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This randomized trial assessed whether urate-lowering treatment with allopurinol could attenuate eGFR decline in at-risk patients with stage 3 or 4 chronic kidney disease. Allopurinol did not slow the decline in eGFR as compared with placebo.

Background and Objectives Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions. Methods The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020. Results Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug–drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies). Conclusion The effect of clinically significant drug–drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

Regular paracetamol is the recommended first-line analgesic for acute low-back pain; however, no high-quality evidence supports this recommendation. We aimed to assess the efficacy of paracetamol taken regularly or as-needed to improve time to recovery from pain, compared with placebo, in patients with low-back pain. We did a multicentre, double-dummy, randomised, placebo controlled trial across 235 primary care centres in Sydney, Australia, from Nov 11, 2009, to March 5, 2013. We randomly allocated patients with acute low-back pain in a 1:1:1 ratio to receive up to 4 weeks of regular doses of paracetamol (three times per day; equivalent to 3990 mg paracetamol per day), as-needed doses of paracetamol (taken when needed for pain relief; maximum 4000 mg paracetamol per day), or placebo. Randomisation was done according to a centralised randomisation schedule prepared by a researcher who was not involved in patient recruitment or data collection. Patients and staff at all sites were masked to treatment allocation. All participants received best-evidence advice and were followed up for 3 months. The primary outcome was time until recovery from low-back pain, with recovery defined as a pain score of 0 or 1 (on a 0–10 pain scale) sustained for 7 consecutive days. All data were analysed by intention to treat. This study is registered with the Australian and New Zealand Clinical Trial Registry, number ACTN 12609000966291. 550 participants were assigned to the regular group (550 analysed), 549 were assigned to the as-needed group (546 analysed), and 553 were assigned to the placebo group (547 analysed). Median time to recovery was 17 days (95% CI 14–19) in the regular group, 17 days (15–20) in the as-needed group, and 16 days (14–20) in the placebo group (regular vs placebo hazard ratio 0·99, 95% CI 0·87–1·14; as-needed vs placebo 1·05, 0·92–1·19; regular vs as-needed 1·05, 0·92–1·20). We recorded no difference between treatment groups for time to recovery (adjusted p=0·79). Adherence to regular tablets (median tablets consumed per participant per day of maximum 6; 4·0 [IQR 1·6–5·7] in the regular group, 3·9 [1·5–5·6] in the as-needed group, and 4·0 [1·5–5·7] in the placebo group), and number of participants reporting adverse events (99 [18·5%] in the regular group, 99 [18·7%] in the as-needed group, and 98 [18·5%] in the placebo group) were similar between groups. Our findings suggest that regular or as-needed dosing with paracetamol does not affect recovery time compared with placebo in low-back pain, and question the universal endorsement of paracetamol in this patient group. National Health and Medical Research Council of Australia and GlaxoSmithKline Australia.

In a randomized trial involving patients with sciatica, the antiepileptic drug pregabalin, at a dose of up to 600 mg per day, was no more effective than placebo in reducing pain or disability over the course of 8 weeks and resulted in a higher incidence of adverse events. Sciatica is characterized by radiating posterior or posterolateral leg pain, which is sometimes accompanied by back pain, sensory loss, weakness, or reflex abnormalities. 1 – 3 Few clinical guidelines for the treatment of sciatica exist, and evidence regarding effective medical treatments is limited. 2 , 3 Treatment with pregabalin (Lyrica, Pfizer) has been shown to be effective in reducing some types of neuropathic pain, including postherpetic neuralgia and diabetic peripheral neuropathy, 4 , 5 as well as allodynia and hyperalgesia from several conditions, 6 – 8 and some guidelines recommend pregabalin for the treatment of pain with neuropathic features. 5 Pregabalin therefore represents a potential treatment for sciatica. Its . . .

