Development and validation of a risk score model for predicting autism based on pre- and perinatal factors.

ABSTRACT:

Background

The use of pre- and perinatal risk factors as predictive factors may lower the age limit for reliable autism prediction. The objective of this study was to develop a clinical model based on these risk factors to predict autism.

Methods

A stepwise logistic regression analysis was conducted to explore the relationships between 28 candidate risk factors and autism risk among 615 Han Chinese children with autism and 615 unrelated typically developing children. The significant factors were subsequently used to create a clinical risk score model. A chi-square automatic interaction detector (CHAID) decision tree was used to validate the selected predictors included in the model. The predictive performance of the model was evaluated by an independent cohort.

Results

SUBMITTER: Ou J 

PROVIDER: S-EPMC10904522 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature