Bayesian Dosing of Tobramycin Outperforms Other Personalized Dosing Strategies
In this article:
DoseMeRx Reduces Labs, Saves Time & Money at The Children’s Hospital at Westmead
A study published in September 2022 in the Journal of Antimicrobial Chemotherapy demonstrated that Bayesian dosing (BD) of tobramycin in children with cystic fibrosis (CF) had fewer blood collections, higher dosing accuracy, and more reliable target concentration attainment compared to log-linear regression (LLR).1
This study was performed between 2015 and 2021 at The Children’s Hospital at Westmead in Australia after their transition to AUC-guided Bayesian dosing using DoseMeRx.
- The use of DoseMeRx improved dosing accuracy, reducing the risk of AKI and antibiotic resistance.
- Compared to other personalized dosing strategies, DoseMeRx significantly reduces the number of AUC0-24 calculations needed.
- DoseMeRx cut the number of blood collections in half compared to other personalized dosing methods, saving time and money as well as reducing patient stress.
Before we look more closely at the results of this study, let’s review cystic fibrosis and tobramycin dosing.
What Is Cystic Fibrosis?
Almost 40,000 children and adults in the United States are living with cystic fibrosis.2 CF impacts multiple organ systems, including the lungs, pancreas, and gastrointestinal system.3 It is caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene.4 Missing or defective CFTR proteins result in thicker and stickier mucus that no longer acts as a lubricant and affects the function of multiple organ systems.4
Pulmonary deterioration is the primary cause of morbidity and mortality in CF. The clinical course of CF typically involves periods of worsening lung function, known as an acute pulmonary exacerbation.5,6
Acute pulmonary exacerbations are caused by microorganisms that opportunistically colonize the airway in CF patients.7 The thick layer of mucus and decreased clearance in CF patients can create an anaerobic environment in the lungs, ideal for facultative anaerobes like Pseudomonas aeruginosa.8 Colonization with P. aeruginosa is a significant predictor of morbidity and mortality in CF patients.9
Other bacteria that are commonly isolated from the airways of patients with CF include:8
- Staphylococcus aureus
- Burkholderia cepacia
- Haemophilus influenzae
- Non-tuberculosis Mycobacteria species
Acute pulmonary exacerbations are commonly treated with antibiotics such as tobramycin.10 However, the impact on pharmacokinetic parameters in patients with CF can complicate dosing narrow therapeutic index drugs like tobramycin.
How Does CF Affect the Pharmacokinetics of Tobramycin?
Patients with CF often need a higher dose of tobramycin compared to the general population because of altered pharmacokinetics.11 Several factors contribute to the altered pharmacokinetics of tobramycin in patients with CF. 12
The thick mucus produced by patients with CF can make it difficult for systemic tobramycin to penetrate the lungs.13
Patients with CF typically have an increased volume of distribution for tobramycin. This is due to decreased adipose tissue compared to the general population, caused by undernourishment.14
Renal function in CF is frequently variable and can be difficult to predict. Some studies show increased renal clearance of aminoglycosides in patients with CF.14 However, patients with advanced disease can have impaired kidney function. Because of reduced muscle mass in CF patients, serum creatinine is not always a reliable marker of kidney function—resulting in an overestimation of kidney function.15
Personalized Tobramycin Dosing
Tobramycin is an aminoglycoside antibiotic commonly used for empiric treatment of acute pulmonary exacerbations in CF, due to its Pseudomonas coverage.10
Dosing strategies for tobramycin have aimed to reduce the toxicity of tobramycin while preserving its efficacy. Over the years, tobramycin dosing has evolved from being dosed every 8 to 12 hours to the now widely accepted once-daily dosing.
It has a concentration-dependent effect, meaning it is more effective at higher concentrations. However, the dose of tobramycin is limited by its potential adverse effects, such as:10
- Vestibular toxicity
Repeated and long-term exposure to aminoglycosides will increase the risk of nephrotoxicity and ototoxicity. This risk is especially important in patients with CF since they have a high risk of multiple antibiotic exposures over their lifetime.16
After an empirical dose is prescribed, personalized tobramycin dosing can be guided by therapeutic drug monitoring (TDM). Two common tobramycin dosing strategies are log-linear regression and Bayesian dosing.
Log-Linear Regression Dosing
The LLR method involves collecting two blood tobramycin concentrations at specific times. These two measurements are used to calculate the AUC from 0 to 24 hours (AUC0-24).17
In addition to requiring two separate blood collections, which can be stressful for patients, this method is also time-consuming for pharmacists to calculate the dose.
Calculating a tobramycin dose using LLR involves manually entering the two tobramycin blood concentrations into a Microsoft Excel spreadsheet embedded with the appropriate pharmacokinetic equations. Because the input is done manually, it may also be prone to transcription errors.
