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Identifying patients at risk of supratherapeutic amisulpride exposure in routine psychiatric practice: a real-world therapeutic drug monitoring study
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DOI：10.1177/20451253261460142.png)
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Abstract
En 中文
<jats:sec>
<jats:title>Background:</jats:title>
<jats:p>Amisulpride is an important antipsychotic used in the treatment of schizophrenia, yet its exposure shows inter-individual variability. Although therapeutic drug monitoring (TDM) is recommended, supratherapeutic concentrations (>600 ng/mL) still occur in routine practice and may increase the risk of adverse effects. However, practical risk prediction studies of supratherapeutic amisulpride exposure based on routine clinical variables remain limited.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Objectives:</jats:title>
<jats:p>To identify patients at increased risk of supratherapeutic amisulpride exposure in routine psychiatric care and to develop a clinically interpretable risk prediction approach based on routinely available clinical variables.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Design:</jats:title>
<jats:p>This was a retrospective observational study using real-world TDM data from psychiatric inpatients.</jats:p>
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<jats:title>Methods:</jats:title>
<jats:p>Therapeutic drug monitoring data were retrospectively analyzed. Patients were split at the patient level into training (70%) and validation (30%) cohorts. To identify the optimal representation of renal function and drug elimination, predictors were organized into three candidate pools: (i) apparent clearance (CL/F) calculated from the final covariate equation of a previously published population pharmacokinetic model, (ii) estimated creatinine clearance (eCLcr), and (iii) the original clinical variables used to calculate eCLcr. Within each candidate predictor pool, predictors were screened using LASSO-penalized logistic regression and then entered into multivariable logistic regression. The most clinically practical model was selected for nomogram construction. Model performance was assessed by discrimination, calibration, and decision curve analysis.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results:</jats:title>
<jats:p>A total of 413 steady-state trough samples from 299 patients were analyzed, of which 19.6% exceeded a steady-state trough plasma concentration of 600 ng/mL. Reduced renal function and higher daily doses were consistently associated with an increased risk of supratherapeutic exposure. Among the three candidate strategies, the Key Covariate-based strategy achieved acceptable discrimination in both the training (area under the receiver operating characteristic curve (AUC) 0.738) and validation (AUC 0.720) cohorts, with good calibration. Decision curve analysis suggested potential clinical utility across a range of lower to moderate threshold probabilities.</jats:p>
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<jats:sec>
<jats:title>Conclusion:</jats:title>
<jats:p>A simple risk stratification approach based on eCLcr and daily dose may offer a reference for individualized risk assessment. This model may provide preliminary support for identifying patients at risk of supratherapeutic exposure in routine psychiatric practice.</jats:p>
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