Bayesian clinical decision support-guided versus clinician-guided vancomycin dosing in attainment of targeted pharmacokinetic parameters in a paediatric population

Hughes, David; Goswami, Srijib; Keizer, Ron J.; Hughes, Maria-Stephanie A.

doi:10.1093/jac/dkz444

Cited by 15 publications

(12 citation statements)

References 15 publications

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“…These samples overestimated vancomycin concentrations compared to correctly timed samples, resulting in an increased likelihood of prescribers incorrectly reducing, discontinuing or withholding doses 6 . Dose‐prediction software is an increasingly popular intervention that has the potential to improve vancomycin dosing 8–11 . State of the art dose‐prediction software uses Bayesian forecasting to integrate readily available patient data with validated population pharmacokinetic models to provide individualised dose recommendations.…”

Section: Introductionmentioning

confidence: 99%

Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose‐recommendation software

Carland

Elhage

Baysari

et al. 2020

Brit J Clinical Pharma

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Aims Dose‐prediction software can optimise vancomycin therapy, improving therapeutic drug monitoring processes and reducing drug toxicity. Success of software in hospitals may be dependent on prescriber uptake of software recommendations. This study aimed to identify the perceived psychosocial and environmental barriers and facilitators to prescriber acceptance of dose‐prediction software. Methods Semi‐structured interviews, incorporating prescribing scenarios, were undertaken with 17 prescribers. Participants were asked to prescribe the next maintenance dose of vancomycin for a scenario(s) and then asked if they would accept a recommendation provided by a dose‐prediction software. Interviews further explored opinions of dose‐prediction software. Interview transcripts were analysed using an inductive approach to identify themes and the Theoretical Domains Framework was used to synthesise barriers and facilitators to software acceptance. Results When presented with software recommendations, half of the participants were comfortable with accepting the recommendation. Key barriers to acceptance of software recommendations aligned with 2 Theoretical Domains Framework domains: Knowledge (uncertainty of software capability) and Beliefs about Consequences (perceived impact of software on clinical outcomes and workload). Key facilitators aligned with 2 domains: Beliefs about Consequences (improved efficiency) and Social Influences (influence of peers). A novel domain, Trust, was identified as influential. Conclusion Prescribers reported barriers to acceptance of dose‐prediction software aligned with limited understanding of, and scepticism about, software capabilities, as well as concerns about clinical outcomes. Identification of key barriers and facilitators to acceptance provides essential information to design of implementation strategies to support the introduction of this intervention into the workplace.

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Section: Introductionmentioning

confidence: 99%

Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose‐recommendation software

Carland

Elhage

Baysari

et al. 2020

Brit J Clinical Pharma

View full text Add to dashboard Cite

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“…18 The study was also not Bayesian dosing tool to be better than clinician judgement in recommending vancomycin dosing regimens. 25 We consider this a conservative improvement, since mean AUC 24 and %TTR could have been further improved had we used higher loading doses. The loading dose used during this study was according to hospital guideline rather than DoseMeRx.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, a recently reported study of vancomycin dosing in children by Hughes et al produced very similar results to our study. The authors found that Bayesian tool guided dosing regimens were predicted to achieve vancomycin steady‐state troughs in the therapeutic range 10–15 mg/L on 70.8% of occasions compared to 37.5% in the clinician guided arm, showing the use of a Bayesian dosing tool to be better than clinician judgement in recommending vancomycin dosing regimens 25 …”

Section: Discussionmentioning

confidence: 99%

Personalised dosing of vancomycin: A prospective and retrospective comparative quasi‐experimental study

Vali

Jenkins

Vaja

et al. 2020

Brit J Clinical Pharma

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Aims The 2019 update to the US consensus guideline for vancomycin therapeutic monitoring advocates using Bayesian‐guided personalised dosing to maximise efficacy and minimise toxicity of vancomycin. We conducted an observational cohort study of the implementation of bed‐side Bayesian‐guided vancomycin dosing in vascular surgery patients. Methods Over a 9‐month prospective study period, vascular surgery patients were dosed vancomycin using Bayesian‐guided dosing decision tool (DoseMeRx) and compared retrospectively with a control group admitted to the same ward in the 14 months prior to the study and dosed using a standard algorithmic approach. Primary endpoints were proportion of patients achieving mean area under the curve in 24 hours (AUC24) in the acceptable range 350–450 mg/L• h and percentage time in acceptable range (%TTR). Secondary endpoints focused on clinical outcomes including incidence of acute kidney injury. Results A significantly higher proportion of DoseMeRx patients achieved mean AUC24 values in the acceptable range compared to the control group; 71/104 (68.3%) vs 58/139 (41.7%), P < .005. The median %TTR was also greater in DoseMeRx patients compared to the control group (57.1 vs 30.0%, P < .00001). Patients in the DoseMeRx group missed an average of 0.23 doses per course compared to 1.04 doses in the control group (P < .00001). No difference was observed in secondary (clinical) outcomes between the 2 groups. Conclusion Bedside Bayesian‐guided personalised dosing of vancomycin increases the proportion of patients achieving target AUC24 and the %TTR.

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“…In the past few decades, many studies have supported the use of the PopPK model to optimize vancomycin dosage schedule. 4,5 Because the sampling method is more convenient for patients, the PopPK has become a practical tool. Vancomycin has a narrow therapeutic index and shows high inter-individual variability, making the dosage adjustment challenging.…”

Section: What Is K Nown and Objec Tivementioning

confidence: 99%

Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents

et al. 2021

J Clin Pharm Ther

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What is known and objectives: Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set. Methods:The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction-and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models.Results: Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F 20 ) ≥35% and PE% within ±30% (F 30 ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models. What is new and conclusion:The 10 published models performed unsatisfactorily in prediction-and simulation-based diagnostics; none of the published models was | 821 LV et aL. How to cite this article: Lv C, Lu J, Jing L, et al. Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents. J

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Bayesian clinical decision support-guided versus clinician-guided vancomycin dosing in attainment of targeted pharmacokinetic parameters in a paediatric population

Cited by 15 publications

References 15 publications

Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose‐recommendation software

Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose‐recommendation software

Personalised dosing of vancomycin: A prospective and retrospective comparative quasi‐experimental study

Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents

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