21 resultados para creatinine clearance
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Standard dosage recommendations for beta-lactam antibiotics can result in very low drug levels in intensive care (IC) patients without renal dysfunction. We compared the pharmacokinetics of two fourth-generation cephalosporins, cefepime and cefpirome, and examined the relationship of drug clearance (CL) to creatinine clearance (CLCR). Two separate but similar pharmacokinetic studies (which used 2 g twice daily for each antibiotic) were conducted. Blood was sampled after an initial and a subsequent antibiotic dose. Drug plasma concentrations were measured, and pharmacokinetic analyses were conducted and compared. The pharmacokinetics of cefepime and cefpirome are similar in IC patients. Any differences in drug CL can largely be attributed to differences in CLCR. Despite normal plasma creatinine concentrations, 54% of patients' antibiotic concentrations were less than the minimum inhibitory concentration (MIC) (4 mg/L) for >20% of the dosing interval. Thirty-four percent of patients had CLCR >144 mL/min (20% higher than the expected maximum of 120 mL/min). Only CLCR was an independent predictor of antibiotic CL. Time above MIC was predicted only by CLCR. Some IC patients have a very large CLCR which results in very low levels of studied antibiotics. Either shortening the dosage interval or using continuous infusions would prevent low levels and keep troughs above the MIC for longer periods. In view of the lack of bedside measurement of cephalosporin levels, we suggest that more frequent use be made of CLCR to allow prediction of small concentrations clinically.
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A retrospective review was undertaken in 744 patients who were dose-individualized with gentamicin once daily to evaluate a change in gentamicin clearance as a potential predictor of nephrotoxicity. The definition of nephrotoxicity was chosen to be a change in creatinine clearance greater than 20%. Similarly, a change in gentamicin clearance of greater than 20% was also considered a possible index of nephrotoxicity. Four criteria were developed to assess the usefulness of gentamicin clearance as a predictor of nephrotoxicity. Following the application of the inclusion/exclusion criteria, 132 patients were available for the analysis. The sensitivity, specificity, positive predictive value, and negative predictive value were assessed for each of the criteria. Receiver operating characteristic (ROC) curves were produced to determine if an optimum value in the change of gentamicin clearance could be found to maximize sensitivity and specificity. The overall incidence of nephrotoxicity based on a decrease in creatinine clearance by 20% or more was 3.8%. Women were overrepresented in the nephrotoxic group [71.4% versus 40.1% (P = 0.0025)]. Patients with nephrotoxicity had statistically longer treatment periods, increased cumulative dose, and more dosing predictions (P < 0.05 in each case). The sensitivity of the criteria ranged from 43 to 46%, and specificity ranged from 93 to 99%. The positive and negative predictive values ranged from 63 to 94% and 86 to 89%, respectively. In those patients in whom nephrotoxicity was predicted from a change in gentamicin clearance, this change occurred on average 3 days before the change in creatinine clearance (P < 0.05). A change in gentamicin clearance to predict nephrotoxicity may be a useful addition to current monitoring methods, although it is not the complete answer.
