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.
EvansPMEvansSJWAlbermanE. Cerebral palsy: why we must plan for survival. Arch Dis Child. 1990;65:1329–1333.
2.
Willerslev-OlsenMLorentzenJSinkjaerTNielsenJB. Passive muscle properties are altered in children with cerebral palsy before the age of 3 years and are difficult to distinguish clinically from spasticity. Dev Med Child Neurol. 2013;55:617–623.
3.
Bjorgaas HM1, Hysing M, Elgen I. Psychiatric disorders among children with cerebral palsy at school starting age. Res Dev Disabil. 2012;33(4):1287–1293. doi:10.1016/j.ridd.2012.02.024.
4.
BaxM.GoldsteinM.RosenbaumP.. Proposed definition and classification of cerebral palsy. Dev Med Child Neurol. 2005;47:571–576.
5.
BesagFMC. Childhood epilepsy in relation to mental handicap and behavioral disorders. J Child Psychol. 2002;43:103–131.
6.
HillTLGraySAKampsJLEnrique VarelaR. Age and adaptive functioning in children and adolescents with ASD: the effects of intellectual functioning and ASD symptom severity. J Autism Dev Disord. 2015;45(12):4074–4083. doi:10.1007/s10803-015-2522-6.
7.
StadskleivKJahnsenRAndersenGLvon TetzchnerS. Neuropsychological profiles of children with cerebral palsy. Dev Neurorehabil. 2018;21(2):108–120. doi:10.1080/17518423.2017.1282054.
8.
VukojeviMCvitkovićTSplavskiBOstojićZŠumanović-GlamuzinaDŠimićJ. Prevalence of intellectual disabilities and epilepsy in different forms of spastic cerebral palsy in adults. Psychiatr Danub. 2017;29(suppl 2):111–117.
9.
BjorgaasHMElgenIBoeTHysingM. Mental health in children with cerebral palsy: does screening capture the complexity?Scientific World J. 2013;2013:468402. doi:10.1155/2013/468402.
10.
KimHCarlsonAGCurbyTWWinslerA. Relations among motor, social, and cognitive skills in pre-kindergarten children with developmental disabilities. Res Dev Disabil. 2016;53-54:43–60. doi:10.1016/j.ridd.2016.01.016.
11.
BertoncelliCMSollaFLoughenburyPRTsirikosAIBertoncelliDRampalV. Risk factors for developing scoliosis in cerebral palsy: a cross sectional descriptive study. J Child Neurol. 2017;32(7):657–662. doi:10.1177/0883073817701047.
12.
BertoncelliCMBertoncelliDElbaumL. Validation of a clinical prediction model for the development of neuromuscular scoliosis: a multinational study. Pediatr Neurol. 2018;79:14–20. doi:10.1016/j.pediatrneurol.2017.10.019.
13.
HastieTTibshiraniRFriedmanJ. The elements of statistical learning. Elements. 2009;1:337–387.
14.
OhESeoSWYoonYC. Prediction of pathologic femoral fractures in patients with lung cancer using machine learning algorithms: comparison of computed tomography-based radiological features with clinical features versus without clinical features. J Orthop Surg. 25(2):1–7. doi:10.1177/2309499017716243.
15.
MossottoEAshtonJJCoelhoTBeattieRMMacArthurDEnnisS. Classification of paediatric inflammatory bowel disease using machine learning. Sci Rep. 2017;7:2427. doi:10.1038/s41598-017-02606-2.
16.
GainsboroughMSurmanGMaestriGColverACansC. Validity and reliability of the guidelines of the Surveillance of Cerebral Palsy in Europe for the classification of cerebral palsy. Dev Med Child Neurol. 2008;50:828–831.
17.
MoonsKGAltmanDGReitsmaJBCollinsGS. New guideline for the reporting of studies developing, validating, or updating a multivariable clinical prediction model: the TRIPOD statement. Adv Anat Pathol. 2015;22:303–305. doi:10.1097/PAP.0000000000000072.
18.
HoldnackJAXiaobinZLarrabeeGMillisSSalthouseT. Confirmatory factor analysis of the WAIS-IV/WMS-IV. Assessment. 2011;18(2):178–191. doi:10.1177/1073191110393106.
19.
Committee to Evaluate the Supplemental Security Income Disability Program for Children with Mental Disorders, et al. Mental Disorders and Disabilities Among Low-Income Children. Washington, DC: National Academies Press; 2015. https://www.ncbi.nlm.nih.gov/books/NBK332877/.
20.
Delobel-AyoubMKlapouszczakDvan BakelMME. Prevalence and characteristics of autism spectrum disorders in children with cerebral palsy. Dev Med Child Neurol. 2017;59(7):738–742. doi:10.1111/dmcn.13436.
