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

被引:18
作者
Bertoncelli, Carlo M. [1 ,2 ]
Altamura, Paola [3 ]
Vieira, Edgar Ramos [4 ]
Bertoncelli, Domenico [5 ]
Thummler, Susanne [6 ]
Solla, Federico [1 ]
机构
[1] Lenval Univ Pediat Hosp Nice, Dept Pediat Orthopaed Surg, Nice, France
[2] EEAP H Germain Fondat Lenval Childrens Hosp, Nice, France
[3] Univ G dAnnunzio, Dept Med Chem & Pharmaceut Technol, Chieti, Italy
[4] Florida Int Univ, Dept Phys Therapy, Miami, FL 33199 USA
[5] Univ Aquila, Dept Informat Engn Comp Sci & Math, Laquila, Italy
[6] Childrens Hosp Nice CHU Lenval, Child & Adolescent Psychiat, F-06034 Nice, France
关键词
prediction model; cerebral palsy; intellectual disability; statistics; machine learning; ABILITY CLASSIFICATION-SYSTEM; RISK-FACTORS; CHILDREN; AGE; DEFINITION; DISORDERS; SCOLIOSIS; GMFCS; MOTOR;
D O I
10.1177/0883073818822358
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
引用
收藏
页码:221 / 229
页数:9
相关论文
共 45 条
[21]   Validity and reliability of the guidelines of the Surveillance of Cerebral Palsy in Europe for the classification of cerebral palsy [J].
Gainsborough, Mary ;
Surman, Geraldine ;
Maestri, Giovanna ;
Colver, Allan ;
Cans, Christine .
DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY, 2008, 50 (11) :828-831
[22]   Comparisons of severity classification systems for oropharyngeal dysfunction in children with cerebral palsy: Relations with other functional profiles [J].
Goh, Yu-ra ;
Choi, Ja Young ;
Kim, Seon Ah ;
Park, Jieun ;
Park, Eun Sook .
RESEARCH IN DEVELOPMENTAL DISABILITIES, 2018, 72 :248-256
[23]   Use of the GMFCS in infants with CP: the need for reclassification at age 2 years or older [J].
Gorter, Jan Willem ;
Ketelaar, Marjolijn ;
Rosenbaum, Peter ;
Helders, Paul J. M. ;
Palisano, Robert .
DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY, 2009, 51 (01) :46-52
[24]   Age and Adaptive Functioning in Children and Adolescents with ASD: The Effects of Intellectual Functioning and ASD Symptom Severity [J].
Hill, Trenesha L. ;
Gray, Sarah A. O. ;
Kamps, Jodi L. ;
Varela, R. Enrique .
JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS, 2015, 45 (12) :4074-4083
[25]   Confirmatory Factor Analysis of the WAIS-IV/WMS-IV [J].
Holdnack, James A. ;
Zhou, Xiaobin ;
Larrabee, Glenn J. ;
Millis, Scott R. ;
Salthouse, Timothy A. .
ASSESSMENT, 2011, 18 (02) :178-191
[26]   Relations among motor, social, and cognitive skills in pre-kindergarten children with developmental disabilities [J].
Kim, Helyn ;
Carlson, Abby G. ;
Curby, Timothy W. ;
Winsler, Adam .
RESEARCH IN DEVELOPMENTAL DISABILITIES, 2016, 53-54 :43-60
[27]  
Kim K, 2017, ANN REHABIL MED-ARM, V41, P266, DOI 10.5535/arm.2017.41.2.266
[28]   Cerebral palsy risk factors and their impact on psychopathology [J].
Levy-Zaks, Anat ;
Pollak, Yehuda ;
Ben-Pazi, Hilla .
NEUROLOGICAL RESEARCH, 2014, 36 (01) :92-94
[29]   Region-specific connectivity in patients with periventricular nodular heterotopia and epilepsy: A study combining diffusion tensor imaging and functional MRI [J].
Liv, Wenyu ;
An, Dongmei ;
Tong, Xin ;
Niu, Running ;
Gong, Qiyong ;
Zhou, Dong .
EPILEPSY RESEARCH, 2017, 136 :137-142
[30]   New Guideline for the Reporting of Studies Developing, Validating, or Updating a Multivariable Clinical Prediction Model: The TRIPOD Statement [J].
Moons, Karel G. M. ;
Altman, Douglas G. ;
Reitsma, Johannes B. ;
Collins, Gary S. .
ADVANCES IN ANATOMIC PATHOLOGY, 2015, 22 (05) :303-305