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Drugs and metabolites are eliminated from the body by metabolism and excretion. The kidney makes the major contribution to excretion of unchanged drug and also to excretion of metabolites. Net renal excretion is a combination of three processes - glomerular filtration, tubular secretion and tubular reabsorption. Renal function has traditionally been determined by measuring plasma creatinine and estimating creatinine clearance. However, estimated creatinine clearance measures only glomerular filtration with a small contribution from active secretion. There is accumulating evidence of poor correlation between estimated creatinine clearance and renal drug clearance in different clinical settings, challenging the 'intact nephron hypothesis' and suggesting that renal drug handling pathways may not decline in parallel. Furthermore, it is evident that renal drug handling is altered to a clinically significant extent in a number of disease states, necessitating dosage adjustment not just based on filtration. These observations suggest that a re-evaluation of markers of renal function is required. Methods that measure all renal handling pathways would allow informed dosage individualisation using an understanding of renal excretion pathways and patient characteristics. Methodologies have been described to determine individually each of the renal elimination pathways. However, their simultaneous assessment has only recently been investigated. A cocktail of markers to measure simultaneously the individual renal handling pathways have now been developed, and evaluated in healthy volunteers. This review outlines the different renal elimination pathways and the possible markers that can be used for their measurement. Diseases and other physiological conditions causing altered renal drug elimination are presented, and the potential application of a cocktail of markers for the simultaneous measurement of drug handling is evaluated. Further investigation of the effects of disease processes on renal drug handling should include people with HIV infection, transplant recipients (renal and liver) and people with rheumatoid arthritis. Furthermore, changes in renal function in the elderly, the effect of sex on renal function, assessment of living kidney donors prior to transplantation and the investigation of renal drug interactions would also be potential applications. Once renal drug handling pathways are characterised in a patient population, the implications for accurate dosage individualisation can be assessed. The simultaneous measurement of renal function elimination pathways of drugs and metabolites has the potential to assist in understanding how renal function changes with different disease states or physiological conditions. In addition, it will further our understanding of fundamental aspects of the renal elimination of drugs.
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Patient outcomes in transplantation would improve if dosing of immunosuppressive agents was individualized. The aim of this study is to develop a population pharmacokinetic model of tacrolimus in adult liver transplant recipients and test this model in individualizing therapy. Population analysis was performed on data from 68 patients. Estimates were sought for apparent clearance (CL/F) and apparent volume of distribution (V/F) using the nonlinear mixed effects model program (NONMEM). Factors screened for influence on these parameters were weight, age, sex, transplant type, biliary reconstructive procedure, postoperative day, days of therapy, liver function test results, creatinine clearance, hematocrit, corticosteroid dose, and interacting drugs. The predictive performance of the developed model was evaluated through Bayesian forecasting in an independent cohort of 36 patients. No linear correlation existed between tacrolimus dosage and trough concentration (r(2) = 0.005). Mean individual Bayesian estimates for CL/F and V/F were 26.5 8.2 (SD) L/hr and 399 +/- 185 L, respectively. CL/F was greater in patients with normal liver function. V/F increased with patient weight. CL/F decreased with increasing hematocrit. Based on the derived model, a 70-kg patient with an aspartate aminotransferase (AST) level less than 70 U/L would require a tacrolimus dose of 4.7 mg twice daily to achieve a steady-state trough concentration of 10 ng/mL. A 50-kg patient with an AST level greater than 70 U/L would require a dose of 2.6 mg. Marked interindividual variability (43% to 93%) and residual random error (3.3 ng/mL) were observed. Predictions made using the final model were reasonably nonbiased (0.56 ng/mL), but imprecise (4.8 ng/mL). Pharmacokinetic information obtained will assist in tacrolimus dosing; however, further investigation into reasons for the pharmacokinetic variability of tacrolimus is required.
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Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16-100 years), weight (25-165kg) and body mass index (BMI) [12-52 kg/m(2)] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (30 kg/m(2)).
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Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.
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Aims To investigate the concentration-effect relationship and pharmacokinetics of leflunomide in patients with rheumatoid arthritis (RA). Methods Data were collected from 23 RA patients on leflunomide therapy (as sole disease modifying antirheumatic drug (DMARD)) for at least 3 months. Main measures were A77 1726 (active metabolite of leflunomide) plasma concentrations and disease activity measures including pain, duration/intensity of morning stiffness, and SF-36 survey. A population estimate was sought for apparent clearance (CL/F ) and volume of distribution was fixed (0.155 l kg(-1)). Factors screened for influence on CL/F were weight, age, gender and estimated creatinine clearance. Results Significantly higher A77 1726 concentrations were seen in patients with less swollen joints and with higher SF-36 mental summary scores than in those with measures indicating more active disease (P < 0.05); concentration-effect trends were seen with five other disease activity measures. Statistical analysis of all disease activity measures showed that mean A77 1726 concentrations in groups with greater control of disease activity were significantly higher than those in whom the measures indicated less desirable control (P < 0.05). There was large between subject variability in the dose-concentration relationship. A steady-state infusion model best described the pharmacokinetic data. Inclusion of age as a covariate decreased interindividual variability (P < 0.01), but this would not be clinically important in terms of dosage changes. Final parameter estimate (% CV interindividual variability) for CL/F was 0.0184 l h(-1) (50%) (95% CI 0.0146, 0.0222). Residual (unexplained) variability (% CV) was 8.5%. Conclusions This study of leflunomide in patients using the drug clinically indicated a concentration-effect relationship. From our data, a plasma A77 1726 concentration of 50 mg l(-1) is more likely to indicate someone with less active disease than is a concentration around 30 mg l(-1). The marked variability in pharmacokinetics suggests a place for individualized dosing of leflunomide in RA therapy.
