Optimizing Vancomycin Dosing in Chronic Kidney Disease by Deriving and Implementing a Web-Based Tool Using a Population Pharmacokinetics Analysis Article Swipe
Sreemanee Raaj Dorajoo
,
Chrystal Leandra Winata
,
Jessica Hui Fen Goh
,
Say Tat Ooi
,
Jyoti Somani
,
Lee Ying Yeoh
,
Siok Ying Lee
,
Chun Wei Yap
,
Alexandre Chan
,
Jung‐woo Chae
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2019.00641
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2019.00641
Background: Chronic kidney disease (CKD) patients requiring intravenous vancomycin bear considerable risks of adverse outcomes both from the infection and vancomycin therapy itself, necessitating especially precise dosing to avoid sub- and supratherapeutic vancomycin exposure. Methods: In this retrospective study, we performed a population pharmacokinetic analysis to construct a vancomycin dose prediction model for CKD patients who do not require renal replacement therapy. The model was externally validated on an independent cohort of patients to assess its prediction accuracy. The pharmacokinetic parameter estimates and the equations were productized into a Web application (VancApp) subsequently implemented in routine care. The association between VancApp-based dosing and time-to-target concentration attainment, 30-day mortality, and nephrotoxicity were assessed postimplementation. Results: The model constructed from an initial cohort (n = 80) revealed a population clearance and volume of distribution of 1.30 L/h and 1.23 L/kg, respectively. External model validation (n = 112) demonstrated a mean absolute prediction error of 1.25 mg/L. Following 4 months of clinical implementation of VancApp as an optional alternative to usual care [VancApp (n = 22) vs. usual care (n = 21)], patients who had received VancApp-based dosing took a shorter time to reach target concentrations (median: 66 vs. 102 h, p = 0.187) and had fewer 30-day mortalities (14% vs. 24%, p = 0.457) compared to usual care. While statistical significance was not achieved, the clinical significance of these findings appear promising. Conclusion: Clinical implementation of a population pharmacokinetic model for vancomycin in CKD can potentially improve dosing precision in CKD and could serve as a practical means to improve vital clinical outcomes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphar.2019.00641
- OA Status
- gold
- Cited By
- 8
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2951986264
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2951986264Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fphar.2019.00641Digital Object Identifier
- Title
-
Optimizing Vancomycin Dosing in Chronic Kidney Disease by Deriving and Implementing a Web-Based Tool Using a Population Pharmacokinetics AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-06-11Full publication date if available
- Authors
-
Sreemanee Raaj Dorajoo, Chrystal Leandra Winata, Jessica Hui Fen Goh, Say Tat Ooi, Jyoti Somani, Lee Ying Yeoh, Siok Ying Lee, Chun Wei Yap, Alexandre Chan, Jung‐woo ChaeList of authors in order
- Landing page
-
https://doi.org/10.3389/fphar.2019.00641Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fphar.2019.00641Direct OA link when available
- Concepts
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Dosing, Medicine, Vancomycin, Kidney disease, Pharmacokinetics, Population, Nephrotoxicity, Cohort, Renal function, Volume of distribution, Intensive care medicine, Renal replacement therapy, Internal medicine, Kidney, Staphylococcus aureus, Biology, Environmental health, Genetics, BacteriaTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1, 2022: 3, 2021: 1, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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