example, monoparesis also occurs. Rather than create a category for monoparesis, we viewed monoparesis of an upper extremity as a partial hemiparesis. Our final categorization includes these 3 groups:

•Quadriparesis: involvement of both lower extremities and involvement of 1 (asymmetric) or both (symmetric) upper extremities; or involvement of both upper extremities and 1 lower extremity (asymmetric quadriparesis);

•Diparesis: involvement of both lower extremities only or only 1 leg;

•Hemiparesis: involvement restricted to only 1 side of the body.

Assumption 2 

Dystonia and dyskinetic forms of CP are more evident later. We did not distinguish qualitative forms of abnormally elevated tone (hypertonia), particularly spasticity and dystonia. Dystonia and spasticity co-occur frequently, and the presence of spasticity may make identification of dystonia more difficult.25, 26 The distinction between the 2 also may be difficult because signs of dystonia may be intermittent and vary with state and level of activity. Finally, the expression of dystonia and dyskinetic forms of CP evolves in the first years of life and usually manifests more obviously later.27

Assumption 3 

The proposed algorithm's value may be limited to the very young child born extremely premature performed at 2 years corrected age. The examination we used and the proposed algorithm was tested and applied to children in the first few years of life. CP evolves in its presentation, sometimes becoming more complex in later years. For example, choreoathetosis, more often seen in infants born at term, becomes more obvious after the first years of life. Because motor findings characteristic of CP can improve or dissipate at later ages,28, 29, 30 we can expect some children given a CP diagnosis at a young age, whether algorithm-based or not, to no longer be given the same diagnosis years later.

Algorithm development as an iterative process: In analyzing the CD-based neurological examination findings, a number of decisions were made sequentially (Figure).

  • View full-size image.
  • Figure. 

    CP classification flow sheet. The algorithm begins by identifying laterality and number of features seen in the lower extremities (column 1). Then on the basis of laterality and extent of findings in the upper extremities (column 2), a CP diagnosis is rendered (column 3).

First, components of the examination that did not specifically evaluate motor status were excluded (eg, visual interactions, extra-ocular muscles).

Second, because the evaluation of deep tendon reflexes is less reliably assessed than other parts of the examination and probably less specific to motor impairment, an effort was made to minimize their impact on the decision tree. After considering approaches that assigned less weight to deep tendon reflexes, we decided to exclude this item from the decision tree.

Third, we required multiple, corroborating abnormal findings. Although we preferred the presence of at least 2 abnormal findings that assess different domains of the motor system (strength, tone, posture, and hand use in the upper extremities and strength and tone in the lower extremities), we accepted strongly affirming items in a single domain (3 of the 4 possible items related to strength in the upper extremities and both items related to strength in the lower extremities). Consequently minimum threshold criteria to identify CP required the presence of at least 3 abnormal items in an upper extremity, at least 2 abnormal items in a lower extremity, or both (Table I).

Table I. Final classification of cerebral palsy types on the basis of topography of upper and lower extremity neurologic examination abnormalities
Number of abnormalities on the neurologic examination
Upper extremityLower extremity
012345
0No CPDiparesis
1
2
3HemiparesisQuadriparesis: if bilateral Hemiparesis: if unilateral
4
5
6
7

Each child is counted only once for the highest number of neurologic exam abnormalities for either right or left extremity.

Fourth, building on these 2- and 3-item requirements for minimal characterization of CP, we classified quadriparesis, hemiparesis, and diparesis on the basis of the quadrants of the body involved (Table II; Figure). Once the threshold for presence of an abnormality was attained, we did not ascertain further gradations of severity by neurological examination findings.

Table II. Prevalence of cerebral palsy classified according to the pattern of extremity involvement (Q plot) in 1056 extremely low gestational age newborns
CP classificationQ plot‡n
Noneoo 936
oo
Diparesisoooo 37
XXXo
Hemiparesis⁎XoXo 19
ooXo
Quadriparesis†XXXoXXXoXX64
XXXXXooXoo

X indicates involvement of each extremity, and o indicates no involvement.

The symbols in the top row are right and left upper extremity and the symbols in the bottom row are right and left lower extremity.

⁎Includes 2 double hemiparesis and 9 quadri-hemiparesis.

†Includes 1 opposite upper extremity-lower extremity diparesis + hemiparesis.

‡Mirror image patterns are combined.

All subjects were classified by using a computerized version of the algorithm program.

