Combinatorial classification model for predicting antipsychotic-induced hyperprolactinemia risk.

OBJECTIVES: To develop a risk prediction model for antipsychotic-induced hyperprolactinemia in female patients with schizophrenia. METHODS: A total of 200 female patients with first-episode schizophrenia who underwent antipsychotic monotherapy at Huzhou Third Municipal Hospital from February 2022 to December 2023 were enrolled in this study. Venous blood samples were collected before treatment to measure thyroid function, cortisol, and sex hormones. Based on the levels of prolactin (PRL) and macroprolactin (MPRL) after four weeks of treatment, patients were divided into two groups: the hyperprolactinemia group (n = 92) and the macroprolactinemia group (n = 108). Baseline clinical data were compared between groups, and a risk prediction model was constructed and evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). External validation was performed in an independent cohort of 57 patients with schizophrenia admitted between January and July 2024. RESULTS: Univariate and multivariate analyses identified body mass index (BMI), free thyroxine (FT4), thyroid-stimulating hormone (TSH), and cortisol as significant predictors of antipsychotic-induced hyperprolactinemia (P < 0.05). The ROC curve showed an area under the curve (AUC) of 0.838 for the modeling cohort, with specificity and sensitivity of 0.896 and 0.635, respectively. Internal and external verification yielded AUCs of 0.852 and 0.689, with specificities/sensitivities of 0.903/0.759 and 0.677/0.615, respectively. The calibration curve demonstrated good alignment with the ideal curve, and DCA indicated the model provided superior net clinical benefit. CONCLUSIONS: The proposed model accurately predicts the risk of antipsychotic-induced hyperprolactinemia in female patients with schizophrenia, offering a valuable tool for early clinical risk assessment.