Structure-Informed Cognitive Representation Improves Prediction of Real-World Functioning in Schizophrenia: A Comparison with Conventional Domain Scores

Predicting real-world functional outcomes in schizophrenia (SCZ) remains a clinical priority, but existing models are limited by methodological constraints and a lack of established clinical utility. Cognition is a commonly used predictor, and the Normative Latent Cognitive Structure (N-LCS) approach provides a structure-informed representation that may address limitations of conventional domain-level scores. Data from two merged COBRE cohorts (163 SCZ, 180 healthy controls) were used to develop ridge regression models for economic (EF), occupational (OF), and social (SF) functioning, using N-LCS deviation metrics alongside a priori selected demographic and clinical predictors. Score-based models using MCCB domain T-scores were developed for comparison. Performance was evaluated using bootstrap-corrected AUC, balanced accuracy, and calibration for binary outcomes, and weighted kappa and log-loss for SF. Decision curve analysis (DCA) was used to assess clinical utility for the binary outcomes. The EF model achieved a corrected AUC of 0.76 and balanced accuracy of 0.73. The OF model achieved 0.72 and 0.71, respectively. The SF model showed modest performance (weighted kappa = 0.33). DCA indicated net benefit across the full threshold range for EF and above 0.37 for OF. N-LCS models demonstrated comparable or modestly superior performance to score-based models while using fewer predictors and showing better calibration for EF. These findings support the predictive utility of N-LCS for functional outcomes in SCZ and underscore the need for external validation in independent cohorts as a next step toward clinical application.