The impact of an electronic hospital system on therapeutic drug monitoring

Firman, Paul; Whitfield, Karen; Tan, Ken‐Soon; Clavarino, Alexandra; Hay, Karen

doi:10.1111/jcpt.13497

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Results from the study indicated that having a pharmacist order the required pathology for vancomycin TDM increased the odds of an appropriate sample (OR = 87.1). These findings build on our previous findings which showed that samples for TDM were more likely to be appropriate if the pharmacist had provided relevant written advice (i.e., documenting when a pathology sample was required for the medical officer) (OR = 2.0; 95% CI = 1.4-2.9) [13]. Here, we have demonstrated further improvement with greater pharmacist involvement.…”

Section: Discussionsupporting

confidence: 86%

“…A recent retrospective review of TDM at one hospital in Australia (13) revealed that the current TDM workflow may not be optimal, and the role of the pharmacist in this process may not be fully utilised. The authors found that TDM samples were more likely to be taken at the appropriate time if the pharmacist had provided advice, whereas this was not the case for appropriate dose adjustment, suggesting the need for greater education and training [13].…”

Section: Introductionmentioning

confidence: 99%

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The Development, Implementation, and Evaluation of a Pharmacist-Managed Therapeutic Drug Monitoring (TDM) Service for Vancomycin—A Pilot Study

et al. 2022

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Background: In recent years, pharmacists in Australia have been able to expand their scope to include the provision of a range of services. Although evidence has demonstrated the benefits of pharmacist-managed TDM services, recent studies have shown that these services are not prominent within Australia and that the current TDM workflow may not be optimal. Methods: An interventional pilot study was conducted of a pharmacist-managed TDM program for vancomycin at a tertiary hospital in Australia. Results: In total, 15 pharmacists participated in the program. They performed 50.5% of the medication-related pathology over the intervention period. Pharmacist involvement in the TDM process was more likely to lead to appropriate TDM sample collection (OR 87.1; 95% CI = 11.5, 661.1) and to an appropriate dose adjustment (OR 19.1; 95% CI = 1.7, 213.5). Pharmacists demonstrated increased confidence after the education and credentialling package was provided. Conclusions: This study demonstrated that a credentialling package for pharmacists can improve knowledge, skills, and confidence around the provision of pharmacist-managed TDM services for vancomycin. This may lead to the evolution of different roles and workflows enabling pharmacists to contribute more efficiently to improving medication safety and use.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

The Development, Implementation, and Evaluation of a Pharmacist-Managed Therapeutic Drug Monitoring (TDM) Service for Vancomycin—A Pilot Study

et al. 2022

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“…A recent Australian study showed the transition from a paper-based to a digital system (ieMR) had no significant impact on two areas of interest; those being appropriate sample collection for TDM and appropriate dose adjustment. However, the study also showed that, overall, regardless of the type of system, the odds of an appropriate sample being taken for TDM increased with pharmacist involvement [ 30 ]. With this impact of pharmacists documented in the literature, there are opportunities in the future for expanding the scope and role of pharmacists in areas including pharmacist-managed TDM programs within the Australian hospital and health service, giving them responsibility for ordering pathology, and prescribing subsequent medication doses based on their clinical review.…”

Section: Discussionmentioning

confidence: 99%

Pharmacist-Managed Therapeutic Drug Monitoring Programs within Australian Hospital and Health Services—A National Survey of Current Practice

et al. 2022

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Pharmacist-managed therapeutic drug monitoring (TDM) services have demonstrated positive outcomes in the literature, including reduced duration of therapy and decreased incidence of the adverse effects of drug therapy. Although the evidence has demonstrated the benefits of these TDM services, this has predominately been within international healthcare systems. The extent to which pharmacist-managed TDM services exist within Australia, and the roles and responsibilities of the pharmacists involved compared to their counterparts in other countries, remains largely unknown. A cross-sectional online survey was conducted evaluating pharmacist-managed TDM programs within Australian hospital and healthcare settings. Pharmacist perceptions were also explored about the strengths, weaknesses, opportunities, and barriers associated with implementing a pharmacist-managed TDM service. A total of 92 surveys were returned, which represents a response rate of 38%. Pharmacist-managed TDM programs were present in 15% of respondents. It is only in the minority of hospitals where there is a pharmacist-managed service, with pharmacists involved in recommending pathology and medication doses. The programs highlighted improved patient outcomes but had difficulty maintaining the educational packages and training. For hospitals without a service, a lack of funding and time were highlighted as barriers. Based on the findings of this survey, there is minimal evidence of pharmacist-managed TDM models within Australian hospital and health services. A standardized national approach to pharmacist-managed TDM services and recognition of this specialist area for pharmacists could be a potential solution to this.

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“…In this study, we aimed to build a simplified XGBoost model to develop an easy-to-use MIPD tool. Considering that the values of some predictors were missing owing to infrequent measurements during TDM (e.g., ALB) or were inaccurate clinical data (e.g., inappropriate sampling time in the TDM practice and irregular single doses or dosing intervals in the prescriptions) ( Jakobsen et al, 2017 ; Firman et al, 2021 ), we built a simplified model by omitting these types of features (i.e., Single Dose, ALB,

) in the final, combined dataset. We developed an easy-to-use model in the clinic by using only CYP2C19 genotypes and some noninvasive clinical parameters as predictors, and observed the influence of the omitted predictors on the performance of the proposed XGBoost model.…”

Section: Methodsmentioning

confidence: 99%

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

et al. 2022

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Background and Aim: Many studies associated with the combination of machine learning (ML) and pharmacometrics have appeared in recent years. ML can be used as an initial step for fast screening of covariates in population pharmacokinetic (popPK) models. The present study aimed to integrate covariates derived from different popPK models using ML.Methods: Two published popPK models of valproic acid (VPA) in Chinese epileptic patients were used, where the population parameters were influenced by some covariates. Based on the covariates and a one-compartment model that describes the pharmacokinetics of VPA, a dataset was constructed using Monte Carlo simulation, to develop an XGBoost model to estimate the steady-state concentrations (Css) of VPA. We utilized SHapley Additive exPlanation (SHAP) values to interpret the prediction model, and calculated estimates of VPA exposure in four assumed scenarios involving different combinations of CYP2C19 genotypes and co-administered antiepileptic drugs. To develop an easy-to-use model in the clinic, we built a simplified model by using CYP2C19 genotypes and some noninvasive clinical parameters, and omitting several features that were infrequently measured or whose clinically available values were inaccurate, and verified it on our independent external dataset.Results: After data preprocessing, the finally generated combined dataset was divided into a derivation cohort and a validation cohort (8:2). The XGBoost model was developed in the derivation cohort and yielded excellent performance in the validation cohort with a mean absolute error of 2.4 mg/L, root-mean-squared error of 3.3 mg/L, mean relative error of 0%, and percentages within ±20% of actual values of 98.85%. The SHAP analysis revealed that daily dose, time, CYP2C19*2 and/or *3 variants, albumin, body weight, single dose, and CYP2C19*1*1 genotype were the top seven confounding factors influencing the Css of VPA. Under the simulated dosage regimen of 500 mg/bid, the VPA exposure in patients who had CYP2C19*2 and/or *3 variants and no carbamazepine, phenytoin, or phenobarbital treatment, was approximately 1.74-fold compared to those with CYP2C19*1/*1 genotype and co-administered carbamazepine + phenytoin + phenobarbital. The feasibility of the simplified model was fully illustrated by its performance in our external dataset. Conclusion: This study highlighted the bridging role of ML in big data and pharmacometrics, by integrating covariates derived from different popPK models.

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The impact of an electronic hospital system on therapeutic drug monitoring

Cited by 7 publications

References 25 publications

The Development, Implementation, and Evaluation of a Pharmacist-Managed Therapeutic Drug Monitoring (TDM) Service for Vancomycin—A Pilot Study

The Development, Implementation, and Evaluation of a Pharmacist-Managed Therapeutic Drug Monitoring (TDM) Service for Vancomycin—A Pilot Study

Pharmacist-Managed Therapeutic Drug Monitoring Programs within Australian Hospital and Health Services—A National Survey of Current Practice

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

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