A risk prediction model for autism spectrum disorder integrating biopsychosocial factors: a systematic review and...

Early identification of individuals at high risk for autism spectrum disorder (ASD) is crucial for optimizing intervention strategies and improving outcomes. This study aims to develop a risk prediction model integrating biopsychosocial factors through a systematic review with multicenter validation.

A comprehensive search was conducted across PubMed, Cochrane Library, and Embase for articles on biopsychosocial ASD risk factors during 2010-2023. Two reviewers independently extracted data. Meta-regression analysis of 37 systematic reviews/meta-analyses identified 18 potential risk factors by Stata 16.0. Four core variables were included in the prediction model, while 14 were excluded due to low-quality evidence or insufficient data after screening. Multivariate logistic regression with least absolute shrinkage and selection operator (LASSO) variable selection derived model weights. External validation was performed in a Chinese cohort (n=1,175) from two tertiary hospitals. Model discrimination was assessed via receiver operating characteristic (ROC) curves and clinical utility by decision curve analysis (DCA).

Analysis of 37 systematic reviews identified four independent predictors of ASD risk: adverse childhood experiences (ACEs) [odds ratio (OR) =2.11; 95% confidence interval (CI): 1.61-2.77], preterm birth (OR =3.3; 95% CI: 1.24-7.60), antidepressant exposure during pregnancy (OR =1.17; 95% CI: 1.08-1.21), and perinatal antibiotic exposure (OR =1.52; 95% CI: 1.09-2.12). The risk model formula was: 0.82 × (ACEs) + 1.19 × (preterm birth) + 0.42 × (antidepressant exposure) + 0.21 × (perinatal antibiotic exposure). External validation showed excellent discrimination [area under the curve (AUC) =0.78; 95% CI: 0.75-0.81]. DCA confirmed significantly higher net clinical benefit compared to universal intervention strategies.

This study developed a risk prediction model integrating biopsychosocial factors, providing an evidence-based tool for early identification of individuals at high risk for ASD.