Abstract
Background: Clozapine and olanzapine are associated with clinically relevant weight gain and metabolic risk.
Objective: To compare the risk of obesity (BMI ⩾ 30) between clozapine and olanzapine and to develop a parsimonious prediction model with performance and clinical-utility evaluation.
Methods: We fit a multivariable logistic regression on complete cases using index drug, daily dose, prolactin (PRL), cortisol, free thyroxine (FT4), and HDL. Discrimination (AUC), probability accuracy (Brier score), calibration (intercept/slope and deciles), and decision-curve analysis (DCA) were reported.
Results: In the classification cohort, obesity counts were equal for clozapine (9/19) and olanzapine (9/19). The model achieved strong internal discrimination (AUC 0.869) with good calibration (slope 1.00; intercept −0.00) and Brier 0.123. DCA indicated net benefit at practical thresholds (∼5–15%), supporting low-harm preventive actions.
Conclusions: Drug-related risk can be individualized with a lean model using routine variables. External validation and unit harmonization—especially for HDL—are recommended before broad deployment. Psychopharmacology Bulletin. 2026;56(1):71–81.
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