Establishment and Evaluation of A Risk Prediction Model for Voriconazole Plasma Concentration Exceeding the Reference Therapeutic Range

OBJECTIVE  To study the risk factors of voriconazole concentration exceeding the upper limit of the reference therapeutic range, and establish a risk prediction model.

METHODS  Hospitalized patients who used voriconazole for invasive fungal infection and monitored the drug concentration were selected in The First People's Hospital of Changzhou from January 2019 to June 2023. Patients whose concentration was within 0.5–5.0 mg·L⁻¹ were classified as the compliance group, and whose voriconazole concentration higher than 5.0 mg·L⁻¹ were classified as the over-standard group, clinical data were collected, including age, gender, body mass index, underlying diseases, smoking and drinking history, voriconazole medication, voriconazole concentration, combined medication, blood routine, liver and kidney function, etc. Univariate analysis and binary logistic regression analysis were used to study the risk factors of voriconazole blood concentration exceeding the upper limit of the reference treatment range, and constructed a nomogram prediction model for evaluation.

RESULTS  The results of binary logistic regression analysis showed that age(OR=3.458, P=0.001), gender(OR=0.398, P=0.032), dose(OR=1.560, P=0.002), albumin(OR=0.352, P=0.007) and C-reactive protein(OR=2.299, P=0.023 and OR=4.898, P=0.002) were independent influencing factors of whether the plasma concentration of voriconazole exceeded the limit. Based on this, the area under the ROC curve of the nomogram model was 0.802(95%CI was 0.744–0.859). And the Bootstrap self-sampling method was used to repeat sampling for 1 000 times to verify the internal verification of the model, and the average absolute error was 0.021, the results showed the good consistency between the predicted value and the observed value. When the risk threshold was in the range of about 10% to 92%, using a nomogram model to predict the probability of voriconazole concentration exceeding the upper limit of the reference treatment range could yield a net clinical benefit.

CONCLUSION Based on binary logistic regression, five factors of excessive voriconazole concentration are screened out, namely age, gender, dose, albumin and C-reactive protein, and establish a nomogram prediction model, it can help physicians identify the risk groups with excessive voriconazole concentration quickly.