Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease

Background: Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD. Methods: We studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation (r) between observed and inferred scores, mean absolute error (MAE), and inverse inferability score (IIS). Results: Here we show that models infer scores with moderate accuracy (r = 0.245–0.648; MAE = 1.6–12.0 points; mean MAE = 6.3). Highest correlations include Digit Span (r = 0.65; p < 0.001), Verbal Comprehension Index (r = 0.594; p < 0.001), and Matrix Reasoning (r = 0.574; p < 0.001). Domain ranking by IIS shows the best (lowest) scores for general intelligence (0.0886), followed by working memory (0.7100), and a higher (worse) score for perceptual reasoning (1.9199). Conclusions: A multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.