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Objective: Previous studies investigating associations between serum lipids and renal disease have generally not taken into account dietary intake or physical activity - both known to influence circulating lipids. Furthermore, inclusion of patients on HMG-CoA reductase inhibitors may also have influenced findings due to the pleiotropic effect of this medication. Therefore, the aim of this study is to determine the relationships between serum lipids and renal function in a group of patients not taking lipid-lowering medication and taking into account dietary intake and physical activity. Methods: Data from 100 patients enrolled in the Lipid Lowering and Onset of Renal Disease (LORD) trial were used in this study. Patients were included with serum creatinine > 120 mu mol/l, and excluded if they were taking lipid-lowering medication. Unadjusted and adjusted relationships were determined between fasting serum lipid concentrations (total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol/HDL ratio) and measures of renal function (estimated glomerular filtration rate (eGFR), creatinine clearance and serum creatinine) and urinary protein excretion. Results: Significant (p < 0.05) negative unadjusted relationships were found between lipids (total cholesterol, LDL and HDL cholesterol) and serum creatinine. In support of these findings, logarithmically-transformed lipids (total cholesterol, LDL and HDL cholesterol) were significantly associated with eGFR and creatinine clearance although the effects were of a smaller magnitude. Adjustment for dietary saturated fat intake and physical activity did not substantially change these effects. Conclusion: These data do not support the premise that lipids are associated with renal dysfunction in patients with normocholesterolemia.
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Aim To develop an appropriate dosing strategy for continuous intravenous infusions (CII) of enoxaparin by minimizing the percentage of steady-state anti-Xa concentration (C-ss) outside the therapeutic range of 0.5-1.2 IU ml(-1). Methods A nonlinear mixed effects model was developed with NONMEM (R) for 48 adult patients who received CII of enoxaparin with infusion durations that ranged from 8 to 894 h at rates between 100 and 1600 IU h(-1). Three hundred and sixty-three anti-Xa concentration measurements were available from patients who received CII. These were combined with 309 anti-Xa concentrations from 35 patients who received subcutaneous enoxaparin. The effects of age, body size, height, sex, creatinine clearance (CrCL) and patient location [intensive care unit (ICU) or general medical unit] on pharmacokinetic (PK) parameters were evaluated. Monte Carlo simulations were used to (i) evaluate covariate effects on C-ss and (ii) compare the impact of different infusion rates on predicted C-ss. The best dose was selected based on the highest probability that the C-ss achieved would lie within the therapeutic range. Results A two-compartment linear model with additive and proportional residual error for general medical unit patients and only a proportional error for patients in ICU provided the best description of the data. Both CrCL and weight were found to affect significantly clearance and volume of distribution of the central compartment, respectively. Simulations suggested that the best doses for patients in the ICU setting were 50 IU kg(-1) per 12 h (4.2 IU kg(-1) h(-1)) if CrCL < 30 ml min(-1); 60 IU kg(-1) per 12 h (5.0 IU kg(-1) h(-1)) if CrCL was 30-50 ml min(-1); and 70 IU kg(-1) per 12 h (5.8 IU kg(-1) h(-1)) if CrCL > 50 ml min(-1). The best doses for patients in the general medical unit were 60 IU kg(-1) per 12 h (5.0 IU kg(-1) h(-1)) if CrCL < 30 ml min(-1); 70 IU kg(-1) per 12 h (5.8 IU kg(-1) h(-1)) if CrCL was 30-50 ml min(-1); and 100 IU kg(-1) per 12 h (8.3 IU kg(-1) h(-1)) if CrCL > 50 ml min(-1). These best doses were selected based on providing the lowest equal probability of either being above or below the therapeutic range and the highest probability that the C-ss achieved would lie within the therapeutic range. Conclusion The dose of enoxaparin should be individualized to the patients' renal function and weight. There is some evidence to support slightly lower doses of CII enoxaparin in patients in the ICU setting.
