Economic evaluations of therapeutic drug monitoring interventions in acute hospital-based settings: A systematic review.

AIMS: Therapeutic drug monitoring (TDM) aims to optimize drug therapy. As demand on health resources increases, and the technology underpinning TDM becomes more sophisticated, the economic benefits of TDM in hospitals is unclear. The aim of this systematic review was to summarize the economic evidence that could be used to support investment in TDM in hospital settings. In so doing, we sought to provide guidance for future economic evaluations. METHODS: Medline, Embase, CENTRAL, Econlit and NHS Economic Evaluation databases were searched (inception to December 2022) for economic evaluations of hospital-based TDM. Two authors reviewed the studies and extracted data. Overall quality of economic analysis reporting was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. RESULTS: Ten prospective studies (including six randomized studies) and nine retrospective studies were eligible. Overall study reporting was poor, publications meeting a median (range) of 61% (46-82%) of CHEERS checklist criteria. An antimicrobial TDM intervention for adult patients was the focus of most studies (n = 18). Variable clinical outcomes were reported, and length of stay was the primary economic outcome for most studies (n = 13). The majority of studies determined that TDM was economically and clinically favourable (n = 14), four studies reporting a cost-reduction in patient sub-populations. CONCLUSIONS: Significant improvements in both economic and clinical outcomes may be realized with TDM interventions, particularly when targeted to complex patient populations. Attainment of therapeutic target could serve as a feasible surrogate measure of benefit for hospital-based TDM interventions. However, systematic reporting of economic outcomes is needed to inform investment decisions.

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients.

BACKGROUND: The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients. METHODS: Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero. RESULTS: In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33). CONCLUSIONS: Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

Blood, dose recommendation reports and phone calls: Experiences of a therapeutic drug monitoring advisory service for vancomycin.

Dose-prediction software is recommended to enable area under the curve over 24 h (AUC24 )-guided dosing of the antibiotic vancomycin. However, uncertainty remains about how best to implement software in the clinic. We describe the activity, over 18 months, of a consultative therapeutic drug monitoring Advisory Service (the Service) for vancomycin that used dose-prediction software alongside clinical expertise, identifying factors that influence attainment of therapeutic targets. Of the 408 vancomycin dose reports provided for 182 courses of therapy, most (57%) recommended a dose change. The majority (82.8%, 193/233) of recommended dose adjustments were accepted by treating teams. A dose report was not published for 125 courses of therapy, with reasons including patient in intensive care unit or service error. An estimated 26.6 h of staff time was allocated to Service activities each month. Publication of a dose report facilitated attainment of therapeutic targets (P = .002). Software integration could improve Service outcomes, avoiding errors and reducing staff workload.

Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients.

AIM: Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction. METHODS: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions. RESULTS: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P < .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing. CONCLUSION: A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.

The safe use of metformin in heart failure patients both with and without T2DM: A cross-sectional and longitudinal study.

AIMS: This study investigated the safe use of metformin in patients with (1) type 2 diabetes mellitus (T2DM) and heart failure on metformin, and (2) heart failure without T2DM and metformin naïve. METHODS: Two prospective studies on heart failure patients were undertaken. The first was a cross-sectional study with two patient cohorts, one with T2DM on metformin (n = 44) and one without T2DM metformin naive (n = 47). The second was a 12-week interventional study of patients without T2DM (n = 27) where metformin (500 mg immediate release, twice daily) was prescribed. Plasma metformin and lactate concentrations were monitored. Individual pharmacokinetics were compared between cohorts. Univariable and multivariable analysis analysed the effects of variables on plasma lactate concentrations. RESULTS: Plasma metformin and lactate concentrations mostly (99.9%) remained below safety thresholds (5 mg/L and 5 mmol/L, respectively). Metformin concentration had no significant relationship with lactic acidosis safety markers. In the interventional study, New York Heart Association (NYHA) II (P < .03) and III (P < .001) grading was associated with higher plasma lactate concentrations, whereas male sex was associated with 47% higher plasma lactate concentrations (P < .05). The pharmacokinetics of heart failure patients with and without T2DM were similar. CONCLUSIONS: We observed no unsafe plasma lactate concentrations in patients with heart failure treated with metformin. Metformin exposure did not influence plasma lactate concentrations, but NYHA class and sex did. The pharmacokinetics of metformin in heart failure patients are similar irrespective of T2DM. These findings may support the safe use of metformin in heart failure patients with and without T2DM.

