Identifying the association between tacrolimus exposure and toxicity in heart and lung transplant recipients: A systematic review.

AIMS: Tacrolimus is the cornerstone of immunosuppression management in heart and lung transplant recipients, improving overall survival. However, tacrolimus-associated toxicities, including nephrotoxicity, neurotoxicity, new-onset diabetes mellitus after transplant (NODAT), and gastrointestinal toxicity, are known contributors to increased post-transplant morbidity outcomes and reduced graft and recipient survival rates. The aim of this systematic review was to identify correlations between pharmacokinetic measures of tacrolimus exposure in heart and lung recipients and tacrolimus toxicities. METHODS: MEDLINE, Embase, the Cochrane Library, CENTRAL and WHO Clinical Trial Registries were searched for published studies evaluating tacrolimus toxicities and their correlation to pharmacokinetic monitoring parameters in thoracic transplant recipients. Studies were reviewed by two authors, with data extracted for evaluation. Risk of bias was assessed using the PEDro scale for randomised control trials and the Newcastle Ottawa Scale for non-randomised cohort studies. RESULTS: Eighteen studies were eligible; a randomised control trial, 11 observational cohort studies, and 6 case series or studies. Of these, 9 studies were in heart transplant recipients alone and 5 in lung transplant recipients alone, 2 studies were in heart and lung transplant recipients and 2 were heart, lung, liver or renal transplant recipients. Studies used variable criteria to define toxicities. Tacrolimus trough concentration (C0) was the marker of tacrolimus exposure most commonly used. Ten studies reported on nephrotoxicity. Elevated tacrolimus C0 was associated with acute kidney injury occurrence and severity in three observational studies. Increasing C0 was a predictor of renal impairment in 6 studies. One study found that for each 5 ng/mL per year of tacrolimus exposure, defined by consecutive AUC, eGFR declined by 1.3 mL/min/1.73m2 (p < 0.001). Comparatively, 2 studies failed to find a significant association between nephrotoxicity and tacrolimus exposure. Seven studies reported on neurotoxicity, including neuro-encephalopathies, polyneuropathies and symptomatic change in neurological status. Neurotoxicity occurred both with tacrolimus C0 within therapeutic range and with supratherapeutic C0. No significant association was found between NODAT and tacrolimus C0 in two studies. One study reported on gastrointestinal toxicity, with supratherapeutic C0 and elevated peak concentration in one lung transplant recipient three days prior to symptom development. CONCLUSION: No clearly defined relationship between tacrolimus exposure and toxicities is described in the literature. Studies with clear toxicity criteria and pharmacokinetic markers of tacrolimus exposure are required to provide valuable information that may optimise tacrolimus therapy, helping to reduce toxicities in heart and lung transplant recipients.

Trends, determinants and differences in antibiotic use in 68 residential aged care homes in Australia, 2014-2017: a longitudinal analysis of electronic health record data.

BACKGROUND: Internationally, point prevalence surveys are the main source of antibiotic use data in residential aged care (RAC). Our objective was to describe temporal trends in antibiotic use and antibiotics flagged for restricted use, resident characteristics associated with use, and variation in use by RAC home, using electronic health record data. METHODS: We conducted a retrospective cohort study of 9793 unique residents aged ≥65 years in 68 RAC homes between September 2014 and September 2017, using electronic health records. We modelled the primary outcome of days of antibiotic therapy /1000 resident days (DOT/1000 days), and secondary outcomes of number of courses/1000 days and the annual prevalence of antibiotic use. Antibiotic use was examined for all antibiotics and antibiotics on the World Health Organization's (WHO) Watch List (i.e. antibiotics flagged for restricted use). RESULTS: In 2017, there were 85 DOT/1000 days (99% CI: 79, 92), 8.0 courses/1000 days (99% CI: 7.6, 8.5), and 63.4% (99% CI: 61.9, 65.0) of residents received at least one course of antibiotics. There were 7.7 DOT/1000 days (99% CI: 6.69, 8.77) of antibiotics on the WHO Watch List administered in 2017. Antibiotic use increased annually by 4.09 DOT/1000 days (99% CI: 1.18, 6.99) before adjusting for resident factors, and 3.12 DOT/1000 days (99% CI: - 0.05, 6.29) after adjustment. Annual prevalence of antibiotic use decreased from 68.4% (99% CI: 66.9, 69.9) in 2015 to 63.4% (99% CI: 61.9, 65.0) in 2017, suggesting fewer residents were on antibiotics, but using them for longer. Resident factors associated with higher use were increasing age; chronic respiratory disease; a history of urinary tract infections, and skin and soft tissue infections; but dementia was associated with lower use. RAC home level antibiotic use ranged between 44.0 to 169.2 DOT/1000 days in 2016. Adjusting for resident factors marginally reduced this range (42.6 to 155.5 DOT/1000 days). CONCLUSIONS: Antibiotic course length and RAC homes with high use should be a focus of antimicrobial stewardship interventions. Practices in RAC homes with low use could inform interventions and warrant further investigation. This study provides a model for using electronic health records as a data source for antibiotic use surveillance in RAC.

Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients.

OBJECTIVES: Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance. METHODS: Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression. RESULTS: Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p = 0.007) significantly influenced the performance of the Goti model. CONCLUSIONS: The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting.

OBJECTIVES: Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated. METHOD: Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting. RESULTS: A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions. CONCLUSION: There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.

Evaluation of Clinical Relevance of Drug-Drug Interaction Alerts Prior to Implementation.

INTRODUCTION: Drug-drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review. METHODS: After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts (n = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE. RESULTS: There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: -0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE. CONCLUSION: Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.

Interrogation of a longitudinal, national pharmacy claims dataset to explore factors that predict the need for add-on therapy in older and socioeconomically disadvantaged Australians with type 2 diabetes mellitus patients (T2DM).

PURPOSE: The management of type 2 diabetes mellitus (T2DM) is complex. The aim of this work is to explore factors that predict the need for add-on therapy in patients with T2DM in the community. METHODS: We accessed longitudinal, pharmacy payment claim records from the national Pharmaceutical Benefits Scheme (PBS) (Subsidises costs of medicines: government pays difference between patient co-payments, lower in concessional patients, and additional cost of drug.) for the period January 2006 to September 2014 (EREC/MI3127) from a 10% random sample of the Australian population validated to be representative of the population by the Australian Bureau of Statistics (ABS). Likely, T2DM patients were identified as those having been dispensed a single anti-hyperglycaemic drug (monotherapy). The time taken and possible factors that might lead to the addition of a second therapy were examined. An examination was made of trends in the co-prescription of either antihypertensive or anti-hyperlipidaemic agents in relation to the time (± 3 years) of initiating an anti-hyperglycaemic agent. RESULTS: Most (83%) presumed T2DM patients were initiated with metformin. The average time until the second agent was added was 4.8 years (95% CI 4.7-4.9). Satisfactory adherence, age, male gender, initiating therapy after 2012 and initiating with a sulphonylurea drug all were significant risks for add-on therapy. There was no overall trend in the initiation of antihypertensive and/or anti-hyperlipidaemic agents with respect to the time of anti-hyperglycaemic initiation. CONCLUSION: The usefulness of a longitudinal dataset of pharmacy-claim records is demonstrated. Over half of all older and socioeconmically disadvantaged T2DM patients captured in this longitudinal claims database will be prescribed a second anti-hyperglycaemic agent within 5 years of their first drug therapy. Several factors can predict the risk of prescription of add-on therapy, and these should be considered when prescribing medications to treat T2DM.

Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis.