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Background There is limited information regarding the clinical utility of amino-terminal pro-B-type natriuretic pepticle (NT-proBNP) for the detection of left ventricular (LV) dysfunction in the community. We evaluated predictors of circulating NT-proBNP levels and determined the utility of NT-proBNP to detect systolic and diastolic LV dysfunction in older adults. Methods. A population-based sample of 1229 older adults (mean age 69.4 years, 50.1% women) underwent echocardiographic assessment of cardiac structure and function and measurement of circulating NT-proBNP levels. Results Predictors of NT-proBNP included age, female sex, body mass index, and cardiorenal parameters (diastolic dysfunction [DID] severity; LV mass and left atrial volume; right ventricular overload; decreasing ejection fraction [EF] and creatinine clearance). The performance of NT-proBNP to detect any degree of LV dysfunction, including mild DID, was poor (area under the curve 0.56-0.66). In contrast, the performance of NT-proBNP for the detection of EF 0.90 regardless of age and sex; history of hypertension, diabetes, coronary artery disease; or body mass category. The ability of NT-proBNP to detect EF
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The aim of this study was to evaluate dosing schedules of gentamicin in patients with end-stage renal disease and receiving hemodialysis. Forty-six patients were recruited who received gentamicin while on hemodialysis. Each patient provided approximately 4 blood samples at various times before and after dialysis for analysis of plasma gentamicin concentrations. A population pharmacokinetic model was constructed using NONMEM (version 5). The clearance of gentamicin during dialysis was 4.69 L/h and between dialysis was 0.453 L/h. The clearance between dialysis was best described by residual creatinine clearance (as calculated using the Cockcroft and Gault equation), which probably reflects both lean mass and residual clearance mechanisms. Simulation from the final population model showed that predialysis dosing has a higher probability of achieving target maximum concentration (C-max) concentrations (> 8 mg/L) within acceptable exposure limits (area under the concentration-time curve [AUC] values > 70 and < 120 mg.h/L per 24 hours) than postdialysis dosing.
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Aim: To identify an appropriate dosage strategy for patients receiving enoxaparin by continuous intravenous infusion (CII). Methods: Monte Carlo simulations were performed in NONMEM, (200 replicates of 1000 patients) to predict steady state anti-Xa concentrations (Css) for patients receiving a CII of enoxaparin. The covariate distribution model was simulated based on covariate demographics in the CII study population. The impact of patient weight, renal function (creatinine clearance (CrCL)) and patient location (intensive care unit (ICU)) were evaluated. A population pharmacokinetic model was used as the input-output model (1-compartment first order output model with mixed residual error structure). Success of a dosing regimen was based on the percent of Css that is between the therapeutic range of 0.5 IU/ml to 1.2 IU/ml. Results: The best dose for patients in the ICU was 4.2IU/kg/h (success mean 64.8% and 90% prediction interval (PI): 60.1–69.8%) if CrCL60ml/min, the best dose was 8.3IU/kg/h (success mean 65.4%, 90% PI: 58.5–73.2%). Simulations suggest that there was a 50% improvement in the success of the CII if the dose rate for ICU patients with CrCL