Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

Author:

Bertoncelli Carlo M.12,Altamura Paola3,Vieira Edgar Ramos4,Bertoncelli Domenico5,Thummler Susanne6,Solla Federico1

Affiliation:

1. Department of Pediatric Orthopaedic Surgery, Lenval University Pediatric Hospital of Nice, Nice, France

2. EEAP H. Germain Fondation Lenval—Children’s Hospital, Nice, France

3. Department of Medicinal Chemistry and Pharmaceutical Technology, University of Chieti, Chieti, Italy

4. Department of Physical Therapy, Florida International University, Miami, FL, USA

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

6. Children’s Hospitals of Nice CHU-Lenval, Child and Adolescent Psychiatry, Nice, France

Abstract

Background: Intellectual disability and impaired adaptive functioning are common in children with cerebral palsy, but there is a lack of studies assessing these issues in teenagers with cerebral palsy. Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy. Methods: This was a multicenter controlled cohort study of 91 teenagers with cerebral palsy (53 males, 38 females; mean age ± SD = 17 ± 1 y; range: 12-18 y). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, motor skills, eating, and drinking abilities were collected between 2005 and 2015. Intellectual disability was classified as “mild,” “moderate,” “severe,” or “profound” based on adaptive functioning, and according to the DSM-5 after 2013 and DSM-IV before 2013, the Wechsler Intelligence Scale for Children for patients up to ages 16 years, 11 months, and the Wechsler Adult Intelligence Scale for patients ages 17-18. Statistical analysis included Fisher’s exact test and multiple logistic regressions to identify factors associated with intellectual disability. A predictive machine learning model was developed to identify factors associated with having profound intellectual disability. The guidelines of the “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Statement” were followed. Results: Poor manual abilities ( P ≤ .001), gross motor function ( P ≤ .001), and type of epilepsy (intractable: P = .04; well controlled: P = .01) were significantly associated with profound intellectual disability. The average model accuracy, specificity, and sensitivity was 78%. Conclusion: Poor motor skills and epilepsy were associated with profound intellectual disability. The machine learning prediction model was able to adequately identify high likelihood of severe intellectual disability in teenagers with cerebral palsy.

Publisher

SAGE Publications

Subject

Neurology (clinical),Pediatrics, Perinatology and Child Health

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