Tools for deprescribing in severe dementia: A scoping review.

OBJECTIVES: Identification of inappropriate medications in people living with severe dementia is a complex task which has the potential to reduce avoidable adverse events and increase quality of life. This scoping review (i) identifies published tools intended to aid deprescribing in people living with severe dementia and (ii) describes evaluations of their usefulness in clinical practice. METHODS: A scoping review was undertaken, with Medline, Medline in Process, EMBASE, Cochrane Library, CINAHL, Scopus and Web of Science databases, from inception to April 2023, identifying tools for deprescribing in severe dementia. A tool was considered as any resource for deprescribing, including clinical study, scientific publication, health guideline, website, algorithm, model or framework. Two reviewers assessed the eligibility of articles through abstract and full text review. Data extracted from included studies were summarized through narrative synthesis. RESULTS: Twelve studies were identified from 18,633 articles screened. Tools were categorized into three groups: deprescribing interventions (n = 2), consensus-based deprescribing criteria (n = 5), and medication-specific recommendations (n = 5). Six studies developed tools using expert opinion and ten tools were tested in people living with severe dementia. Only one of the four studies that evaluated patient outcomes (cognitive change and adverse events) identified clear clinical benefit from medication withdrawal. CONCLUSIONS: Clinical application of current deprescribing tools is limited due to the lack of evidence-based research on the clinical effects of individual medication deprescribing in people with severe dementia. Further research on patient outcomes, including cognitive change and adverse events, will help clarify the role of these tools in clinical care.

'It's a fragile open door'-enhancing COVID-19 vaccination rates in people receiving treatment for substance use disorder.

BACKGROUND: People with substance use disorder are at high risk of harms from COVID-19 infection. Vaccine hesitancy is common in this population and compounds pre-existing barriers to accessing health care. A drug and alcohol service in Sydney, Australia introduced strategies to enhance COVID-19 vaccination in people receiving opioid agonist treatment (OAT). We report vaccination outcomes and staff experiences of this. METHODS: This mixed methods study (i) retrospectively evaluated vaccine uptake in people accessing OAT and (ii) explored perceptions of staff who delivered vaccination interventions through surveys and semi-structured interviews. RESULTS: Of the 984 patients receiving OAT on 9 December 2021, 90.9% had received the first COVID-19 vaccination and 86.7% the second. Australia wide vaccination rates on that date were 93.1% and 88.7% for first and second doses, respectively. Staff commented that having a deep knowledge, understanding and connection with the patient group drove implementation and success of vaccination interventions. This was further supported by staff engagement with the vaccination interventions, and communication and sharing information, both between staff and with patients. CONCLUSION: High rates of COVID-19 vaccination can be achieved in a vulnerable population. Engaged staff providing information and facilitating access to healthcare underpin this success.

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients.

BACKGROUND AND AIM: Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments. METHODS: Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use. RESULTS: Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations. CONCLUSION: All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Evaluation of amikacin use and comparison of the models implemented in two Bayesian forecasting software packages to guide dosing.

AIMS: Bayesian forecasting software can assist in guiding therapeutic drug monitoring (TDM)-based dose adjustments for amikacin to achieve therapeutic targets. This study aimed to evaluate amikacin prescribing and TDM practices, and to determine the suitability of the amikacin model incorporated into the DoseMeRx® software as a replacement for the previously available software (Abbottbase®). METHODS: Patient demographics, pathology, amikacin dosing history, amikacin concentrations and Abbottbase® predicted TDM targets (area under the curve up to 24 hours, maximum concentration and trough concentration) were collected for adults receiving intravenous amikacin (2012-2017). Concordance with the Australian Therapeutic Guidelines was assessed. Observed and predicted amikacin concentrations were compared to determine the predictive performance (bias and precision) of DoseMeRx®. Amikacin TDM targets were predicted by DoseMeRx® and compared to those predicted by Abbottbase®. RESULTS: Overall, guideline compliance for 63 courses of amikacin in 47 patients was suboptimal. Doses were often lower than recommended. For therapy >48 h, TDM sample collection timing was commonly discordant with recommendations, therapeutic target attainment low and 34% of dose adjustments inappropriate. DoseMeRx® under-predicted amikacin concentrations by 0.9 mg/L (95% confidence interval [CI] -1.4 to -0.5) compared with observed concentrations. However, maximum concentration values (n = 19) were unbiased (-1.7 mg/L 95%CI -5.8 to 0.8) and precise (8.6% 95%CI 5.4-18.1). Predicted trough concentration values (n = 7) were, at most, 1 mg/L higher than observed. Amikacin area under the curve values estimated using Abbottbase® (181 mg h/L 95%CI 161-202) and DoseMeRx® (176 mg h/L 95%CI 152-199) were similar (P = .59). CONCLUSION: Amikacin dosing and TDM practice was suboptimal compared with guidelines. The model implemented by DoseMeRx® is satisfactory to guide amikacin dosing.

