Literature DB >> 25403847

Comparing dosage adjustment methods for once-daily tobramycin in paediatric and adolescent patients with cystic fibrosis.

Stefanie Hennig1, Franziska Holthouse, Christine E Staatz.   

Abstract

BACKGROUND AND OBJECTIVES: Several dosage adjustment methods are currently available to individualize intravenous tobramycin dosing. This study compared different methods in terms of their recommendations for dosage adjustment, their estimation of patients' pharmacokinetic parameter values and their ability to predict subsequent observed tobramycin concentrations following once-daily tobramycin treatment in children and adolescents with cystic fibrosis.
METHODS: Retrospective data from 172 patients treated at the Royal Children's Hospital (Brisbane, QLD, Australia) were analysed. To be included in the analysis, each patient had to have at least one pair of tobramycin plasma concentration-time measurements recorded over a dosing interval. One or both of the concentrations in the paired set were applied in each of the following dosage adjustment methods: (i) the Therapeutic Guidelines Aminoglycoside nomogram; (ii) the Massie nomogram; (iii) log-linear regression analysis; and two Bayesian forecasting software programs: (iv) TCIWorks and (v) DoseMe. All methods were compared in regard to their recommendations for tobramycin dosage adjustment. The latter three methods were also examined in terms of estimated pharmacokinetic parameter values and their ability to predict subsequent observed tobramycin concentrations.
RESULTS: The Therapeutic Guidelines nomogram recommended significantly greater mean doses for dosage adjustment (27.0 mg/kg) compared with all other methods (p ≤ 0.01), which gave similar mean dose recommendations (11.6-14.6 mg/kg); however, >20 % differences in doses on an individual level were seen on 20-35 % of occasions across all methods. The log-linear regression analysis method and the two Bayesian forecasting methods (TCIWorks and DoseMe) showed negligible bias but imprecision of around 20 % in predicting subsequent observed tobramycin concentrations. The Bayesian forecasting methods showed no significant difference in mean dose recommendations when using either one or two concentration measurements but increased imprecision in predicting subsequent observed tobramycin concentrations.
CONCLUSION: The log-linear regression method and Massie nomogram are likely to be suitable alternative methods for tobramycin dosage adjustment when Bayesian forecasting software is unavailable. The Therapeutic Guidelines nomogram should not be used to aid dose adjustment of tobramycin therapy in children with cystic fibrosis.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 25403847     DOI: 10.1007/s40262-014-0211-9

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  40 in total

1.  Pharmacokinetic profile of once daily intravenous tobramycin in children with cystic fibrosis.

Authors:  John Massie; Noel Cranswick
Journal:  J Paediatr Child Health       Date:  2006-10       Impact factor: 1.954

2.  Comparison of gentamicin dose estimates derived from manual calculations, the Australian 'Therapeutic Guidelines: Antibiotic' nomogram and the SeBA-GEN and DoseCalc software programs.

Authors:  Mitali Mohan; Kevin T Batty; Jennifer A Cooper; Richard E Wojnar-Horton; Kenneth F Ilett
Journal:  Br J Clin Pharmacol       Date:  2004-11       Impact factor: 4.335

3.  Application of the optimal design approach to improve a pretransplant drug dose finding design for ciclosporin.

Authors:  Stefanie Hennig; Joakim Nyberg; Samuel Fanta; Janne T Backman; Kalle Hoppu; Andrew C Hooker; Mats O Karlsson
Journal:  J Clin Pharmacol       Date:  2011-05-04       Impact factor: 3.126

4.  A Bayesian feedback method of aminoglycoside dosing.

Authors:  M E Burton; D C Brater; P S Chen; R B Day; P J Huber; M R Vasko
Journal:  Clin Pharmacol Ther       Date:  1985-03       Impact factor: 6.875

5.  Bayesian individualization of pharmacokinetics: simple implementation and comparison with non-Bayesian methods.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharm Sci       Date:  1982-12       Impact factor: 3.534

6.  Ceftazidime disposition in acute and stable cystic fibrosis.

Authors:  J S Leeder; M Spino; A F Isles; A M Tesoro; R Gold; S M MacLeod
Journal:  Clin Pharmacol Ther       Date:  1984-09       Impact factor: 6.875

7.  Use of aminoglycosides in critically ill patients: individualization of dosage using Bayesian statistics and pharmacokinetic principles.

