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使用预测性学习模型识别脑瘫青少年中与严重智力残疾相关的因素。

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

作者信息

Bertoncelli Carlo M, Altamura Paola, Vieira Edgar Ramos, Bertoncelli Domenico, Thummler Susanne, Solla Federico

机构信息

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

EEAP H. Germain Fondation Lenval-Children's Hospital, Nice, France.

出版信息

J Child Neurol. 2019 Mar;34(4):221-229. doi: 10.1177/0883073818822358. Epub 2019 Jan 22.

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 after 2013 and 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 ( ≤ .001), gross motor function ( ≤ .001), and type of epilepsy (intractable: = .04; well controlled: = .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.

摘要

背景

智力残疾和适应性功能受损在脑瘫儿童中很常见,但缺乏针对脑瘫青少年这些问题的研究。因此,本研究的目的是开发并测试一种预测性机器学习模型,以识别与脑瘫青少年智力残疾相关的因素。

方法

这是一项多中心对照队列研究,研究对象为91名脑瘫青少年(53名男性,38名女性;平均年龄±标准差=17±1岁;范围:12 - 18岁)。在2005年至2015年期间收集了病因、诊断、痉挛、癫痫、临床病史、沟通能力、行为、运动技能、饮食能力等数据。根据适应性功能,智力残疾被分为“轻度”“中度”“重度”或“极重度”,2013年以后及2013年以前,16岁11个月及以下患者使用韦氏儿童智力量表,17 - 18岁患者使用韦氏成人智力量表。统计分析包括费舍尔精确检验和多元逻辑回归,以识别与智力残疾相关的因素。开发了一种预测性机器学习模型,以识别与极重度智力残疾相关的因素。遵循了“个体预后或诊断多变量预测模型的透明报告声明”的指南。

结果

手部能力差(≤.001)、粗大运动功能(≤.001)和癫痫类型(难治性:=.04;控制良好:=.01)与极重度智力残疾显著相关。模型的平均准确率、特异性和敏感性为78%。

结论

运动技能差和癫痫与极重度智力残疾相关。机器学习预测模型能够充分识别脑瘫青少年严重智力残疾的高可能性。

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