Dataset Information

Establishment of a mathematical prediction model for voriconazole stable maintenance dose: a prospective study.

ABSTRACT:

Background

In patients with invasive fungal infection (IFI), the steady-state serum trough concentration (C _min) of voriconazole (VCZ) is highly variable and can lead to treatment failure (C _min < 0.5 mg/L) and toxicity (C _min ≥ 5.0 mg/L). However, It remains challenging to determine the ideal maintenance dose to achieve the desired C _min level quickly.

Aims

This randomized, prospective observational single-center study aimed to identify factors affecting VCZ-C _min and maintenance dose and create an algorithmic model to predict the necessary maintenance dose. MeThe study enrolled 306 adult IFI patients, split into two groups: non-gene-directed (A) (where CYP2C19 phenotype is not involved in determining VCZ dose) and gene-directed (B) (where CYP2C19 phenotype is involved in determining VCZ dose).

Results

Results indicated that CYP2C19 genetic polymorphisms might significantly impact VCZ loading and maintenance dose selection. CYP2C19 phenotype, C-reaction protein (CRP), and average daily dose/body weight were significant influencers on VCZ-C _min, while CYP2C19 phenotype, CRP, and body weight significantly impacted VCZ maintenance dose. A feasible predictive formula for VCZ stable maintenance dose was derived from the regression equation as a maintenance dose (mg) =282.774-0.735×age (year)+2.946×body weight(Kg)-19.402×CYP2C19 phenotype (UM/RM/NM:0, IM:1, PM:2)-0.316×CRP (mg/L) (p < 0.001).

Discussion

DiThis formula may serve as a valuable supplement to the Clinical Pharmacogenetics Implementation Consortium (CPIC^®) guideline for CYP2C19 and VCZ therapy, especially for IFI patients with highly variable inflammatory cytokines during VCZ therapy.

SUBMITTER: Zhou L

PROVIDER: S-EPMC10410275 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Establishment of a mathematical prediction model for voriconazole stable maintenance dose: a prospective study.

Zhou Lijuan L Li Min M Li Huihong H Guo Zhiqiang Z Gao Yanqiu Y Zhang Hua H Qin Fuli F Sang Zhihui Z Xing Qinghe Q Cheng Long L Cao Wei W

Frontiers in cellular and infection microbiology 20230726

<h4>Background</h4>In patients with invasive fungal infection (IFI), the steady-state serum trough concentration (<i>C</i> <sub>min</sub>) of voriconazole (VCZ) is highly variable and can lead to treatment failure (<i>C</i> <sub>min</sub> < 0.5 mg/L) and toxicity (<i>C</i> <sub>min</sub> ≥ 5.0 mg/L). However, It remains challenging to determine the ideal maintenance dose to achieve the desired <i>C</i> <sub>min</sub> level quickly.<h4>Aims</h4>This randomized, prospective observational single-ce ...[more]

PMID: 37565064

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Project description:BackgroundChronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. There is no nomogram model available for mortality prediction of stable COPD. We intended to develop and validate a nomogram model to predict mortality risk in stable COPD patients for personalised prognostic assessment.MethodsA prospective observational study was made of COPD outpatients registered in the RealDTC study between December 2016 and December 2019. Patients were randomly assigned to the training cohort and validation cohort in a ratio of 7:3. We used Lasso regression to screen predicted variables. Further, we evaluated the prognostic performance using the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. We used the AUC, concordance index, and decision curve analysis to evaluate the net benefits and utility of the nomogram compared with three earlier prediction models.ResultsOf 2499 patients, the median follow-up was 38 months. The characteristics of the patients between the training cohort (n = 1743) and the validation cohort (n = 756) were similar. ABEODS nomogram model, combining age, body mass index, educational level, airflow obstruction, dyspnoea, and severe exacerbation in the first year, was constructed to predict mortality in stable COPD patients. In the integrative analysis of training and validation cohorts of the nomogram model, the three-year mortality prediction achieved AUC = 0.84; 95% confidence interval (CI) = 0.81, 0.88 and AUC = 0.80; 95% CI = 0.74, 0.86, respectively. The ABEODS nomogram model preserved excellent calibration in both the training cohort and validation cohort. The time-dependent AUC, concordance index, and net benefit of the nomogram model were higher than those of BODEx, updated ADO, and DOSE, respectively.ConclusionsWe developed and validated a prognostic nomogram model that accurately predicts mortality across the COPD severity spectrum. The proposed ABEODS nomogram model performed better than earlier models, including BODEx, updated ADO, and DOSE in Chinese patients with COPD.RegistrationChiCTR-POC-17010431.

Dataset Information

Establishment of a mathematical prediction model for voriconazole stable maintenance dose: a prospective study.

Background

Aims

Results

Discussion

Publications

Establishment of a mathematical prediction model for voriconazole stable maintenance dose: a prospective study.

Similar Datasets

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