AIM：To develop and validate a predictive model for the risk of high plasma concentration of voriconazole, and to guide clinical individualized medication of voriconazole. METHODS: Based on the real-world data from the hospital Information system (HIS), the clinical data of hospitalized patients who received voriconazole treatment and underwent voriconazole plasma concentration monitoring in our hospital from August 2017 to August 2021 were collected. Univariate and multivariate logistic regression analysis were performed on the included influencing factors. At the same time, in order to minimize the potential collinearity and overfitting between variables, the least absolute shrinkage and selection operator regression were used to screen the potential predictors. Logistic regression analysis was used to construct a prediction model for the risk of high plasma concentration of voriconazole. C-index, calibration chart and clinical decision curve analysis were used to evaluate the discrimination, consistency and clinical applicability of the model, and a nomogram was drawn. RESULTS: A total of 147 patients were enrolled in this study. Plasma albumin and procalcitonin were selected as predictive variables for Logistic regression analysis, and the prediction model was established. Draw predict voriconazole nomogram risk blood drug concentration on the high side. The receiver operating characteristic curve showed that the AUC of the prediction model for predicting the risk of high plasma concentration of voriconazole was 0.787 (95%CI 0.663-0.911). Voriconazole blood drug concentration was high incidence of cut-off value was 33.06%, sensitivity was 63.64%, 87.65% and 58.33% positive predictive value, negative predictive value of 89.87%. The calibration curve showed good consistency, and the clinical decision curve showed that the model had a positive net benefit when the threshold probability was between 6.67% and 99.99%. CONCLUSION:The predictive model for the risk of high plasma concentration of voriconazole has good predictive efficacy, which can provide guidance for clinical individualized medication of voriconazole.