Development and validation of a risk score model for predicting autism based on pre- and perinatal factors
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a clinical risk score model using pre- and perinatal factors to predict autism earlier. The model shows good discrimination for autism prediction, aiding early intervention.
Area Of Science
- Developmental Pediatrics
- Genetics and Genomics
- Clinical Psychology
Background
- Early identification of autism spectrum disorder (ASD) is crucial for timely intervention.
- Existing predictive methods often have limitations in lowering the age of reliable prediction.
- Pre- and perinatal risk factors offer potential for earlier autism prediction.
Purpose Of The Study
- To develop and validate a clinical model utilizing pre- and perinatal risk factors for autism prediction.
- To establish a clinical risk score for identifying children at higher risk of autism.
- To assess the model's predictive performance and clinical applicability.
Main Methods
- Stepwise logistic regression analysis was employed to identify significant risk factors from 28 candidates in 615 Han Chinese children with autism and 615 controls.
- A clinical risk score model was constructed using identified significant factors.
- Validation was performed using a chi-square automatic interaction detector (CHAID) decision tree and an independent cohort.
Main Results
- Five factors—pregnancy influenza-like illness, pregnancy stressors, maternal allergic/autoimmune disease, cesarean section, and hypoxia—were significantly associated with autism risk.
- The risk score model demonstrated good discrimination ability for autism, with an Area Under the Curve (AUC) of 0.711.
- External validation showed similar predictive performance, with higher scores correlating with increased behavioral problems and robustness in mild autism cases.
Conclusions
- The developed clinical risk score model can potentially lower the age threshold for reliable autism prediction.
- The model exhibits good discrimination performance and practical advantages for clinical use.
- This tool supports earlier identification and intervention for autism spectrum disorder.
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