Implementation and validation of a Bayesian method for accurately forecasting duration of optimal pharmacodynamic target attainment with dalbavancin during long-term use for subacute and chronic staphylococcal infections
Language English Country Netherlands Media print-electronic
Document type Journal Article
PubMed
37981075
DOI
10.1016/j.ijantimicag.2023.107038
PII: S0924-8579(23)00327-8
Knihovny.cz E-resources
- Keywords
- Bayesian prediction, MwPharm, TDM, dalbavancin,
- MeSH
- Anti-Bacterial Agents * therapeutic use pharmacology MeSH
- Bayes Theorem MeSH
- Humans MeSH
- Microbial Sensitivity Tests MeSH
- Staphylococcal Infections * drug therapy MeSH
- Staphylococcus MeSH
- Teicoplanin therapeutic use pharmacology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Anti-Bacterial Agents * MeSH
- dalbavancin MeSH Browser
- Teicoplanin MeSH
Dalbavancin is increasingly being used for long-term treatment of subacute and chronic staphylococcal infections. In this study, a new Bayesian model was implemented and validated using MwPharm software for accurately forecasting the duration of pharmacodynamic target attainment above the efficacy thresholds of 4.02 mg/L or 8.04 mg/L against staphylococci. Forecasting accuracy improved substantially with the a posteriori approach compared with the a priori approach, particularly when two measured concentrations were used. This strategy may help clinicians to estimate the duration of optimal exposure with dalbavancin in the context of long-term treatment.
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