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Related Experiment Video

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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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Identifying Postural Instability in Children with Cerebral Palsy Using a Predictive Model: A Longitudinal Multicenter

Carlo Marioi Bertoncelli1,2,3, Domenico Bertoncelli1,3, Sikha S Bagui1

  • 1Department of Computer Science, Hal Marcus College of Science & Engineering, University of West Florida, Pensacola, FL 32514, USA.

Diagnostics (Basel, Switzerland)
|June 28, 2023
PubMed
Summary

A new predictive model accurately identifies factors linked to trunk tone impairments in children with cerebral palsy (CP). This aids in understanding and managing postural control issues in this population.

Keywords:
artificial intelligence in diagnosticscerebral palsymachine learningpostural instabilityprediction modeltruncal tone

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Area of Science:

  • Neurology
  • Pediatrics
  • Rehabilitation Medicine

Background:

  • Children with cerebral palsy (CP) often experience significant challenges with postural control and trunk instability.
  • Truncal tone (TT) impairments, including spastic or hypotonic types, are common and impact functional abilities.

Purpose of the Study:

  • To develop and validate a predictive model, TT-PredictMed, for identifying factors associated with spastic and hypotonic truncal tone in children with CP.
  • To improve the understanding of the complex interplay of factors contributing to postural impairments in this population.

Main Methods:

  • A longitudinal, double-blinded, multicenter descriptive study involving 102 teenagers with CP.
  • Development of a multiple logistic regression model (TT-PredictMed) following "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis" guidelines.
  • Collection of clinical and functional data from 2006 to 2021.

Main Results:

  • Predictors of hypotonic TT included hip dysplasia, postnatal etiology, male gender, and poorer manual and gross motor function.
  • Predictors of spastic TT included neuromuscular scoliosis, prenatal etiology, specific spasticity patterns (quadri/triplegia), dystonia, and refractory epilepsy.
  • The TT-PredictMed model demonstrated an average accuracy, sensitivity, and specificity of 82%.

Conclusions:

  • The TT-PredictMed model effectively identifies key factors associated with hypotonic and spastic truncal tone in children with CP.
  • These findings contribute to a better clinical understanding and management of postural instability in pediatric CP.
  • The model's accuracy supports the application of machine learning in clinical prognosis for CP.