Latent class analysis of symptoms across schizophrenia, schizoaffective disorder, and bipolar I disorder.

ABSTRACT: Data-driven phenotypes have the potential to accelerate biomedical research but must be vetted thoroughly for robustness, interpretability, and generalizability. This study sought to create and evaluate the predictive validity of empirical phenotypes derived from assessments of signs and symptoms of psychopathology collected in a large cohort of patients using a validated semi-structured clinical interview based on the OPCRIT system. Data was available for N = 17,719 individuals diagnosed with schizophrenia (n = 10,429; 30% female), bipolar I disorder (n = 4,520; 53% female), or schizoaffective disorder (n = 2,770; 44% female) using DSM-IV-TR criteria. The best-fitting latent class analysis (LCA) model was a 6-class solution. Three of the six class profiles replicated results from previous

SUBMITTER: Lapato DM 

PROVIDER: S-EPMC12636642 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature