Population pharmacokinetic modelling of febuxostat in healthy subjects and people with gout

AIMS

To investigate and characterise the pharmacokinetics of febuxostat and the effect of the covariates of renal function and body size descriptors on the pharmacokinetics of the drug.

METHODS

Blood samples (n = 239) were collected using sparse and rich sampling strategies from healthy (n = 9) and gouty (n = 29) subjects. Febuxostat plasma concentrations were measured by a validated high-performance liquid chromatography method. Population pharmacokinetic analysis was performed using NONMEM. A common variability on bioavailability (FVAR) approach was used to test the effect of fed status on absorption parameters. Covariates were modelled using a power model.

RESULTS

The time course of the plasma concentrations of febuxostat is best described by a two-compartment model. In the final model, the population mean for apparent clearance (CL/F), apparent central volume of distribution (Vc/F), apparent peripheral volume of distribution (Vp/F), absorption rate constant (ka) and apparent intercompartmental clearance (Q/F) were 6.91 l h^-1 , 32.8 l, 19.4 l, 3.6 h^-1 and 1.25 l h^-1 , respectively. The population parmater variability (coefficient of variation) for CL/F, Vc/F and Vp/F were 13.6, 22 and 19.5%, respectively. Food reduced the relative biovailability and ka by 67% and 87%, respectively. Renal function, as assessed by creatinine clearance, was a significant covariate for CL/F while body mass index was a significant covariate for Vc/F.

CONCLUSIONS

Renal function and body mass index were significant covariates. Further work is warranted to investigate the clinical relevance of these results, notably as renal impairment and obesity are common occurrences in people with gout.

Defining core patient descriptors for perforated peptic ulcer research: international Delphi

BACKGROUND

Perforated peptic ulcer (PPU) remains a common condition globally with significant morbidity and mortality. Previous work has demonstrated variation in reporting of patient characteristics in PPU studies, making comparison of studies and outcomes difficult. The aim of this study was to standardize the reporting of patient characteristics, by creating a core descriptor set (CDS) of important descriptors that should be consistently reported in PPU research.

METHODS

Candidate descriptors were identified through systematic review and stakeholder proposals. An international Delphi exercise involving three survey rounds was undertaken to obtain consensus on key patient characteristics for future research. Participants rated items on a scale of 1-9 with respect to their importance. Items meeting a predetermined threshold (rated 7-9 by over 70 per cent of stakeholders) were included in the final set and ratified at a consensus meeting. Feedback was provided between rounds to allow refinement of ratings.

RESULTS

Some 116 clinicians were recruited from 29 countries. A total of 63 descriptors were longlisted from the literature, and 27 were proposed by stakeholders. After three survey rounds and a consensus meeting, 27 descriptors were included in the CDS. These covered demographic variables and co-morbidities, risk factors for PPU, presentation and pathway factors, need for organ support, biochemical parameters, prognostic tools, perforation details, and surgical history.

CONCLUSION

This study defines the core descriptive items for PPU research, which will allow more robust synthesis of studies.

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.

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.

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 &lt;25% and &lt;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.

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.

The safety and pharmacokinetics of metformin in patients with chronic liver disease

BACKGROUND

The FDA approved 'label' for metformin lists hepatic insufficiency as a risk for lactic acidosis. Little evidence supports this warning.

AIMS

To investigate the safety and pharmacokinetics of metformin in patients with chronic liver disease (CLD).

METHODS

Chronic liver disease patients with and without type 2 diabetes mellitus (T2DM) were studied by a cross-sectional survey of patients already prescribed metformin (n = 34), and by a prospective study where metformin (500 mg, immediate release, twice daily) for up to 6 weeks was prescribed (n = 24). Plasma metformin and lactate concentrations were monitored. Individual pharmacokinetics were obtained and compared to previously published values from healthy and T2DM populations without CLD.

RESULTS

All plasma metformin and lactate concentrations remained below the putative safety thresholds (metformin, 5 mg/L; lactate, 5 mmol/L). Lactate concentrations were unrelated to average steady-state metformin concentrations. In patients with CLD, T2DM was associated with higher plasma lactate concentrations (48% higher than those without T2DM, P < 0.0001). CLD patients with cirrhosis had 23% higher lactate concentrations than those without cirrhosis (P = 0.01). The pharmacokinetics of metformin in CLD patients were similar to patients with T2DM and no liver disease. The ratio of apparent metformin clearance (CL_Met /F) to creatinine clearance was marginally lower in CLD patients compared to healthy subjects (median, interquartile range; 12.6, 9.5-15.9 vs 14.9, 13.4-16.4; P = 0.03).

CONCLUSIONS

The pharmacokinetics of metformin are not altered sufficiently in CLD patients to raise concerns regarding unsafe concentrations of metformin. There were no unsafe plasma lactate concentrations observed in CLD patients receiving metformin (ACTRN12619001292167; ACTRN12619001348145).

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.

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.

Clinical Pharmacokinetics and Pharmacodynamics of Febuxostat

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.

Search for CP Violation and Measurement of the Branching Fraction in the Decay D^{0}→K_{S}^{0}K_{S}^{0}

We report a study of the decay D^{0}→K_{S}^{0}K_{S}^{0} using 921  fb^{-1} of data collected at or near the ϒ(4S) and ϒ(5S) resonances with the Belle detector at the KEKB asymmetric energy e^{+}e^{-} collider. The measured time-integrated CP asymmetry is A_{CP}(D^{0}→K_{S}^{0}K_{S}^{0})=(-0.02±1.53±0.02±0.17)%, and the branching fraction is B(D^{0}→K_{S}^{0}K_{S}^{0})=(1.321±0.023±0.036±0.044)×10^{-4}, where the first uncertainty is statistical, the second is systematic, and the third is due to the normalization mode (D^{0}→K_{S}^{0}π^{0}). These results are significantly more precise than previous measurements available for this mode. The A_{CP} measurement is consistent with the standard model expectation.

Individualising the dose of allopurinol in patients with gout

AIMS

The aims of the study were to: 1) determine if a plasma oxypurinol concentration-response relationship or an allopurinol dose-response relationship best predicts the dose requirements of allopurinol in the treatment of gout; and 2) to construct a nomogram for calculating the optimum maintenance dose of allopurinol to achieve target serum urate (SU) concentrations.

METHODS

A nonlinear regression analysis was used to examine the plasma oxypurinol concentration- and allopurinol dose-response relationships with serum urate. In 81 patients (205 samples), creatinine clearance (CL_CR ), concomitant diuretic use and SU concentrations before (U_P ) and during (U_T ) treatment were monitored across a range of allopurinol doses (D, 50-700 mg daily). Plasma concentrations of oxypurinol (C) were measured in 47 patients (98 samples). Models (n = 47 patients) and predictions from each relationship were compared using F-tests, r² values and paired t-tests. The best model was used to construct a nomogram.

RESULTS

The final plasma oxypurinol concentration-response relationship (U_T  = U_P  - C*(U_P  - U_R )/(ID₅₀  + C), r²  = 0.64) and allopurinol dose-response relationship (U_T  = U_P  - D^* (U_P  - U_R )/(ID₅₀  + D), r²  = 0.60) did not include CL_CR or diuretic use as covariates. There was no difference (P = 0.87) between the predicted SU concentrations derived from the oxypurinol concentration- and allopurinol dose-response relationships. The nomogram constructed using the allopurinol dose-response relationship for all recruited patients (n = 81 patients) required pretreatment SU as the predictor of allopurinol maintenance dose.

CONCLUSIONS

Plasma oxypurinol concentrations, CL_CR and diuretic status are not required to predict the maintenance dose of allopurinol. Using the nomogram, the maintenance dose of allopurinol estimated to reach target concentrations can be predicted from U_P .

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 (CL_R), and apparent non-renal clearance of metformin (CL_NR/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, CL_CR; 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 AUC_0-τ, CL_R, and CL_NR/F.

RESULTS

The data (66 patients, 327 observations) were best described by a two-compartment model, and CL_CR was a covariate for CL_R. Mean CL_R was 17 L/h (CV 22%) and mean CL_NR/F was 1.6 L/h (69%).The median recovery of metformin in urine was 49% (range 19-75%) over a dosage interval. When CL_R increased due to improved renal function, AUC_0-τ decreased proportionally, while CL_NR/F did not change with kidney function. Target doses (mg/day) of metformin can be reached using CL_CR/3 × 100 to obtain median AUC_0-12 of 18-26 mg/L/h for metformin IR and AUC_0-24 of 38-51 mg/L/h for metformin XR, with C_max < 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.

Lepton-Flavor-Dependent Angular Analysis of B→K^{*}ℓ^{+}ℓ^{-}

We present a measurement of angular observables and a test of lepton flavor universality in the B→K^{*}ℓ^{+}ℓ^{-} decay, where ℓ is either e or μ. The analysis is performed on a data sample corresponding to an integrated luminosity of 711  fb^{-1} containing 772×10^{6} BB[over ¯] pairs, collected at the ϒ(4S) resonance with the Belle detector at the asymmetric-energy e^{+}e^{-} collider KEKB. The result is consistent with standard model (SM) expectations, where the largest discrepancy from a SM prediction is observed in the muon modes with a local significance of 2.6σ.

Energy Scan of the e^{+}e^{-}→h_{b}(nP)π^{+}π^{-} (n=1, 2) Cross Sections and Evidence for ϒ(11020) Decays into Charged Bottomoniumlike States

Using data collected with the Belle detector at the KEKB asymmetric-energy e^{+}e^{-} collider, we measure the energy dependence of the e^{+}e^{-}→h_{b}(nP)π^{+}π^{-} (n=1, 2) cross sections from thresholds up to 11.02 GeV. We find clear ϒ(10860) and ϒ(11020) peaks with little or no continuum contribution. We study the resonant substructure of the ϒ(11020)→h_{b}(nP)π^{+}π^{-} transitions and find evidence that they proceed entirely via the intermediate isovector states Z_{b}(10610) and Z_{b}(10650). The relative fraction of these states is loosely constrained by the current data: The hypothesis that only Z_{b}(10610) is produced is excluded at the level of 3.3 standard deviations, while the hypothesis that only Z_{b}(10650) is produced is not excluded at a significant level.

First Observation of the Doubly Cabibbo-Suppressed Decay of a Charmed Baryon: Λ_{c}^{+}→pK^{+}π^{-}

We report the first observation of the decay Λ_{c}^{+}→pK^{+}π^{-} using a 980  fb^{-1} data sample collected by the Belle detector at the KEKB asymmetric-energy e^{+}e^{-} collider. This is the first observation of a doubly Cabibbo-suppressed decay of a charmed baryon. We measure the branching ratio of this decay with respect to its Cabibbo-favored counterpart to be B(Λ_{c}^{+}→pK^{+}π^{-})/B(Λ_{c}^{+}→pK^{-}π^{+})=(2.35±0.27±0.21)×10^{-3}, where the uncertainties are statistical and systematic, respectively.

Observation of Z_{b}(10610) and Z_{b}(10650) Decaying to B Mesons

We report the analysis of the three-body e^{+}e^{-}→BB[over ¯]π^{±}, BB[over ¯]^{*}π^{±}, and B^{*}B[over ¯]^{*}π^{±} processes, including the first observations of the Z_{b}^{±}(10610)→[BB[over ¯]^{*}+c.c.]^{±} and Z_{b}^{±}(10650)→[B^{*}B[over ¯]^{*}]^{±} transitions that are found to dominate the corresponding final states. We measure Born cross sections for the three-body production of σ(e^{+}e^{-}→[BB[over ¯]^{*}+c.c.]^{±}π^{∓})=[17.4±1.6(stat)±1.9(syst)]  pb and σ(e^{+}e^{-}→[B^{*}B[over ¯]^{*}]^{±}π^{∓})=[8.75±1.15(stat)±1.04(syst)]  pb and set a 90% C.L. upper limit of σ(e^{+}e^{-}→[BB[over ¯]]^{±}π^{∓})<2.9  pb. The results are based on a 121.4  fb^{-1} data sample collected with the Belle detector at a center-of-mass energy near the ϒ(10860) peak.

Observation of the Decay B_{s}^{0}→K^{0}K[over ¯]^{0}

We measure the decay B_{s}^{0}→K^{0}K[over ¯]^{0} using data collected at the ϒ(5S) resonance with the Belle detector at the KEKB e^{+}e^{-} collider. The data sample used corresponds to an integrated luminosity of 121.4  fb^{-1}. We measure a branching fraction B(B_{s}^{0}→K^{0}K[over ¯]^{0})=[19.6_{-5.1}^{+5.8}(stat)±1.0(syst)±2.0(N_{B_{s}^{0}B[over ¯]_{s}^{0}})]×10^{-6} with a significance of 5.1 standard deviations. This measurement constitutes the first observation of this decay.

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.

The incidence, mortality and timing of Pneumocystis jiroveci pneumonia after hematopoietic cell transplantation: a CIBMTR analysis

Pneumocystis jiroveci pneumonia (PJP) is associated with high morbidity and mortality after hematopoietic stem cell transplantation (HSCT). Little is known about PJP infections after HSCT because of the rarity of disease given routine prophylaxis. We report the results of a Center for International Blood and Marrow Transplant Research study evaluating the incidence, timing, prophylaxis agents, risk factors and mortality of PJP after autologous (auto) and allogeneic (allo) HSCT. Between 1995 and 2005, 0.63% allo recipients and 0.28% auto recipients of first HSCT developed PJP. Cases occurred as early as 30 days to beyond a year after allo HSCT. A nested case cohort analysis with supplemental data (n=68 allo cases, n=111 allo controls) revealed that risk factors for PJP infection included lymphopenia and mismatch after HSCT. After allo or auto HSCT, overall survival was significantly poorer among cases vs controls (P=0.0004). After controlling for significant variables, the proportional hazards model revealed that PJP cases were 6.87 times more likely to die vs matched controls (P<0.0001). We conclude PJP infection is rare after HSCT but is associated with high mortality. Factors associated with GVHD and with poor immune reconstitution are among the risk factors for PJP and suggest that protracted prophylaxis for PJP in high-risk HSCT recipients may improve outcomes.