Identifying Postural Instability in Children with Cerebral Palsy Using a Predictive Model: A Longitudinal Multicenter Study

Author:

Bertoncelli Carlo Marioi123,Bertoncelli Domenico13,Bagui Sikha S.1ORCID,Bagui Subhash C.1ORCID,Costantini Stefania3ORCID,Solla Federico2ORCID

Affiliation:

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

2. EEAP H Germain and Department of Pediatric Orthopaedic Surgery, Lenval Foundation, University Pediatric Hospital of Nice, 06000 Nice, France

3. Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, 67100 L’Aquila, Italy

Abstract

Insufficient postural control and trunk instability are serious concerns in children with cerebral palsy (CP). We implemented a predictive model to identify factors associated with postural impairments such as spastic or hypotonic truncal tone (TT) in children with CP. We conducted a longitudinal, double-blinded, multicenter, descriptive study of 102 teenagers with CP with cognitive impairment and severe motor disorders with and without truncal tone impairments treated in two specialized hospitals (60 inpatients and 42 outpatients; 60 males, mean age 16.5 ± 1.2 years, range 12 to 18 yrs). Clinical and functional data were collected between 2006 and 2021. TT-PredictMed, a multiple logistic regression prediction model, was developed to identify factors associated with hypotonic or spastic TT following the guidelines of “Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis”. Predictors of hypotonic TT were hip dysplasia (p = 0.01), type of etiology (postnatal > perinatal > prenatal causes; p = 0.05), male gender, and poor manual (p = 0.01) and gross motor function (p = 0.05). Predictors of spastic TT were neuromuscular scoliosis (p = 0.03), type of etiology (prenatal > perinatal > postnatal causes; p < 0.001), spasticity (quadri/triplegia > diplegia > hemiplegia; p = 0.05), presence of dystonia (p = 0.001), and epilepsy (refractory > controlled, p = 0.009). The predictive model’s average accuracy, sensitivity, and specificity reached 82%. The model’s accuracy aligns with recent studies on applying machine learning models in the clinical field.

Publisher

MDPI AG

Subject

Clinical Biochemistry

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