PURPOSE: Drug-drug interactions (DDIs) are often avoidable and, if undetected, can lead to patient harm. This review aimed to determine the prevalence of potential DDIs (pDDIs), clinically relevant DDIs (DDIs that could lead to measurable patient harm, taking into account the patient's individual clinical profile) and DDIs that resulted in actual patient harm during hospitalisation. METHOD: Four databases were scanned for English papers published from 2000 to 2016. Papers that reported prevalence of DDIs in the outpatient setting, at admission or discharge, involving only specific drugs, or in specific disease populations or age groups were excluded. RESULTS: Twenty-seven papers met the inclusion criteria and were graded for quality using the Critical Appraisal Skills Programme (CASP) cohort study checklist. Ten papers were rated as 'poor', 14 as 'fair' and only three papers as 'good'. Overall, the meta-analysis revealed that 33% of general patients and 67% of intensive care patients experienced a pDDI during their hospital stay. It was not possible to determine the prevalence of clinically relevant DDIs or DDIs that resulted in actual patient harm as data on these categories were limited. Of the very few studies that reported on harm, only a small proportion of DDIs were found to have resulted in actual patient harm. CONCLUSIONS: Standardisation of DDI definitions and research methods are required to allow meaningful prevalence rates to be obtained and compared. Studies that go further than measuring pDDIs are critically needed to determine the impact of DDIs on patient safety.

A dosing algorithm for metformin based on the relationships between exposure and renal clearance of metformin in patients with varying degrees of kidney function.

PURPOSE: The aims of this study were to investigate the relationship between metformin exposure, renal clearance (CLR), and apparent non-renal clearance of metformin (CLNR/F) in patients with varying degrees of kidney function and to develop dosing recommendations. METHODS: Plasma and urine samples were collected from three studies consisting of patients with varying degrees of kidney function (creatinine clearance, CLCR; range, 14-112 mL/min). A population pharmacokinetic model was built (NONMEM) in which the oral availability (F) was fixed to 0.55 with an estimated inter-individual variability (IIV). Simulations were performed to estimate AUC0-τ, CLR, and CLNR/F. RESULTS: The data (66 patients, 327 observations) were best described by a two-compartment model, and CLCR was a covariate for CLR. Mean CLR was 17 L/h (CV 22%) and mean CLNR/F was 1.6 L/h (69%).The median recovery of metformin in urine was 49% (range 19-75%) over a dosage interval. When CLR increased due to improved renal function, AUC0-τ decreased proportionally, while CLNR/F did not change with kidney function. Target doses (mg/day) of metformin can be reached using CLCR/3 × 100 to obtain median AUC0-12 of 18-26 mg/L/h for metformin IR and AUC0-24 of 38-51 mg/L/h for metformin XR, with Cmax < 5 mg/L. CONCLUSIONS: The proposed dosing algorithm can be used to dose metformin in patients with various degrees of kidney function to maintain consistent drug exposure. However, there is still marked IIV and therapeutic drug monitoring of metformin plasma concentrations is recommended.

Trends in metformin utilisation and dose appropriateness in Australia.

PURPOSE: The study aimed to (1) determine the trends in the utilisation of metformin in Australia, (2) determine the appropriateness of metformin dosing in an Australian teaching hospital and (3) gather the opinions of prescribers on the relationship between metformin dose and renal function. METHODS: National prescription data between 1990 and 2012 were accessed. A retrospective audit (2008-2012) of metformin doses and patient renal function (20 % random sample of all in-patients prescribed metformin) was conducted at St Vincent's Hospital (SVH), Sydney. Prescribers of metformin were interviewed (semi-structured; consultants at SVH) or surveyed (Australian endocrinologists) to gather their understanding of metformin dosing in relation to renal function. RESULTS: Metformin utilisation increased fivefold nationally between 1995 and 2012. Metformin tended to be under-dosed in SVH patients with normal renal function (83.5 %) and over-dosed in patients with impaired renal function (estimated glomerular filtration rate (eGFR) <30 mL/min, 50 %). Consultants indicated that metformin doses needed to be reduced in renal impairment. Most endocrinologists (61 %) were comfortable prescribing metformin down to eGFRs around 30 mL/min. CONCLUSION: The use of metformin increased greatly over the period of the study. Metformin is prescribed frequently for patients with eGFR values below the minimal level approved in the product label (60 mL/min). While prescribers expressed their understanding of the need to reduce metformin doses in patients with renal impairment, we found that metformin doses were higher than appropriate in patients with impaired renal function. Metformin may be used safely when renal function is poor provided dosage is appropriately reduced.

Evaluation of hospital-wide computerised decision support in an intensive care unit: an observational study.