Data Analysis 

We used the GMFCS to assess the extent of gross motor impairment. For the purposes of this study, children with a score <1 were classified as not impaired, children with a score of 1 were classified as mildly impaired, and children with a score of 2 to 5 were classified as more highly impaired. The head circumference, measured as part of the 2-year neurological examination, was assigned a Z-score on the basis of standards established by the Centers for Disease Control.31 Children with Z-scores of ≤–2 were deemed microcephalic. On the BSID-II, a score of <70 for either the MDI or PDI defined delay. Children were categorized on the M-CHAT screen as positive when they scored positive in 2 of 6 “critical” items or 3 of the 23 total items.24, 32

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Results 

CP Algorithm 

Of the 1056 children examined at 2 years of age, 11.4% (120/1056) satisfied the algorithm's identification criteria for CP. Fifty-two percent had quadriparesis, 31% had diparesis, and 17% had hemiparesis (Table II).

According to the algorithm, 9 individuals had motor deficits that conformed to a pattern most often seen in diparesis or quadriparesis (leg > arm), however the abnormalities occurred uniquely on 1 side, more typical of hemiparesis, but did not demonstrate the characteristic hemiparetic pattern of arm weakness greater than leg weakness. We found that the extent of motor limitations and prevalence of co-morbid dysfunctions were nearly identical whether the pattern of weakness was arm>leg or leg>arm (data not shown). As a result, we combined into a single group all individuals who had involvement of arm and leg or of arm alone.

Severity and Co-morbidities of CP (Table III) 

More than three-quarters of all children with quadriparesis were given a GMFCS ≥2 (high functional impairment). In contrast, only 8% of children with diparesis were so classified, and an additional 27% had milder functional impairment. Only 19 children were classified as having hemiparesis. Two of them had high functional impairment and 1 had a milder impairment. Two percent (20/934) of children who were not identified as having CP had functional motor impairment, and only 3 of them were more highly affected.

Table III. The percent of children classified by their cerebral palsy classification who had the abnormality on the left
CP classification
QuadriparesisDiparesisHemiparesisNone
GMFCS ≥ 2768110.3
MDI <7072345822
PDI <7093626325
M-CHAT positive76304418
Microcephaly⁎428218
Maximum N643719936

These are column percents.

⁎Head circumference >SD below the mean for age.

Seventy-two percent of children with quadriparesis had a BSID-II MDI subscale score <70, in contrast to 34% of children with diparesis and 58% of children with hemiparesis. The PDI scores, which are more an assessment of motor function than the MDI, tended to be even lower. Thus, 93% of children with quadriparesis and more than half the children with diparesis and children with hemiparesis had a PDI score <70. Only 4% of children with quadriparesis had both a PDI and an MDI score ≥70, in contrast with 32% of children with diparesis, 38% of children with hemiparesis, and 66% of children not identified to have CP (data not shown).

Fully 42% of the 62 children with quadriparesis were microcephalic, in contrast with 21% of children with hemiparesis and 8% of children with diparesis or no CP. Motor impairment was associated with being screened as positive on the M-CHAT. The highest rate of M-CHAT positivity was in children with quadriparesis (76%), with lower rates in children with hemiparesis (44%) and children with diparesis (30%). In contrast, 18% of children without CP were M-CHAT positive.

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Discussion 

With the algorithm developed for this study, we identified CP in 11% of the ELGAN study cohort. This overall CP rate is within range of reported CP rates in extremely low gestational age infants6, 7, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 In the cohorts most comparable with the ELGAN children, the Victorian Collaborative Infant Study Group and the northwest North Carolina cohort, the frequency of CP was 11% to 13%.8, 9, 51

In this cohort, 52% of the CP population had quadriparesis, 31% had diparesis, and 17% had hemiparesis. Algorithm-based quadriparesis prevalence is somewhat higher and diparesis prevalence is modestly lower than other studies have reported on the basis of clinical diagnoses.44, 45, 46, 47, 48, 52 The differences in distribution of topography-based patterns of CP might be a consequence of unstable rates expected with small numbers of children with CP,45, 46, 47, 48 use of birth weight rather than gestational age, inclusion criteria,45, 47, 48 or differences in identification criteria. A lack of operational classification for CP makes studies of antecedents and therapies difficult to replicate and limits reasonable comparisons with reported or future studies.53