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

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.

Accuracy of documented administration times for intravenous antimicrobial drugs and impact on dosing decisions.

AIMS: Accurate documentation of medication administration time is imperative for many therapeutic decisions, including dosing of intravenous antimicrobials. The objectives were to determine (1) the discrepancy between actual and documented administration times for antimicrobial infusions and (2) whether day of the week, time of day, nurse-to-patient ratio and drug impacted accuracy of documented administration times. METHODS: Patient and dosing data were collected (June-August 2019) for 55 in-patients receiving antimicrobial infusions. "Documented" and "actual" administration times (n = 660) extracted from electronic medication management systems and smart infusion pumps, respectively, were compared. Influence of the day (weekday/weekend), time of day (day/evening/night), nurse-to-patient ratio (high 1:1/low 1:5) and drug were examined. Monte Carlo simulation was used to predict the impact on dose adjustments for vancomycin using the observed administration time discrepancies compared to the actual administration time. RESULTS: The median discrepancy between actual and documented administration times was 16 min (range, 2-293 min), with discrepancies greater than 60 minutes in 7.7% of administrations. Overall, discrepancies (median [range]) were similar on weekends (17 [2-293] min) and weekdays (16 [2-188] min), and for high (16 [2-157] min) and low nurse-to-patient ratio wards (16 [2-293] min). Discrepancies were smallest for night administrations (P < .05), and antimicrobials with shorter half-lives (P < .0001). The observed discrepancies in vancomycin administration time resulted in a different dose recommendation in 58% of cases (30% higher, 28% lower). CONCLUSIONS: Overall, there were discrepancies between actual and documented antimicrobial infusion administration times. For vancomycin, these discrepancies in administration time were predicted to result in inappropriate dose recommendations.

Are vancomycin dosing guidelines followed? A mixed methods study of vancomycin prescribing practices.

AIMS: Despite the availability of international consensus guidelines, vancomycin dosing and therapeutic drug monitoring (TDM) remain suboptimal. This study aimed to assess concordance of vancomycin dosing and TDM with institutional guidelines and to identify factors taken into consideration by clinicians when prescribing vancomycin. METHODS: A retrospective audit of 163 patients receiving vancomycin therapy (≥48 hours) was undertaken. Data collected included patient characteristics, dosing history and plasma vancomycin and creatinine concentrations. Concordance of dosing and TDM with institutional guidelines was evaluated. Semi-structured interviews, including simulated prescribing scenarios, were undertaken with prescribers (n = 17) and transcripts analysed. RESULTS: Plasma vancomycin concentrations (n = 1043) were collected during 179 courses of therapy. Only 24% of courses commenced with a loading dose with 72% lower than recommended. The initial maintenance dose was concordant in 42% of courses with 34% lower than recommended. Only 14% of TDM samples were trough vancomycin concentrations. Dose was not adjusted for 60% (21/35) of subtherapeutic and 43% (18/42) of supratherapeutic trough vancomycin concentrations, respectively. Interview participants reported that patient characteristics (including renal function), vancomycin concentrations, guidelines and expert advice influenced vancomycin prescribing decisions. Despite referring to guidelines when completing simulated prescribing scenarios, only 37% of prescribing decisions aligned with guideline recommendations. CONCLUSION: Poor compliance with institutional vancomycin guidelines was observed, despite prescriber awareness of available guidelines. Multifaceted strategies to support prescriber decision-making are required to improve vancomycin dosing and monitoring.

Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software.

BACKGROUND: Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations. METHODS: A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated. RESULTS: The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy. CONCLUSIONS: Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

Assessing the accuracy of two Bayesian forecasting programs in estimating vancomycin drug exposure.