We conducted an observational study with interviews in a 12-bed general/neurological intensive care unit (ICU) at a teaching hospital in Sydney, Australia, to determine whether hospital-wide computerised decision support (CDS) embedded in an electronic prescribing system is used and perceived as useful by doctors in an ICU setting. Twenty doctors were shadowed by the observer while on ward rounds (33.6 hours) and non-ward rounds (28 hours) in the ICU. These doctors were also interviewed to explore views of CDS. We found that computerised alerts were triggered frequently in the ICU (n=166, in 59% of orders), less than half of the alerts were read by doctors and only four alerts resulted in a medication order being changed. Pre-written orders were utilised frequently, however reference material was rarely accessed. Interviews with doctors revealed a willingness to use CDS features; however the primary barrier to use was lack of customisation for the ICU setting. Doctors working in the ICU triggered a high number of alerts when prescribing, 40% more alerts than doctors working on general wards of the same hospital. Certain procedures in place in the ICU (e.g. daily microbiology ward rounds) made many alerts redundant in this setting. Lack of customisation for the ICU led to dissatisfaction with CDS and infrequent use of some CDS features.

A time and motion study of junior doctor work patterns on the weekend: a potential contributor to the weekend effect?

BACKGROUND: Patients admitted to hospital on weekends have a greater risk of mortality compared to patients admitted on weekdays. Junior medical officers (JMO) make up the majority of medical staff on weekends. No previous study has quantified JMO work patterns on weekends. AIM: To describe and quantify JMO work patterns on weekends and compare them with patterns previously observed during the week. METHODS: Observational time and motion study of JMO working weekends using the Work Observation Method by Activity Timing (WOMBAT; Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia) software. Descriptive statistics were used to determine the proportion of total observed time spent in tasks. RESULTS: Weekend JMO predominately spent time in indirect care (32.0%), direct care (23.0%) and professional communication (22.1%). JMO spent 20.9% of time multitasking and were interrupted, on average, every 9 min. Weekend JMO spent significantly more time in direct care compared with weekdays (13.0%; P < 0.001) and nights (14.3%; P < 0.001). Weekend JMO spent significantly less time on breaks (8.5%), with less than 1 h in an 11-h shift, compared with JMO during weekdays (16.4%; P = 0.004) and nights (27.6%; P = <0.001). Weekend JMO were interrupted at a higher rate (6.6/h) than on weekdays (rate ratio (RR) 2.9, 95% confidence intervals (CI) 2.6, 3.3) or nights (RR 5.1, 95% CI 4.2, 6.1). Multitasking on weekends (20.9%) was comparable to weekdays (18.9%; P = 0.19) but significantly higher than nights (6.4%; P = <0.001). CONCLUSION: On weekends, JMO had few breaks, were interrupted frequently and engaged in high levels of multitasking. This pattern of JMO work could be a potential contributing factor to the weekend effect in terms of JMO abilities to respond safely and adequately to care demands.

Understanding and improving the use of allopurinol in a teaching hospital.

BACKGROUND: An emphasis on renal function in deciding maintenance doses of allopurinol to prevent allopurinol hypersensitivity has resulted in ineffective prevention of attacks of gout. New therapeutic guidelines for gout have shifted the focus back to titrating maintenance doses to reach a serum uric acid (SUA) concentration target of ≤ 0.36 mmol/L. AIMS: To examine trends in the prescribing of allopurinol in a teaching hospital and their concordance with the new guidelines for gout management, and to explore prescribers' approaches and attitudes to the use of allopurinol. METHODS: An audit was conducted of all inpatients prescribed allopurinol at a teaching hospital between January 2008 and December 2012. Allopurinol dose, SUA, serum creatinine concentrations and estimated glomerular filtration rates were extracted from the hospital databases. Doctors from medical units who regularly prescribed allopurinol were interviewed. RESULTS: The allopurinol dose prescribed in gout patients most commonly was a continuation of the pre-admission dosage. Dosage change during admission was rarely observed. Dosages reflected a consideration of renal function. SUA concentrations were measured in only 21% (n = 269) of gout patients. Prescriber interviews (n = 12) reflected adequate knowledge regarding allopurinol use, but most maintained that the primary care setting was more suitable for the management of dose titration in gout. CONCLUSIONS: SUA concentrations were not routinely measured in the majority of admitted gout patients taking allopurinol. Without SUA measurements and allopurinol dose titration, patients with SUA > 0.36 mmol/L are at increased risk for acute attacks of gout in hospital.

