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Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls
BACKGROUND: Accuracy of vancomycin trough monitoring has come into question. We evaluated an area under the curve (AUC) monitoring protocol and 3 different dosing calculators at a single center. METHODS: Adult inpatients with vancomycin AUC monitoring from 5/2016–1/2017 were included. We excluded th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631934/ http://dx.doi.org/10.1093/ofid/ofx163.569 |
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author | Kassamali, Zahra Nguyen, Thu |
author_facet | Kassamali, Zahra Nguyen, Thu |
author_sort | Kassamali, Zahra |
collection | PubMed |
description | BACKGROUND: Accuracy of vancomycin trough monitoring has come into question. We evaluated an area under the curve (AUC) monitoring protocol and 3 different dosing calculators at a single center. METHODS: Adult inpatients with vancomycin AUC monitoring from 5/2016–1/2017 were included. We excluded those with peaks drawn less than 1 hour after infusion. AUC was calculated with Sawchuck–Zaske (SZ) methodology. This was compared with two publicly available online calculators: ClinCalc and UCSF’s infectious disease monitoring program (IDMP). Paired t-tests were used to compare AUCs from ClinCalc and IDMP to SZ. We collected renal function, infection, microbiology, and dosing data. Clinical outcomes included survival to discharge, discharge disposition, rate of acute kidney injury (AKI) per Risk-Injury-Failure-Loss-End Stage Renal Disease criteria, and bacterial clearance. RESULTS: 29 subjects were included. Median age was 48 years, 59% were male, median weight was 80.4 kg. Median daily dose was 3000 mg (32.4 mg/kg). No patient had renal impairment at baseline. Skin and soft-tissue infections were most common, 11 (38%). Six subjects had bacteremia, 2 had confirmed endocarditis. MRSA was isolated in 14 cases (48%). Median duration of vancomycin was 11 days. Mean 24 hour AUC (standard deviation) was 654 (203) mg/L for SZ, 536 (278) mg/l for ClinCalc (P = 0.02) and 556 (187) mg/L (P = 0.004) for IDMP. AUC differences of at least 30% compared with SZ were identified in 14 (48%) and 6 (21%) subjects evaluated with ClinCalc and IDMP respectively. AKI occurred in three subjects: two risk and one injury. All survived to discharge; 52% discharged home, 41% to a skilled nursing facility, 7% left against medical advice. Twenty (69%) had bacterial clearance, 2 (7%) had persistently positive cultures, 7 (24%) were treated empirically. CONCLUSION: Vancomycin AUC varies with calculation methodology. The SZ method was impacted by dose and duration of infusion. ClinCalc showed greater variability in higher weight patients. ClinCalc and IDMP calculated lower AUCs than SZ, and recommended higher doses to target an AUC:MIC ratio of at least 400. As institutions adopt vancomycin AUC monitoring, awareness of calculator variation is critical due to impact on dose selection and risk of toxicity to patients. DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-5631934 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56319342017-11-07 Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls Kassamali, Zahra Nguyen, Thu Open Forum Infect Dis Abstracts BACKGROUND: Accuracy of vancomycin trough monitoring has come into question. We evaluated an area under the curve (AUC) monitoring protocol and 3 different dosing calculators at a single center. METHODS: Adult inpatients with vancomycin AUC monitoring from 5/2016–1/2017 were included. We excluded those with peaks drawn less than 1 hour after infusion. AUC was calculated with Sawchuck–Zaske (SZ) methodology. This was compared with two publicly available online calculators: ClinCalc and UCSF’s infectious disease monitoring program (IDMP). Paired t-tests were used to compare AUCs from ClinCalc and IDMP to SZ. We collected renal function, infection, microbiology, and dosing data. Clinical outcomes included survival to discharge, discharge disposition, rate of acute kidney injury (AKI) per Risk-Injury-Failure-Loss-End Stage Renal Disease criteria, and bacterial clearance. RESULTS: 29 subjects were included. Median age was 48 years, 59% were male, median weight was 80.4 kg. Median daily dose was 3000 mg (32.4 mg/kg). No patient had renal impairment at baseline. Skin and soft-tissue infections were most common, 11 (38%). Six subjects had bacteremia, 2 had confirmed endocarditis. MRSA was isolated in 14 cases (48%). Median duration of vancomycin was 11 days. Mean 24 hour AUC (standard deviation) was 654 (203) mg/L for SZ, 536 (278) mg/l for ClinCalc (P = 0.02) and 556 (187) mg/L (P = 0.004) for IDMP. AUC differences of at least 30% compared with SZ were identified in 14 (48%) and 6 (21%) subjects evaluated with ClinCalc and IDMP respectively. AKI occurred in three subjects: two risk and one injury. All survived to discharge; 52% discharged home, 41% to a skilled nursing facility, 7% left against medical advice. Twenty (69%) had bacterial clearance, 2 (7%) had persistently positive cultures, 7 (24%) were treated empirically. CONCLUSION: Vancomycin AUC varies with calculation methodology. The SZ method was impacted by dose and duration of infusion. ClinCalc showed greater variability in higher weight patients. ClinCalc and IDMP calculated lower AUCs than SZ, and recommended higher doses to target an AUC:MIC ratio of at least 400. As institutions adopt vancomycin AUC monitoring, awareness of calculator variation is critical due to impact on dose selection and risk of toxicity to patients. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2017-10-04 /pmc/articles/PMC5631934/ http://dx.doi.org/10.1093/ofid/ofx163.569 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Kassamali, Zahra Nguyen, Thu Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls |
title | Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls |
title_full | Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls |
title_fullStr | Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls |
title_full_unstemmed | Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls |
title_short | Implementation of a Vancomycin AUC Monitoring Program: Peaks and Pitfalls |
title_sort | implementation of a vancomycin auc monitoring program: peaks and pitfalls |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631934/ http://dx.doi.org/10.1093/ofid/ofx163.569 |
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