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Published byLynette Shields Modified over 8 years ago
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INTERMITTENT IV VANCOMYCIN WHAT DOSE SHOULD WE START WITH?
Clare Nash Sheffield Children’s NHS Foundation Trust November 2014 1
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Vancomycin Glycopeptide antibiotic in use since 1950s
Useful vs Staph including MRSA Renal toxicity Monitor trough concentrations Time dependent killing Small post antibiotic effect
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15mg/kg tds 5-10mg/l (10-15mg/l for less sensitive organisms)
Pre 2007 2007 onwards 5-10mg/l (10-15mg/l for less sensitive organisms) 10-15mg/l (15-20mg/l)
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Why were target troughs increased?
Increasing resistance Long use shown less renal toxicity than originally thought AUC24/MIC >400mg.h/l
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Method Prospective data collection Seven paediatric centres
Record every dose, time dose given, level and time levels were taken. Collect demographic data, indication, renal function, co-morbidities likely to affect vancomycin kinetics. Descriptive Statistics/PK analysis Basing our work around this study we decided to do something similar that would be applicable to UK practice.
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Descriptive Statistics
243 patients 1017 vancomycin levels First level in range 24% Low 69% High 7% Patients NEVER in range 33%
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Trough levels by Creatinine Clearance
CRCL (ml/min/1.73m2) Number % Mean Trough levels (mg/L) Min Max Stand. Dev. [0-50] 41 7.77% 17.19 0.15 22.3 6.81 >50 487 92.23% 11.60 0.1 24 6.47 Total 528 100.00%
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Mean Trough levels (mg/L)
Trough levels by age Age Number % Mean Trough levels (mg/L) Min Max Stand. Dev. 1-6months 101 19.17% 13.46 3.1 28 5.55 6months-2years 127 24.10% 10.85 2.4 40 6.13 2years to 12 years 215 40.80% 11.99 2.1 46 7.47 >12 years 84 15.94% 12.21 7.59 Total 527 100.00%
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Results of Pharmacokinetic Modelling and Simulation
Number of patients = 226 Patients on renal/extracorporeal support excluded Number of observations (plasma levels) = 912 >85% trough levels, < 5 % peak levels All data pooled into a single analysis dataset Pharmacokinetic parameters (clearance, volume of distribution) estimated using ‘Non-linear mixed effects modelling’ 1-compartment PK model best fit the data
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Results Clearance (CL) found to be dependent on actual Body Weight and Creatinine Clearance Volume of distribution (Vd) found to be dependent on Age Significant variability in found in both parameters : CL (46 % CV), Vd (72% CV) Final PK model then used to simulate BNFC and optimum doses Doses that achieve AUC0-24 > 400 identified Model thoroughly evaluated using standard statistical techniques
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Simulations from the model
BNF-C Dose (15mg/kg every 8 hours) New doses to achieve target AUC>400
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Simulated Steady State AUC
Age Category BNF C mg/kg 8 hourly AUC0-24,ss Model Dose 6 hourly Model Dose Model Dose mg/kg 1 mnth 15 544 10 485 6 mnth 328 437 1 yr 259 346 17.5 404 20 461 3 yr 278 371 433 494 6 yr 297 397 463 529 12 yr 329 439 16 yr 353 470
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Simulated steady state trough levels
Age Category BNF C mg/kg 8 hourly Troughss Model Dose 6 hourly Model Dose Model Dose mg/kg 1 mnth 15 15.1 10 15.2 6 mnth 10.4 1 yr 8.6 12.3 17.5 14.3 20 16.38 3 yr 9.8 13.8 16.1 18.36 6 yr 14.6 17.1 19.50 12 yr 10.7 15.4 16 yr 11.1 16.5
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Conclusions (1) Robust 1-compartment model developed from prospectively collected TDM data Clearance influenced by actual body weight and creatinine clearance Volume influenced by age Current BNF-C dose (15mg/kg 8 hourly) does not achieve target AUC>400 in the typical patient (except infants 1-6 months)
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Conclusions(2) Infant 1 month: 10mg/kg 6 hourly
To achieve target AUC>400, model optimised doses proposed: Infant 1 month: 10mg/kg 6 hourly Infant 6 month: 15mg/kg 6 hourly Child 1 – 6 year: 17.5mg/kg 6 hourly Child year: 15mg/kg 6 hourly These doses equate to trough levels mg/L
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Some caveats….. Modelling excluded patients with severe renal failure and renal/extracorporeal support Proposed doses are for the ‘typical’ patient in the population and: are initial starting doses subsequent doses should be ‘individualised’ using TDM Proposed doses assume ‘normal’ creatinine clearance for age doses will need to be adjusted with changes in renal function Proposed doses need to be evaluated in a prospective study
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Thank you to all those collecting data
All my pharmacy colleagues at Sheffield CH Nigel Gooding Addenbrookes, Cambridge Caroline Cole University Hospital, Southampton Tricia Cummings Our Lady’s Children’s Hospital, Dublin Abimbola Olusoga, LGI,Leeds Jenny Haylor, Bristol Children’s Hospital David Sharpe, Alder Hey Children’s Hospital Liverpool
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Project Team Clare Nash: Sheffield Children’s Hospital
Nigel Gooding: Lead Pharmacist Paediatrics, Addenbrooks Hussain Mulla: Senior Research Pharmacist – Paediatric Clinical Pharmacology, University Hospitals of Leicester (Pharmacokineticist) Bradley Manktelow: Senior Research Fellow, Department of Health Sciences, University of Leicester (Statistician)
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Funded by the Neonatal and Paediatric Pharmacists Group
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