Analyze the Influencing Factors and Predictability of Metabolic Syndrome in Schizophrenic Patients Treated with Olanzapine through Decision Tree Model - Anh, Kieu Mai; Thanh Tuyen, Nguyen; Huu Chien, Nguyen; Xuan Bach, Nguyen; Thu Huong, Pham; Chi Thanh, Nguyen; Thanh Hai, Nguyen (VNU Journal of Science: Medical and Pharmaceutical Sciences, 2022)
Abstract: Olanzapine is a typical antipsychotic that has demonstrated efficacy for the treatment of schizophrenia, but the patients treated with olanzapine usually appear it’s specific adverse events such as metabolism. It shows some factors that may affect metabolism such as age, BMI, high-dose antipsychotics, etc. Up to now the main predictors for metabolism in a patient with schizophrenia have not been comprehensively evaluated. Subjects and research methods: In this prospective cohort study, a total of 202 inpatients with schizophrenia at Vietnamese National Psychiatric Hospital No1 were included. The univariate regression and decision tree model were applied to find out the statistically significant factors. Results: The factors influencing metabolism were: baseline waist, baseline triglyceride, baseline HDL, age, baseline BMI, baseline metabolism, cholesterol ≥6.2 history, and schizophrenia duration. The final decision tree model included 3 important nodes: baseline waist < 89 cm, baseline triglyceride <3.1 mmol/l, age <36. The predictive accuracy and other parameters were good to be able to apply for predictive purposes: accuracy 0.88, precision 0.90, recall 0.69, f1-score 0.78. Conclusion: The final model was good to predict no metabolism (0 code). In contrast, it is necessary to verify the metabolism status and have appropriate routine monitoring. Keywords: Metabolism, schizophrenia, olanzapine, predictive model, decision tree model.