Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA.
Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA.
Autism Res. 2022 Jan;15(1):156-170. doi: 10.1002/aur.2622. Epub 2021 Oct 11.
Diverse large cohorts are necessary for dissecting subtypes of autism, and intellectual disability is one of the most robust endophenotypes for analysis. However, current cognitive assessment methods are not feasible at scale. We developed five commonly used machine learning models to predict cognitive impairment (FSIQ<80 and FSIQ<70) and FSIQ scores among 521 children with autism using parent-reported online surveys in SPARK, and evaluated them in an independent set (n = 1346) with a missing data rate up to 70%. We assessed accuracy, sensitivity, and specificity by comparing predicted cognitive levels against clinical IQ data. The elastic-net model has good performance (AUC = 0.876, sensitivity = 0.772, specificity = 0.803) using 129 predictive features to impute cognitive impairment (FSIQ<80). Top-ranked predictive features included parent-reported language and cognitive levels, age at autism diagnosis, and history of services. Prediction of FSIQ<70 and FSIQ scores also showed good performance. We show cognitive levels can be imputed with high accuracy for children with autism, using commonly collected parent-reported data and standardized surveys. The current model offers a method for large-scale autism studies seeking estimates of cognitive ability when standardized psychometric testing is not feasible. LAY SUMMARY: Children with autism who have more severe learning challenges or cognitive impairment have different needs that are important to consider in research studies. When children in our study were missing standardized cognitive testing scores, we were able to use machine learning with other information to correctly "guess" when they have cognitive impairment about 80% of the time. We can use this information in research in the future to develop more appropriate treatments for children with autism and cognitive impairment.
为了剖析自闭症亚型，需要有多样化的大样本群体，而智力障碍是最稳健的分析内表型之一。然而，目前的认知评估方法在规模上不可行。我们开发了五种常用的机器学习模型，使用 SPARK 中的家长在线调查来预测 521 名自闭症儿童的认知障碍（FSIQ<80 和 FSIQ<70）和 FSIQ 评分，并在具有高达 70%缺失数据率的独立样本（n=1346）中对其进行评估。我们通过将预测的认知水平与临床智商数据进行比较来评估准确性、敏感性和特异性。弹性网络模型使用 129 个预测特征来进行认知障碍（FSIQ<80）的推断，具有良好的性能（AUC=0.876，敏感性=0.772，特异性=0.803）。排名靠前的预测特征包括家长报告的语言和认知水平、自闭症诊断年龄以及服务史。FSIQ<70 和 FSIQ 评分的预测也表现出良好的性能。我们表明，使用常见的家长报告数据和标准化调查，可以高度准确地推断自闭症儿童的认知水平。目前的模型为寻求在标准化心理测量测试不可行时估算认知能力的大规模自闭症研究提供了一种方法。