BACKGROUND: Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome. OBJECTIVES: To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0-∞ in adults with limited blood concentration sampling. METHODS: The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0-∞ values using a single vancomycin concentration sampled at various times post-infusion. RESULTS: Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018. 40: 212-21) and Thomson et al. (J Antimicrob Chemother 2009. 63: 1050-7) had precise and unbiased sampling times (defined as mean imprecision <25% and <38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion. CONCLUSIONS: When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.

Voriconazole: an audit of hospital-based dosing and monitoring and evaluation of the predictive performance of a dose-prediction software package.

BACKGROUND: Therapeutic drug monitoring (TDM) is recommended to guide voriconazole therapy. OBJECTIVES: To determine compliance of hospital-based voriconazole dosing and TDM with the Australian national guidelines and evaluate the predictive performance of a one-compartment population pharmacokinetic voriconazole model available in a commercial dose-prediction software package. METHODS: A retrospective audit of voriconazole therapy at an Australian public hospital (1 January to 31 December 2016) was undertaken. Data collected included patient demographics, dosing history and plasma concentrations. Concordance of dosing and TDM with Australian guidelines was assessed. Observed concentrations were compared with those predicted by dose-prediction software. Measures of bias (mean prediction error) and precision (mean squared prediction error) were calculated. RESULTS: Adherence to dosing guidelines for 110 courses of therapy (41% for prophylaxis and 59% for invasive fungal infections) was poor, unless oral formulation guidelines recommended a 200 mg dose, the most commonly prescribed dose (56% of prescriptions). Plasma voriconazole concentrations were obtained for 82% (90/110) of courses [median of 3 (range: 1-27) obtained per course]. A minority (27%) of plasma concentrations were trough concentrations [median concentration: 1.5 mg/L (range: <0.1 to >5.0 mg/L)]. Of trough concentrations, 57% (58/101) were therapeutic, 37% (37/101) were subtherapeutic and 6% (6/101) were supratherapeutic. The dose-prediction software performed well, with acceptable bias and precision of 0.09 mg/L (95% CI -0.08 to 0.27) and 1.32 (mg/L)2 (95% CI 0.96-1.67), respectively. CONCLUSIONS: Voriconazole dosing was suboptimal based on published guidelines and TDM results. Dose-prediction software could enhance TDM-guided therapy.

A pharmacokinetic-pharmacodynamic study of a single dose of febuxostat in healthy subjects.

AIMS: To examine the pharmacokinetic-phamacodynamic (PK-PD) relationships of plasma febuxostat and serum urate and the effect of a single dose of the drug on renal excretion and fractional clearance of urate (FCU). METHODS: Blood and urine samples were collected at baseline and up to 145 hours following administration of febuxostat (80 mg) to healthy subjects (n = 9). Plasma febuxostat and serum and urinary urate and creatinine concentrations were determined. Febuxostat pharmacokinetics were estimated using a two-compartment model with first-order absorption. An Emax PK-PD model was fitted to mean febuxostat and urate concentrations. Urinary urate excretion and FCU were calculated pre- and post-dose. RESULTS: Maximum mean plasma concentration of febuxostat (2.7 mg L-1 ) was observed 1.2 hours after dosage. Febuxostat initial and terminal half-lives were 2.0 ± 1.0 and 14.0 ± 4.7 hours (mean ± SD), respectively. The majority (81%) of the drug was eliminated in the 9 hours after dosing. Serum urate declined slowly achieving mean nadir (0.20 mmol L-1 ) at 24 hours. The IC50 (plasma febuxostat concentration that inhibits urate production by 50%) was 0.11 ± 0.09 mg L-1 (mean ± SD). Urinary urate excretion changed in parallel with serum urate. There was no systematic or significant change in FCU from baseline. CONCLUSION: The PK-PD model could potentially be used to individualise febuxostat treatment and improve clinical outcomes. A single dose of febuxostat does not affect the efficiency of the kidney to excrete urate. Further investigations are required to confirm the present results following multiple dosing with febuxostat.

Education to improve vancomycin use: the perspectives of educators and education recipients.