Considerable investments are being made in commercial electronic prescribing systems (e-prescribing) in many countries. Few studies have measured or evaluated their effectiveness at reducing prescribing error rates, and interactions between system design and errors are not well understood, despite increasing concerns regarding new errors associated with system use. This study evaluated the effectiveness of two commercial e-prescribing systems in reducing prescribing error rates and their propensities for introducing new types of error. We conducted a before and after study involving medication chart audit of 3,291 admissions (1,923 at baseline and 1,368 post e-prescribing system) at two Australian teaching hospitals. In Hospital A, the Cerner Millennium e-prescribing system was implemented on one ward, and three wards, which did not receive the e-prescribing system, acted as controls. In Hospital B, the iSoft MedChart system was implemented on two wards and we compared before and after error rates. Procedural (e.g., unclear and incomplete prescribing orders) and clinical (e.g., wrong dose, wrong drug) errors were identified. Prescribing error rates per admission and per 100 patient days; rates of serious errors (5-point severity scale, those ≥3 were categorised as serious) by hospital and study period; and rates and categories of postintervention \"system-related\" errors (where system functionality or design contributed to the error) were calculated. Use of an e-prescribing system was associated with a statistically significant reduction in error rates in all three intervention wards (respectively reductions of 66.1% [95% CI 53.9%-78.3%]; 57.5% [33.8%-81.2%]; and 60.5% [48.5%-72.4%]). The use of the system resulted in a decline in errors at Hospital A from 6.25 per admission (95% CI 5.23-7.28) to 2.12 (95% CI 1.71-2.54; p<0.0001) and at Hospital B from 3.62 (95% CI 3.30-3.93) to 1.46 (95% CI 1.20-1.73; p<0.0001). This decrease was driven by a large reduction in unclear, illegal, and incomplete orders. The Hospital A control wards experienced no significant change (respectively -12.8% [95% CI -41.1% to 15.5%]; -11.3% [-40.1% to 17.5%]; -20.1% [-52.2% to 12.4%]). There was limited change in clinical error rates, but serious errors decreased by 44% (0.25 per admission to 0.14; p = 0.0002) across the intervention wards compared to the control wards (17% reduction; 0.30-0.25; p = 0.40). Both hospitals experienced system-related errors (0.73 and 0.51 per admission), which accounted for 35% of postsystem errors in the intervention wards; each system was associated with different types of system-related errors. Implementation of these commercial e-prescribing systems resulted in statistically significant reductions in prescribing error rates. Reductions in clinical errors were limited in the absence of substantial decision support, but a statistically significant decline in serious errors was observed. System-related errors require close attention as they are frequent, but are potentially remediable by system redesign and user training. Limitations included a lack of control wards at Hospital B and an inability to randomize wards to the intervention.

Febuxostat is a xanthine oxidoreductase inhibitor that has been developed to treat chronic gout. In healthy subjects, the pharmacokinetic parameters of febuxostat after multiple oral dose administration include an oral availability of about 85 %, an apparent oral clearance (CL/ F ) of 10.5 ± 3.4 L/h and an apparent volume of distribution at steady state ( V ss / F ) of 48 ± 23 L. The time course of plasma concentrations follows a two-compartment model. The initial half-life ( t ½ ) is approximately 2 h and the terminal t ½ determined at daily doses of 40 mg or more is 9.4 ± 4.9 h. Febuxostat is administered once daily. The maximum (peak) plasma concentrations are approximately 100-fold greater than the trough concentrations. Consequently, there is no significant accumulation of the drug during multiple dose administration. There are few data on the pharmacokinetics of febuxostat in patients with gout. While the pharmacokinetic parameters are not affected by mild to moderate hepatic impairment, there is no consensus on whether renal impairment has any effect on the pharmacokinetics of febuxostat. Febuxostat is extensively metabolised by oxidation (approximately 35 %) and acyl glucuronidation (up to 40 %); febuxostat acyl glucuronides are cleared by the kidney. In healthy subjects treated with multiple doses of febuxostat 10–240 mg, the concentrations of serum urate are reduced by a maximum of about 80 %. The percentage reduction in the concentrations of serum urate is slightly less in gouty patients than in healthy subjects.

Background The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. Methods A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. Results The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. Conclusions This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.