We created an algorithm that classifies CP subtypes. Our proposal offers reasonably objective criteria for identifying and classifying CP. Earlier attempts to enhance diagnostic reliability of CP in young children born at extremely low gestational age have focused on severity measures, such as the GMFCS,14, 21, 22, 23 or distinguishing disabling from non-disabling CP.54 In contrast to earlier definitions of CP, the definition proposed by the 2004 International Workshop on Definition and Classification of Cerebral Palsy includes an explicit criterion for the lower limit of abnormality that must be exceeded to diagnose CP (ie, “activity limitation”). However, the authors of that definition did not specify an operational approach to the decision as to whether activity restriction is present. For this purpose, several recent clinical trials have included measurements of the GMFCS, as was used in this study. In attempting to study antecedents of CP, the full range of severity ought to be identified as even individuals with CP who have minimal functional motor disability are at increased risk of substantial neurodevelopmental morbidity.46

The Surveillance of Cerebral Palsy in Europe project55 and the Neonatal Research Network developed decision trees similar to that described here. Both studies identify increased tone or reflexes, but diagnoses were not based on specified objective or reproducible criteria. The Neonatal Research Network enhances reliability, for example, by use of hands-on workshops52, 56, 57 with a CP diagnosis “based on the writing of Amiel Tison.”58 Other investigators have tried to improve uniformity of CP diagnosis with video59 or having more precise definitions of spasticity,60 but neither effort resulted in operational, replicable criteria.

Individuals with quadriparesis are distinguished from individuals with hemiparesis and diparesis in relation to both the severity of dysfunction and the likelihood of having co-morbid conditions. Children with quadriparesis were more than 9 times more likely than children with diparesis to be classified on the GMFCS as having high degree of impairment. In addition, children with quadriparesis were twice as likely as children with diparesis to have an MDI score <70.

Microcephaly was 5 times more likely to occur in children with quadriparesis (43% versus 8%) and twice more likely to occur in children with hemiparesis than in children with diparesis or no CP. Because head circumference percentile measure is a function of brain tissue volume, children with quadriparesis or hemiparesis can be assumed to have substantially less brain tissue than other children. Notably, children with diparesis have a rate of microcephaly that is not distinguishable from children without CP; however, the 8% microcephaly prevalence in both groups is still nearly triple the rate of children born at term.

We also found that children with hemiparesis have higher degree of gross motor dysfunction and co-morbidity rates intermediate between rates of children with quadriparesis and diparesis. This pattern of quadriparesis > hemiparesis > diparesis was seen consistently and was somewhat different from our expectation that children with hemiparesis would be least affected. Excluding the 9 children whose hemiparesis involved the leg as much or more than the arm and the 2 individuals who had a double hemiparesis (hemiparesis with arm more affected than leg on both sides of the body) did not alter these findings.

Some authors challenge the existence of CP subtypes categorized with topography because of vagueness in identifying each type.25 The impairment classification system for CP offered in the 2005 concept/consensus paper, which was re-presented in 2007, recommended simplifying topographic description of CP by removing the term spastic diparesis (and quadriparesis) from the CP lexicon,1, 3 preferring a simple descriptive statement of either 2 or 4 extremity involvement. We, however, have found support for maintaining a topographic-based system of CP categorization.61

The neuropathology underlying hemiparesis may differ from that underlying diparesis or quadriparesis in children born at extremely low gestational age. For example, symmetrical white matter injury close to the ventricle (leg fibers of the pyramidal systems are closest to the ventricles) is likely to be associated with diparesis, and involvement of broader areas of white matter, including areas further from the ventricle subserving arms, is likely to be associated with quadriparesis.62, 63, 64 As a result, children with diparesis have brain lesions that are less likely to be located in white matter association areas that may impair cognition and they have lower rates of microcephaly. In contrast, hemiparesis, with arm more involved than leg, often reflects focal injury/middle cerebral artery distribution stroke-like events, more often sparing fibers closest to the ventricle that control legs.65, 66, 67 Hemiparesis, which involves the leg comparably or more than the arm, is associated with periventricular hemorrhagic infarction.68 Thus the risk factors and antecedents of the different types of CP may vary on the basis of differences in pathophysiology that are associated with these cerebral lesions.

In children born at term, M-CHAT positivity is an indicator of elevated risk of autism. A number of the M-CHAT questionnaire items are influenced by motor dysfunction. Consequently, we cannot distinguish an increased risk of autism from a positive screen mainly on the basis of motor dysfunctions in CP groups. Eighteen percent of infants without CP also screen positive, an observation that warrants further study.

