1. Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model
- Author
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Susanne Thummler, Federico Solla, Domenico Bertoncelli, Carlo M. Bertoncelli, Paola Altamura, and Edgar Ramos Vieira
- Subjects
Male ,Adolescent ,Cerebral palsy ,Adaptive functioning ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Risk Factors ,Intellectual Disability ,Intellectual disability ,medicine ,Humans ,0501 psychology and cognitive sciences ,Child ,Predictive learning ,Cerebral Palsy ,05 social sciences ,Models, Theoretical ,Prognosis ,medicine.disease ,Severe intellectual disabilities ,Motor Skills ,Pediatrics, Perinatology and Child Health ,Female ,Neurology (clinical) ,Psychology ,030217 neurology & neurosurgery ,050104 developmental & child psychology ,Clinical psychology - 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.
- Published
- 2019
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