BackgroundPain medicines are widely prescribed by general practitioners (GPs) when managing people with low back pain (LBP), but little is known about what drives decisions to prescribe these medicines.ObjectivesThe aim of this study was to investigate what influences GPs’ decision to prescribe pain medicines for LBP.DesignQualitative study with in-depth interviews.SettingAustralian primary care.ParticipantsWe interviewed 25 GPs practising in Australia experienced in managing LBP (mean (SD) age 53.4 (9.1) years, mean (SD) years of experience: 24.6 (9.3), 36% female). GPs were provided three vignettes describing common LBP presentations (acute exacerbation of chronic LBP, subacute sciatica and chronic LBP) and were asked to think aloud how they would manage the cases described in the vignettes.Data analysisWe summarised GP’s choices of pain medicines for each vignette using content analysis and used framework analysis to investigate factors that affected GP’s decision-making.ResultsGPs more commonly prescribed opioid analgesics. Anticonvulsants and antidepressants were also commonly prescribed depending on the presentation described in the vignette. GP participants made decisions about what pain medicines to prescribe for LBP largely based on previous experiences, including their own personal experiences of LBP, rather than guidelines. The choice of pain medicine was influenced by a range of clinical factors, more commonly the patient’s pathoanatomical diagnosis. While many adhered to principles of judicious use of pain medicines, polypharmacy scenarios were also common. Concerns about drug-seeking behaviour, adverse effects, stigma around opioid analgesics and pressure from regulators also shaped their decision-making process.ConclusionsWe identified several aspects of decision-making that help explain the current profile of pain medicines prescribed for LBP by GPs. Themes identified by our study could inform future implementation strategies to improve the quality use of medicines for LBP.

IntroductionThere is a clear need for improved care quality and quality monitoring in aged care. Aged care providers collect an abundance of data, yet rarely are these data integrated and transformed in real-time into actionable information to support evidence-based care, nor are they shared with older people and informal caregivers. This protocol describes the co-design and testing of a dashboard in residential aged care facilities (nursing or care homes) and community-based aged care settings (formal care provided at home or in the community). The dashboard will comprise integrated data to provide an ‘at-a-glance’ overview of aged care clients, indicators to identify clients at risk of fall-related hospitalisations and poor quality of life, and evidence-based decision support to minimise these risks. Longer term plans for dashboard implementation and evaluation are also outlined.MethodsThis mixed-method study will involve (1) co-designing dashboard features with aged care staff, clients, informal caregivers and general practitioners (GPs), (2) integrating aged care data silos and developing risk models, and (3) testing dashboard prototypes with users. The dashboard features will be informed by direct observations of routine work, interviews, focus groups and co-design groups with users, and a community forum. Multivariable discrete time survival models will be used to develop risk indicators, using predictors from linked historical aged care and hospital data. Dashboard prototype testing will comprise interviews, focus groups and walk-through scenarios using a think-aloud approach with staff members, clients and informal caregivers, and a GP workshop.Ethics and disseminationThis study has received ethical approval from the New South Wales (NSW) Population & Health Services Research Ethics Committee and Macquarie University’s Human Research Ethics Committee. The research findings will be presented to the aged care provider who will share results with staff members, clients, residents and informal caregivers. Findings will be disseminated as peer-reviewed journal articles, policy briefs and conference presentations.

Introduction Gout management remains suboptimal despite safe and effective urate‐lowering therapy. Self‐monitoring of urate may improve gout management, however, the acceptability of urate self‐monitoring by people with gout is unknown. The aim of this study was to explore the experiences of urate self‐monitoring in people with gout. Methods Semistructured interviews were conducted with people taking urate‐lowering therapy (N = 30) in a 12‐month trial of urate self‐monitoring in rural and urban Australia. Interviews covered the experience of monitoring and its effect on gout self‐management. Deidentified transcripts were analysed thematically. Results Participants valued the ability to self‐monitor and gain more understanding of urate control compared with the annual monitoring ordered by their doctors. Participants indicated that self‐monitoring at home was easy, convenient and informed gout self‐management behaviours such as dietary modifications, hydration, exercise and medication routines. Many participants self‐monitored to understand urate concentration changes in response to feeling a gout flare was imminent or whether their behaviours, for example, alcohol intake, increased the risk of a gout flare. Urate concentrations were shared with doctors mainly when they were above target to seek management support, and this led to allopurinol dose increases in some cases. Conclusion Urate self‐monitoring was viewed by people with gout as convenient and useful for independent management of gout. They believed self‐monitoring achieved better gout control with a less restricted lifestyle. Urate data was shared with doctors at the patient's discretion and helped inform clinical decisions, such as allopurinol dose changes. Further research on implementing urate self‐monitoring in routine care would enable an evaluation of its impact on medication adherence and clinical outcomes, as well as inform gout management guidelines. Patient or Public Contribution One person with gout, who was not a participant, was involved in the study design by providing feedback and pilot testing the semistructured interview guide. In response to their feedback, subsequent modifications to the interview guide were made to improve the understandability of the questions from a patient perspective. No additional questions were suggested.