21.
GohYRChoiJYKimSAParkJParkES. Comparisons of severity classification systems for oropharyngeal dysfunction in children with cerebral palsy: relations with other functional profiles. Res Dev Disabil. 2018;72:248–256. doi:10.1016/j.ridd.2017.12.002.
22.
VerdugoMAAriasVBGuillénVM. Are type, frequency, and daily time equally valid estimators of support needs in children with intellectual disability? A multitrait-multimethod analysis of the Supports Intensity Scale for Children (SIS-C) [published online ahead of print October 30, 2017]. Assessment. doi:10.1177/1073191117732411.
23.
OhrvallAMKrumlinde-SundholmLEliassonAC. The stability of the Manual Ability Classification System over time. Dev Med Child Neurol. 2014;56:185–189.
24.
CompagnoneEManiglioJCamposeoS. Functional classifications for cerebral palsy: correlations between the Gross Motor Function Classification System (GMFCS), the Manual Ability Classification System (MACS) and the Communication Function Classification System (CFCS). Res Dev Disabil. 2014;35:2651–2657.
25.
HarebFRampalVBertoncelliCMSollaF. Botulinum toxin in children with cerebral palsy: an update. Clin Cases Miner Bone Metab. (in press).
SinhaSSiddiquiKA. Definition of intractable epilepsy. Neurosciences. 2011;16:3–9.
28.
BergAT. Identification of pharmacoresistant epilepsy. Neurol Clin. 2009;27:1003–1013.
29.
AgrestiA. Contingency Tables. An Introduction to Categorical Data Analysis. 2nd ed. Hoboken, NJ: Wiley; 2007.
30.
SollaFTranABertoncelliDMusoffCBertoncelliC. Why a p-value is not enough. Clin Spine Surg. 2018;31(9):385–388. doi:10.1097/BSD.0000000000000695.
31.
SullivanKAndrewDMinn MinnS. OpenEpi: a web-based epidemiologic and statistical calculator for public health. Public Health Rep. 2009;124:458–471.
32.
AltmanDGDeeksJJSackettDL. Odds ratios should be avoided when events are common. BMJ. 1998;317:1318.
33.
The R Core Team. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.
34.
VapnikV.The Nature of Statistical Learning Theory. New York, NY: Springer; 2013.
35.
WenZZengNWangN. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. Paper presented at: NESUG Proceedings: Health Care and Life Sciences; 2010; Baltimore, MD.
36.
RobertJT. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. Sausalito, California: University Science Books; 1999.
37.
ThombsBDRiceDB. Sample sizes and precision of estimates of sensitivity and specificity from primary studies on the diagnostic accuracy of depression screening tools: a survey of recently published studies. Int J Methods Psychiatr Res. 2016;25(2):145–152. doi:10.1002/mpr.1504.
38.
KimKKangJYJangDH. Relationship between mobility and self-care activity in children with cerebral palsy. Ann Rehabil Med. 2017;41(2):266–272. doi:10.5535/arm.2017.41.2.266.
39.
WagnerAPCroudaceTJBatemanN. Clinical services for adults with an intellectual disability and epilepsy: a comparison of management alternatives. PLOS ONE. 2017;12(7):e0180266. doi:10.1371/journal.pone.0180266.
40.
LiuWAnDTongXNiuRGongQZhouD. Region-specific connectivity in patients with periventricular nodular heterotopia and epilepsy: a study combining diffusion tensor imaging and functional MRI. Epilepsy Res. 2017;136:137–142. doi:10.1016/j.eplepsyres.2017.08.007.
41.
TakedaKMatsudaHMiyamotoYYamamotoH. Structural brain network analysis of children with localization-related epilepsy. Brain Dev. 2017;39(8):678–686. doi:10.1016/j.braindev.2017.04.010.
42.
StevaninGDurrABriceA. Spastic paraplegia 11. In: AdamMPArdingerHHPagonRAWallaceSEBeanLJHStephensKAmemiyaA, eds. GeneReviews. Seattle: University of Washington; 1993–2018.
43.
PicelliATamburinSGajofattoF. Association between severe upper limb spasticity and brain lesion location in stroke patients. Biomed Res Int. 2014;2014:162754. doi:10.1155/2014/162754.
44.
Levy-ZaksAPollakYBen-PaziH. Cerebral palsy risk factors and their impact on psychopathology. Neurol Res. 2014;36(1):92–94. doi:10.1179/1743132813Y.0000000290.
45.
GorterJWKetelaarMRosenbaumPHeldersPJPalisanoR. Use of the GMFCS in infants with CP: the need for reclassification at age 2 years or older. Dev Med Child Neurol. 2009;51(1):46–52. doi:10.1111/j.1469-8749.2008.03117.x.