Does the availability of therapeutic drug monitoring, computerised dose recommendation and prescribing decision support services promote compliance with national gentamicin prescribing guidelines?

BACKGROUND: Gentamicin is an aminoglycoside antibiotic that is highly effective in treating Gram-negative infections, but inappropriate use leads to toxicity. In 2010, the Australian Therapeutic Guidelines (Antibiotic) were revised to recommend the use of computerised methods to individualise dosing of gentamicin and optimise therapy, rather than traditional nomogram approaches. AIM: To determine whether gentamicin prescribing was compliant with the Australian Therapeutic Guidelines, version 14 (2010) in a setting where computerised dose recommendation resources and computerised decision support were available, and to determine why the resources were effective or ineffective in achieving compliance to guidelines. METHODS: During phase 1, a retrospective audit of gentamicin prescribing from 1 January 2012 to 31 December 2012 (n = 826) at a 320-bed teaching hospital in Sydney was undertaken. In phase 2, 12 doctors from specialties with high-volume prescribing of gentamicin were interviewed. RESULTS: Intravenous gentamicin was used in 545 cases, 81% of which were for short-term therapy (≤48 h). Doctors feared inducing toxicity in patients, but limited the dose rather than altering the dosing interval according to renal function. Of the 'continued' dosing cases, 55% went unmonitored and the computerised dose recommendation service was rarely used. Doctors were unaware of its availability despite electronic alerts accompanying prescriptions of gentamicin. CONCLUSIONS: In comparison with the national guidelines, there was significant under-dosing and monitoring practices were haphazard. Computerised electronic alerts were ineffective in informing users. To improve prescribing practices, we recommend exploring alternative computerised decision support approaches (e.g. pre-written orders) and more pervasive and persuasive implementation strategies.

iPad use at the bedside: a tool for engaging patients in care processes during ward rounds?

BACKGROUND: Previous work has examined the impact of technology on information sharing and communication between doctors and patients in general practice consultations, but very few studies have explored this in hospital settings. AIMS: To assess if, and how, senior clinicians use an iPad to share information (e.g. patient test results) with patients during ward rounds and to explore patients' and doctors' experiences of information sharing events. METHODS: Ten senior doctors were shadowed on ward rounds on general wards during interactions with 525 patients over 77.3 h, seven senior doctors were interviewed and 180 patients completed a short survey. RESULTS: Doctors reported that information sharing with patients is critical to the delivery of high-quality healthcare, but were not seen to use the iPad to share information with patients on ward rounds. Patients did not think the iPad had impacted on their engagement with doctors on rounds. Ward rounds were observed to follow set routines and patient interactions were brief. CONCLUSIONS: Although the iPad potentially creates new opportunities for information sharing and patient engagement, the ward round may not present the most appropriate context for this to be done.

Shedding light on junior doctors' work practices after hours.

BACKGROUND: It is imperative to understand the current work practices of hospital personnel to inform efforts and secure resources towards the improvement of hospital systems. Research examining doctors' work during night-shifts is limited. AIM: To describe and quantify the night-shift work practices of junior doctors. METHODS: An observational time and motion study was conducted. Eight resident doctors in four general wards were observed for 96 h during night shifts (Monday-Friday, 2200-0800). RESULTS: Doctors spent the highest proportion (28%; 95% CI 21-35) of their time performing social/personal tasks (e.g. sleeping, eating) and indirect care (24%; 95% CI 22-25) (e.g. reviewing notes, ordering tests). Work-related discussion comprised 15% (95% CI 13-17), and most took place at the beginning of the night. Medication-related tasks consumed a small proportion of time (4%; 95% CI 3-4) but attracted a higher level of multitasking and interruptions than most other tasks. On average, 2 h of every shift were spent at a computer and 1.3 h with patient notes. Doctors spent 72% of the night-shift alone, multitasked 6.4% of the time and were interrupted, on average, once every 46 min. CONCLUSIONS: This study provides new data about junior doctors' work at night. Relative to doctors during the day, greater proportions of time were devoted to social/personal tasks (including sleep) and indirect care, but a similar proportion to direct care. Multitasking and interruptions were minimal. Computer activities were an integral part of work. Handovers were observed at the beginning but not the completion of the night shift, which may have implications for patient safety.