BACKGROUND: Vancomycin is the primary treatment for methicillin-resistant Staphylococcus aureus infections. Hospital audits have showed that dosing and therapeutic drug monitoring practices for vancomycin are suboptimal. Limited studies have examined the current educational resources used to support vancomycin use. AIMS: To explore and compare the perceptions of health educators and recipients of education on the methods currently used to educate health professionals about vancomycin and to identify ideal methods of education. METHODS: Semi-structured interviews were conducted with health educators around Australia and with recipients of education (doctors and nurses). Interview questions explored previous experiences of education and perceptions of ideal methods of education. Interviews were audio-taped, transcribed and thematically analysed. RESULTS: Health educators explained that current vancomycin education comprises large-scale presentations, but they perceived these to be ineffective. The recipients of vancomycin education reported a lack of formal education on vancomycin. Despite this, both educators and recipients agreed on the ideal methods of education: nurses are reported to be protocol driven, and education for nurses should be through in-services or e-learning modules, while doctors require an evidenced-based approach. Senior doctors initially require convincing that education is needed. Once convinced, they respond well to brief emails and case-based and one-on-one education strategies. Technology-based strategies and problem-based learning were observed to be effective methods for junior doctors. CONCLUSIONS: Vancomycin dosing and therapeutic drug monitoring involves multiple health professionals, but current education strategies do not take this into account. Ideal education strategies need to be multimodal and targeted to specific health profession groups.

Is the use of metformin in patients undergoing dialysis hazardous for life? A systematic review of the safety of metformin in patients undergoing dialysis.

AIMS: Metformin may have clinical benefits in dialysis patients; however, its safety in this population is unknown. This systematic review evaluated the safety of metformin in dialysis patients. METHODS: MEDLINE, Embase, CENTRAL, PsycINFO and the Cochrane Library were searched for randomised controlled trials and observational studies evaluating metformin use in dialysis patients. Three authors reviewed the studies and extracted data. The primary outcomes were mortality, occurrence of lactic acidosis and myocardial infarction (MI) in patients taking metformin during dialysis treatment for ≥12 months (long term). Risk of bias was assessed using Risk Of Bias In Nonrandomised Studies of Interventions (ROBINS-1). Overall quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: Fifteen observational studies were eligible; 7 were prospective observational studies and 8 were case reports/case series. No randomised controlled trials were identified. The 7 prospective observational studies (n = 194) reported on cautious metformin use in patients undergoing maintenance dialysis. Only 3 provided long-term follow-up data. In 2 long-term studies of metformin therapy (≤1000 mg/d) in patients undergoing peritoneal dialysis (PD), 1 reported 6 deaths (6/83; 7%) due to major cardiovascular events (3 MI) and the other reported no deaths (0/35). One long-term study of metformin therapy (250 mg to 500 mg thrice weekly) in patients undergoing haemodialysis reported 4 deaths (4/61; 7%) due to major cardiovascular events (2 MI). These findings provide very low-quality evidence as they come from small observational studies. CONCLUSION: The evidence regarding the safety of metformin in people undergoing dialysis is inconclusive. Appropriately designed randomised controlled trials are needed to resolve this uncertainty.

Comparison of the Area Under the Curve for Vancomycin Estimated Using Compartmental and Noncompartmental Methods in Adult Patients With Normal Renal Function.

BACKGROUND: Vancomycin pharmacokinetics are best described using a 2-compartment model. However, 1-compartment population models are commonly used as the basis for dose prediction software. Therefore, the validity of using a 1-compartment model to guide vancomycin drug dosing was examined. METHODS: Published plasma concentration-time data from adult subjects (n = 30) with stable renal function administered a single intravenous infusion of vancomycin were extracted from previous studies. The vancomycin area under the curve (AUC0-∞) was calculated for each subject using noncompartmental methods (AUCNCA) and by fitting 1- (AUC1CMT), 2- (AUC2CMT), and 3- (AUC3CMT) compartment infusion models. The optimal model fit was determined using the Akaike information criterion and visual inspection of the residual plots. The individual compartmental AUC0-∞ values from the 1- and 2-compartment models were compared with AUCNCA values using one-way repeated measures analysis of variance. RESULTS: The mean (±SD) AUC estimates were similar for the different methods: AUCNCA 180 ± 86 mg·h/L, AUC1CMT 167 ± 79 mg·h/L, and AUC2CMT 183 ± 88 mg·h/L. Despite the overlapping AUC values, AUC2CMT and AUCNCA were significantly greater than AUC1CMT (P < 0.05). The 3-compartment model was excluded from the analysis because of the failure to converge in some instances. CONCLUSIONS: Dose prediction software using a 1-compartment model as the basis for Bayesian forecasting underestimates drug exposure (estimated as the AUC) by less than 10%. This is unlikely to be clinically significant with respect to dose adjustment. Therefore, a 1-compartment model may be sufficient to guide vancomycin dosing in adult patients with stable renal function.