Extremely premature children in our study who do not have CP also had substantial rates of neurodevelopmental dysfunction as measured with the BSID-II. They also had a higher than expected rate of microcephaly, although less notably than in children with CP. This is consistent with the high rate of neurodevelopmental dysfunction reported with extreme prematurity, even in the absence of CP,46 or apparent structural damage on cranial ultrasound scanning studies.69

We have applied the term cerebral palsy to children in this cohort whom we deemed to have motor impairment without the benefit of an expert clinician to validate the presence or absence of CP. We created our algorithm to assist researchers who study CP to have some measure of comparability of CP phenotypes. We believe that from a population perspective, we have identified most children who have CP and excluded from the diagnosis almost all children who do not have the diagnosis at 2 years. We do not advise that our algorithm be used clinically.

Because the algorithm targeted young children, we did not seek to evaluate certain CP forms, such as choreoathetosis, which often do not manifest until a later age.

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Members of Elgan Study Cerebral Palsy-Algorithm Group 

Olaf Dammann, MD, New England Medical Center, Floating Hospital for Children, Boston, MA; Diane Marshall, MD, MPH, University of North Carolina, Chapel Hill, NC; Janet S. Soul, MD, CM, Dept of Neurology, Children's Hospital Boston, Harvard University, Boston, MA and Brigham and Women's Medical Center, Boston, MA; Steve Engelke, MD, East Carolina University, Greenville, NC; Sunila E. O'Connor, MD, University of Chicago, Chicago, IL; Herbert Gilmore, MD, Baystate Medical Center, Springfield, MA; Adré Duplessis, MBChB, MPH, Dept of Neurology, Children's Hospital Boston, Harvard University, Boston, MA, and Brigham and Women's Medical Center, Boston, MA; Kalpathy Krishnamoorthy, MD, Massachusetts General Hospital, Boston, MA; Cecil Hahn, MD, FRCPC, Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada; Karen Miller, MD, Tufts- New England Medical Center, Boston, MA; Paige T. Church, MD, Department of Pediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada; Cecelia Keller, PT, MHA, Tufts- New England Medical Center, Boston, MA; Richard Bream, MD, UMass Memorial Medical Center, Worcester MA; Robin Adair, MD, UMass Memorial Medical Center, Worcester MA; Alice Miller, PT, MS, UMass Memorial Medical Center, Worcester MA; Elaine Romano, MSN, Yale University Medical Center, New Haven, CT; Haim Bassan, MD, Dept of Neurology, Children's Hospital Boston, Harvard University, Boston, MA; Kristi Milowic, MD, University of North Carolina, Chapel Hill, NC; Carol Hubbard, RN, MSN, CPNP, University of North Carolina, Chapel Hill, NC; Lisa Washburn, MD,2 Robert Dillard, MD, Department of Neonatology, Wake Forest University, Winston-Salem, NC; Cherrie Heller, MD, Department of Neonatology, Wake Forest University, Winston-Salem, NC; Wendy Burdo-Hartman, MD, DeVos Children's Hospital, Grand Rapids, MI; Lynn Fagerman, MSN, APRN-BC, PNP, DeVos Children's Hospital, Grand Rapids, MI; Dinah Sutton, RN, DeVos Children's Hospital, Grand Rapids, MI; Padu Karna, MBBS, Michigan State University-Sparrow Medical Center, East Lansing, MI; Nicholas Olomu, MB, BS, Michigan State University-Sparrow Medical Center, East Lansing, MI; Leslie Caldarelli, MD, University of Chicago, Chicago, IL; Melisa Oca, MD, William Beaumont Hospital, Ann Arbor, MI; Kim Lohr, MSN, DeVos Children's Hospital, Grand Rapids, MI; Albert Scheiner, MD, UMass Memorial Medical Center, Worcester MA; Omar Khwaha, MD, PhD, Dept of Neurology, Children's Hospital Boston, Harvard University, Boston, MA; Christy Stine, MD, Department of Pediatrics, University of Massachusetts Medical School, Worcester MA; Scott S. MacGilvray, MD, East Carolina University, Greenville, NC; Sharon Buckwald, MD, East Carolina University, Greenville, NC; Victoria J. Caine, PT, Michigan State University-Sparrow Medical Center, East Lansing, MI.

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 Financial support for this research was provided by the National Institute of Neurological Disorders and Stroke (Cooperative agreement: 1 U01 NS 40069-01A2). The authors declared no potential conflicts of interest.

PII: S0022-3476(08)00280-1

doi:10.1016/j.jpeds.2008.04.013

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