A cross-sectional study of hydroxychloroquine concentrations and effects in people with systemic lupus erythematosus.

BACKGROUND/AIMS: To investigate the pharmacokinetics of hydroxychloroquine (HCQ) and relationships of HCQ concentration with disease activity variables (concentration-effect relationships) in patients with systemic lupus erythematosus (SLE). METHODS: HCQ concentration and disease activity were measured at a single time point in 60 patients with SLE receiving HCQ for at least 6 months. Some retrospective objective data on disease activity prior to HCQ commencement were available. Correlations were sought between HCQ blood concentrations and disease variables (Systemic Lupus Activity Measure (SLAM) scores, joint scores, morning stiffness, pain, well-being, general improvement, other medication use (including corticosteroids), and physician and patient assessments). Blood HCQ concentrations were also dichotomised into 'more' and 'less/no' disease activity, and mean blood concentrations in the two groups for each disease activity measure were compared. RESULTS: The pharmacokinetics (dose-concentration relationships) of HCQ were highly variable in people with SLE. Apparent total clearance of HCQ from blood was 316 (± 319) mL/min (mean (± standard deviation). There was no correlation between SLAM score, patient global assessment or physician global assessment, and blood HCQ concentrations (r(2) = 0.2, 0.04 and 0.13, respectively; P > 0.05). However, during HCQ therapy, 64% (14 of 22 patients) had a reduction in prednisolone dose of 50% or more compared with pre-HCQ therapy. CONCLUSIONS: No significant concentration-effect relationship for HCQ in the treatment of SLE was observed perhaps because consistently high enough blood HCQ concentrations were not achieved. Pharmacokinetic variability led to a wide range of blood HCQ concentrations. A concentration-targeting study for HCQ in SLE would be timely.

Comparing dose prediction software used to manage gentamicin dosing.

BACKGROUND: Current Australian guidelines recommend initiating directed therapy of gentamicin if administration exceeds 48 h. Directed doses of gentamicin require the monitoring of plasma concentrations of gentamicin to determine the 24-h area under the time course of plasma gentamicin concentrations (AUC) and a dosage prediction program, for example TCIWorks or Aladdin. However, doses calculated by such programs have not been compared with an established program. AIM: To compare the directed dosage of gentamicin calculated by TCIWorks, Aladdin and an Excel-based program, with an established program, Abbottbase. METHODS: Peak and trough plasma concentrations after the first and second administered doses of gentamicin were available from three patient groups (n = 20-23) with varying creatinine clearances (<40, 40-80, >80 mL/min). The directed dose needed to produce 24-h AUC values of 80 mg.h/L was calculated using each program. RESULTS: There was a strong correlation between the directed doses predicted by each of the three programs compared with Abbottbase, following the first administered dose (r(2) > 0.97, P < 0.0001). The mean ratio (90% confidence intervals) of these directed doses of the gentamicin were: TCIWorks/Abbottbase 106% (105-107%), Aladdin/Abbottbase 102% (101-103%) and Excel/Abbottbase 108% (106-109%). The correlations and dose ratios were also similar when comparisons were made following the second administered dose. For each of the three renal function groups, all programs yielded similar directed doses. CONCLUSIONS: The four programs used in the calculation of directed doses of gentamicin yielded similar results. Any would be suitable for use in clinical practice.

Formoterol, a highly ß2-selective agonist, increases energy expenditure and fat utilisation in men.

