An algorithm for identifying and classifying cerebral palsy in young children
Karl C K Kuban1, Elizabeth N Allred, Michael O'Shea
1Division of Pediatric Neurology, Department of Pediatrics, Boston Medical Center, Boston University, Boston, MA, USA. karl.kuban@bmc.org
Insights
A new algorithm classifies cerebral palsy (CP) subtypes in preterm infants. Quadriparesis is linked to higher impairment and co-morbidities like microcephaly and autism screening positivity compared to diparesis.
Area of Science:
- Neurology
- Pediatrics
- Developmental Pediatrics
Background:
- Cerebral palsy (CP) affects children born prematurely, with varying subtypes impacting development.
- Standardized classification of CP subtypes is crucial for research and clinical comparisons.
Purpose of the Study:
- To develop a novel algorithm for classifying cerebral palsy subtypes.
- To determine the prevalence of diparesis, hemiparesis, and quadriparesis in very preterm infants.
- To compare the extent of handicap and co-morbidities across CP subtypes.
Main Methods:
- A cohort of 2-year-old children born before 28 weeks gestation underwent a standardized neurological examination.
- An algorithm was developed to classify children into CP subtypes: diparesis, hemiparesis, and quadriparesis.
- Children were compared based on microcephaly, cognitive impairment (BSID-II), and autism screening (MCAT).
Main Results:
- 11.4% of 1056 very preterm children had algorithm-classified CP.
- Quadriparesis was the most prevalent subtype (52%), followed by diparesis (31%) and hemiparesis (17%).
- Children with quadriparesis exhibited significantly higher rates of impairment, microcephaly, cognitive deficits, and autism screening positivity compared to those with diparesis.
Conclusions:
- A reliable algorithm for classifying CP subtypes in very preterm infants was successfully developed.
- Quadriparesis is associated with the most severe gross motor dysfunction and highest rates of co-morbidities.
- Diparesis appears to be the subtype with the least severe outcomes in this cohort.
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.