Bayesian dosing software, such as DoseMeRx, uses population pharmacokinetic data to calculate the tobramycin dose.
Only one blood tobramycin concentration taken at any time after the infusion to estimate AUC0-24 is needed for Bayesian dosing.
Bayesian dosing platforms like DoseMeRx can improve accuracy and save time by integrating into the electronic health record (EHR), versus manual input for LLR dosing.
Bayesian Dosing of Tobramycin Offers Clinical and Practical Advantages Over Other Personalized Dosing Strategies
Now that we have reviewed CF and tobramycin dosing, let’s dig into this study’s results.
The goal of this study was to compare the clinical and performance outcomes of LLR and Bayesian dosing strategies in children with CF being treated for acute pulmonary exacerbations.
Which Outcomes Were Measured?
The primary clinical outcomes were:
- Length of stay (LOS) in the hospital
- Unexpected hospital readmission within one month after initial discharge
- Pulmonary function measured by ΔFEV1 — the change in forced expiratory volume in the first second from hospital admission to discharge
- Rate of acute kidney injury (AKI) — defined as a second blood test showing an increase in baseline serum creatinine (SCr) of 1.5 times or more during the hospital stay
The performance outcomes were:
- Number and timing of tobramycin TDM blood samples collected
- Initial dose selection
- Final maintenance dose
- Frequency and precision of target concentration attainment
A total of 376 hospital admissions for CF pulmonary exacerbation were included in this study. LLR dosing was used in 248 (66%) of admissions and BD was used in 128 (34%) of admissions. The patient demographics (gender ratios, age, weight, and BMI) were similar between the LLR and Bayesian dosing (BD) cohorts.
What Benefits of Bayesian Dosing Did This Study Find?
“Programs like DoseMeRx have significantly impacted our clinical workflow when we are so time-poor. I don’t think we can ever return to what we were doing before!”
Tony Lai, Senior Pharmacist, Antimicrobial Stewardship
The Children’s Hospital at Westmead, Australia
The statistically significant performance outcomes of the study were:
- Patients in the LLR group had about twice as many blood samples collected during their hospital admission (3.8 versus 1.9 for LLR and BD, respectively; P < 0.001)
- Patients in the LLR group had a higher percentage of dedicated blood samples taken (74%; 690/933) compared to the BD group (35%; 86/249) (P < 0.001)
- Fewer patients in the BD group required three or more AUC0-24 calculations (11%; 14/128) versus the LLR group (18%; 45/248) (P = 0.01)
- Target AUC0-24 of ≥100 mg/L·h was attained more frequently in the BD group (72%; 92/128) versus the LLR group (50%; 61/248) (P = 0.004)
Bayesian Dosing’s Clinical Advantage
This study demonstrates Bayesian dosing’s clinical advantage over LLR dosing. Even in this notoriously hard-to-dose pediatric CF population, Bayesian dosing had better target AUC0-24 attainment.
Accurate dosing with BD can decrease the risk of AKI and antibiotic resistance, which is especially important for children with CF who will likely have multiple antibiotic exposures in their life.16
BD was also non-inferior to LLR regarding other clinical outcomes such as hospital LOS and ΔFEV1 in this study.
Bayesian Dosing’s Practical Advantage
Not only is Bayesian dosing more accurate, but it’s also more practical than other precision dosing methods.
Bayesian dosing only requires one blood sample that can be taken at any time after the infusion is complete. Fewer blood tobramycin concentrations mean fewer venipunctures. This opens opportunities to combine blood samples with other labs to reduce psychological distress for pediatric patients and potentially save money for the institutions.
Bayesian dosing software also saves time and improves accuracy because it does not rely on manual calculations, which are time-consuming and prone to transcription errors.
DoseMeRx Can Help Your Institution Transition to Bayesian Dosing
Bayesian dosing has been available for more than 20 years and the availability of easy-to-use and efficient software has made it easier than ever to adopt into practice.18 This study shows that there is sufficient support for Bayesian dosing software like DoseMeRx to be advocated as the preferred method of precision drug dosing for tobramycin.
Although there isn’t currently an international consensus on tobramycin TDM, many institutions and guidelines are recognizing the benefit of Bayesian dosing. The Government of South Australia’s tobramycin dosing guidelines recommend manual calculation of AUC only when a software program is not available.19
DoseMeRx uses Bayesian dosing to give personalized tobramycin dosing recommendations for adult, pediatric, and neonatal patients. The dosing models used by DoseMeRx are validated by a team of bioinformaticians, scientists, and clinical pharmacists.
DoseMeRx’s integrated platform can improve your workflow by automatically integrating the patient’s data into the dosing software. DoseMeRx’s embedded analytics platform can give your team instant access to clinical and statistical dashboards to help visualize institutional trends.
Get in touch to explore how DoseMeRx can help your institution improve patient outcomes while saving time and money.