Authors:  H C Böttger; M Oellerich; G W Sybrecht
Journal:  Ther Drug Monit       Date:  1988       Impact factor: 3.681

8.  Comparison of Sawchuk-Zaske and Bayesian forecasting for aminoglycosides in seriously ill patients.

Authors:  C P Denaro; P J Ravenscroft
Journal:  Br J Clin Pharmacol       Date:  1989-07       Impact factor: 4.335

9.  Once-daily aminoglycoside dosing assessed by MIC reversion time with Pseudomonas aeruginosa.

Authors:  J A Karlowsky; G G Zhanel; R J Davidson; D J Hoban
Journal:  Antimicrob Agents Chemother       Date:  1994-05       Impact factor: 5.191

10.  Dosing implications of altered gentamicin disposition in patients with cystic fibrosis.

Authors:  G L Kearns; B C Hilman; J T Wilson
Journal:  J Pediatr       Date:  1982-02       Impact factor: 4.406

View more
  17 in total

Review 1.  Pharmacokinetic and Pharmacodynamic Optimization of Antibiotic Therapy in Cystic Fibrosis Patients: Current Evidences, Gaps in Knowledge and Future Directions.

Authors:  Charlotte Roy; Manon Launay; Sophie Magréault; Isabelle Sermet-Gaudelus; Vincent Jullien
Journal:  Clin Pharmacokinet       Date:  2021-01-24       Impact factor: 6.447

Review 2.  Optimising antimicrobial therapy through the use of Bayesian dosing programs.

Authors:  M L Avent; B A Rogers
Journal:  Int J Clin Pharm       Date:  2019-08-07

3.  Evaluation of Tobramycin Exposure Predictions in Three Bayesian Forecasting Programmes Compared with Current Clinical Practice in Children and Adults with Cystic Fibrosis.

Authors:  Marc Burgard; Indy Sandaradura; Sebastiaan J van Hal; Sonya Stacey; Stefanie Hennig
Journal:  Clin Pharmacokinet       Date:  2018-08       Impact factor: 6.447

4.  Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations.

Authors:  Yu Cheng; Chen-Yu Wang; Zi-Ran Li; Yan Pan; Mao-Bai Liu; Zheng Jiao
Journal:  Clin Pharmacokinet       Date:  2021-01       Impact factor: 6.447

5.  Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis: an Update.

Authors:  Yanhua Gao; Michael Barras; Stefanie Hennig
Journal:  Antimicrob Agents Chemother       Date:  2018-02-23       Impact factor: 5.191

6.  An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting.

Authors:  Alzana A Kumar; Marc Burgard; Sonya Stacey; Indy Sandaradura; Tony Lai; Christine Coorey; Marisol Cincunegui; Christine E Staatz; Stefanie Hennig
Journal:  Br J Clin Pharmacol       Date:  2019-08-06       Impact factor: 4.335

7.  Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis.

Authors:  Michael A Barras; David Serisier; Stefanie Hennig; Katrina Jess; Ross L G Norris
Journal:  Antimicrob Agents Chemother       Date:  2016-10-21       Impact factor: 5.191

8.  Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice?

Authors:  Yanhua Gao; Stefanie Hennig; Michael Barras
Journal:  Clin Pharmacokinet       Date:  2019-03       Impact factor: 6.447

Review 9.  A systematic review of population pharmacokinetic analyses of digoxin in the paediatric population.

Authors:  Mariam H Abdel Jalil; Noura Abdullah; Mervat M Alsous; Mohammad Saleh; Khawla Abu-Hammour
Journal:  Br J Clin Pharmacol       Date:  2020-04-01       Impact factor: 4.335

10.  Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients.

Authors:  Celeste Bloomfield; Christine E Staatz; Sean Unwin; Stefanie Hennig
Journal:  Antimicrob Agents Chemother       Date:  2016-05-23       Impact factor: 5.191

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.