BACKGROUND: The sympathetic nervous system regulates energy metabolism via ß-adrenoreceptors. Therapeutic exploitation of previous ß2-adrenegic agonists for metabolic benefits has been hindered by cross stimulation of cardiac ß1-adrenoceptor, causing tachycardia. Formoterol is a novel highly ß2-selective adrenergic agonist and holds promise as a ß2-agonist that could impart selective beneficial metabolic effects. OBJECTIVE: To investigate the metabolic effects of formoterol on energy and substrate metabolism. PARTICIPANTS: Healthy volunteers. DESIGN: (1) Dose-finding study, step-wise incremental design of weekly administration of 80, 160 and 320 µg daily of formoterol in four subjects and, (2) metabolic study, an open-label metabolic evaluation of 1-week treatment in eight men using a dose determined from (1). MAIN OUTCOME: Resting energy expenditure (EE), diet-induced thermogenesis (DIT) and fat oxidation (Fox) using indirect calorimetry, heart rate and plasma non-esterified fatty acid (NEFA) levels. RESULTS: In the dose-finding study, all three doses increased resting EE and Fox with the 320 µg dose significantly increasing heart rate. In the metabolic study, the selected 160 µg daily dose increased resting EE by 13 ± 2% (P<0.001) and Fox by 23 ± 4% (P<0.01), but not DIT. Plasma NEFA levels rose by 16 ± 2% (P<0.01). Heart rate did not change significantly. Out of the eight subjects, six reported tremor and palpitation, two lost appetite and one suffered from insomnia. CONCLUSIONS: At a dose of 160 µg per day, formoterol increases resting EE and fat utilization without inducing tachycardia. From this first metabolic evaluation in humans, we conclude that formoterol imparts beneficial metabolic changes that may be exploited for therapy of obesity.

Audit and feedback of antibiotic use: utilising electronic prescription data.

BACKGROUND: There is now little doubt that improving antimicrobial use is necessary for the containment of resistance. OBJECTIVE: To determine whether providing individualised feedback to doctors about their recent compliance with the hospital's antibiotic policy improves compliance with the policy. METHODS: This study was conducted at a teaching hospital in Sydney, Australia. Computerised alerts integrated into the electronic prescribing system (ePS) inform prescribers of the local antibiotic policy. We utilised prescribing data extracted from the ePS for 'audit and feedback'. Thirty-six prescribers were sent feedback letters via email. We also interviewed doctors who had received letters to explore their views of the feedback and the policy in general. RESULTS: There was no significant change in compliance with the policy following implementation of the feedback intervention (0% compliant vs 11.9%; p = 0.07). Several problems with the policy and the approval process were identified by researchers during auditing and by prescribers during interviews. Some problems identified made it difficult to accurately assess compliance and for doctors to comply with the policy. CONCLUSION: Our intervention did not result in improved compliance with the antibiotic policy but revealed practical problems with the policy and approval process that had not been identified prior to the trial. Greater support for the policy by senior doctors and the assignment of more clearly defined roles and responsibilities associated with antibiotic use and approval may result in improved compliance. Harnessing the full potential of technology would streamline the antimicrobial approval process and allow more efficient and reliable monitoring of antibiotic use and compliance. Many of the problems we identified are generic issues of importance to all organisations seeking to integrate antimicrobial stewardship into ePS.

Metformin therapy in patients with chronic kidney disease.

Metformin therapy is limited in patients with chronic kidney disease (CKD) due to the potential risk of lactic acidosis. This open-label observational study investigated metformin and lactate concentrations in patients with CKD (n = 22; creatinine clearances 15-40 ml/min) and in two dialysed patients. Patients were prescribed a range of metformin doses (250-2000 mg daily) and metformin concentrations were compared with data from healthy subjects (scaled to 1500 mg twice daily). A subset of patients (n = 7) was controlled on low doses of metformin (250 or 500 mg daily). No correlation between metformin and lactate concentrations was observed. Three patients had high lactate concentrations (>2.7 mmol/l) and two had high metformin concentrations (3-5 mg/l), but none had any symptoms of lactic acidosis. Reducing metformin dosage and monitoring metformin concentrations will allow the safe use of metformin in CKD, provided that renal function is stable.