Request a demo
See how easy DoseMeRx is to operate and integrate into your workday.
Request a demo below. You can also phone us on +1 (832) 358-3308 or email email@example.com.
- Imani S, Fitzgerald DA, Robinson PD, et al. Personalized tobramycin dosing in children with cystic fibrosis: A comparative clinical evaluation of log-linear and Bayesian methods. J Antimicrob Chemother. 2022;77(12):3358-3366. doi:10.1093/jac/dkac324
- About cystic fibrosis. Cystic Fibrosis Foundation. (n.d.). Retrieved February 3, 2023 from https://www.cff.org/intro-cf/about-cystic-fibrosis
- Sanders DB, Fink AK. Background and Epidemiology. Pediatr Clin North Am. 2016;63(4):567-584. https://doi.org/10.1016/j.pcl.2016.04.001
- U.S. National Library of Medicine. (n.d.). CFTR gene: CF transmembrane conductance regulator. MedlinePlus. Retrieved February 2, 2023 from https://medlineplus.gov/genetics/gene/cftr/
- Sanders DB, Bittner RC, Rosenfeld M, et al. Pulmonary exacerbations are associated with subsequent FEV1 decline in both adults and children with cystic fibrosis. Pediatr Pulmonol. 2011;46(4):393-400. doi:10.1002/ppul.21374
- Goss CH. Acute pulmonary exacerbations in cystic fibrosis. Semin Respir Crit Care Med. 2019;40(6):792-803. https://doi.org/10.1055/s-0039-1697975
- Castellani C, Duff AJA, Bell SC, et al. ECFS best practice guidelines: The 2018 revision. J Cyst Fibros. 2018;17(2):153-178. https://doi.org/10.1016/j.jcf.2018.02.006.
- Filkins LM, O’Toole GA. Cystic fibrosis lung infections: Polymicrobial, complex, and hard to treat. PLoS Pathog. 2015;11(12):e1005258. https://doi.org/10.1371/journal.ppat.1005258
- Emerson J, Rosenfeld M, McNamara S, et al. Pseudomonas aeruginosa and other predictors of mortality and morbidity in young children with cystic fibrosis. Pediatr Pulmonol. 2002;34(2):91-100. doi:https://doi.org/10.1002/ppul.10127
- Flume PA, Mogayzel PJ Jr, Robinson KA, et al. Cystic fibrosis pulmonary guidelines: Treatment of pulmonary exacerbations. Am J Respir Crit Care Med. 2009;180(9):802-808. doi:https://doi.org/10.1164/rccm.200812-1845PP
- Horrevorts AM, Driessen OM, Michel MF, Kerrebijn KF. Pharmacokinetics of antimicrobial drugs in cystic fibrosis. Aminoglycoside antibiotics. Chest. 1988;94(2 Suppl):120S-125S.
- Akkerman-Nijland AM, Akkerman OW, Grasmeijer F, et al. The pharmacokinetics of antibiotics in cystic fibrosis. Expert Opin Drug Metab Toxicol. 2021;17:1:53-68. https://doi.org/10.1080/17425255.2021.1836157
- Mendelman PM, Smith AL, Levy J, et al. Aminoglycoside penetration, inactivation, and efficacy in cystic fibrosis sputum. Am Rev Respir Dis. 1985;132(4):761-765. https://doi.org/10.1164/arrd.19184.108.40.2061
- Touw DJ. Clinical pharmacokinetics of antimicrobial drugs in cystic fibrosis. Pharm World Sci. 1998;20(4):149-160. doi:https://doi.org/10.1023/a:1008634911114
- Galiniak RM, Biesiadecki S, Gala-Błądzińska A. Renal function in patients with cystic fibrosis: A single-center study. Int J Envrion Res Public Health. 2022;19(9):5454. https://doi.org/10.3390/ijerph19095454
- Prayle A, Watson A, Fortnum H, Smyth A. Side effects of aminoglycosides on the kidney, ear and balance in cystic fibrosis. Thorax. 2010;65(7):654-658. doi:https://doi.org/10.1136/thx.2009.131532
- Jawień W. Searching for an optimal AUC estimation method: A never-ending task?
J Pharmacokinet Pharmacodyn. 2014;41(6):655-673. https://doi.org/10.1007/s10928-014-9392-y
- Barras MA, Serisier D, Hennig S, et al. Bayesian estimation of tobramycin exposure in patients with cystic fibrosis. Antimicrob Agents Chemother. 2016; 60(11):6698-6702. https://doi.org/10.1128/AAC.01131-16
- Aminoglycosides: Recommendations for use, dosing and monitoring. Government of South Australia. 2020. Retrieved from: https://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/clinical+resources/clinical+programs+and+practice+guidelines/medicines+and+drugs/antimicrobial+guidelines/antimicrobial+guidelines