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Development of a nomogram risk prediction model for subtherapeutic valproic acid plasma concentration: A
Jing Ding1, Jiarui Liu1, Xiaohua Cui1
1Xi'an Mental Health Center, Xi'an, Shaanxi, China.
Objective:
Limited research has explored the factors contributing to subtherapeutic concentrations (< 50 μg/mL) of Valproic acid (VPA) in patients with mental disorders. To address this gap, the present study aims to identify key factors associated with subtherapeutic VPA levels and to develop a visual nomogram prediction model.
Methods:
This retrospective single-center study collected the therapeutic drug monitoring (TDM) results of patients treated with valproate at the Xi'an Mental Health Center from January to June 2024. Single-factor analysis was performed to identify candidate variables for multivariate logistic regression, which was then used to determine the final risk factors.
Results:
Among 686 patients receiving VPA therapy, 138 (20.12 %) had plasma concentrations below the therapeutic threshold (<50 μg/mL). Multivariate logistic regression identified age (OR = 0.449, 95 % CI: 0.259-0.780), body mass index (OR = 3.266, 95 % CI: 1.907-5.592), and daily dose (OR = 0.006, 95 % CI: 0.002-0.020) as independent predictors of subtherapeutic VPA levels (p < 0.01). The nomogram model demonstrated strong predictive performance with an area under the curve (AUC) of 0.766 through 10-fold cross-validation. The calibration curve confirmed a concordance index (C-index) of 0.788 using 1000 bootstrap resamples, and the Hosmer-Lemeshow test showed good model fit (χ2 = 4.778, p = 0.311). Decision curve analysis (DCA) indicated favorable clinical utility, with a wide range of threshold probabilities (13 %-78 %).
Conclusions:
A visual nomogram model for predicting VPA concentrations was developed for Chinese patients with mental disorders. This tool offers a personalized approach to optimizing VPA dosing in clinical psychiatric practice.
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