Comparison of a neural network approach with five traditional methods for predicting creatinine clearance in patients with human immunodeficiency virus infection.

STUDY OBJECTIVE: To compare the results of an artificial neural network approach with those of five published creatinine clearance (Cl(cr)) prediction equations and with the measured (true) Cl(cr) in patients infected with the human immunodeficiency virus (HIV). DESIGN: Six-month prospective study. SETTINGS: Two university medical centers. PATIENTS: Sixty-five HIV-infected patients: 18 relatively healthy outpatients and 47 inpatients. INTERVENTIONS: All subjects had urine collected for 24 hours to determine Cl(cr). MEASUREMENTS AND MAIN RESULTS: The 16 input variables were age, ideal body weight, actual body weight, body surface area, height, and the following blood chemistries: sodium, potassium, aspartate aminotransferase, alanine aminotransferase, red blood cell count, platelet count, white blood cell count, glucose, serum creatinine, blood urea nitrogen, and albumin. The only output variable was Cl(cr). A training set of 55 subjects was used to develop the relationship between input variables and the output variable. The trained neural network was then used to predict Cl(cr) of a validation set of 10 subjects. Mean differences between predicted Cl(cr) and actual Cl(cr) (bias) were 4.1, 28.7, 29.4, 26.0, 31.8, and 55.8 ml/min/1.73 m2 for the artificial neural network, Cockcroft and Gault, Jelliffe 1, Jelliffe 2, Mawer et al, and Hull et al methods, respectively. CONCLUSION: The accuracy of predicting Cl(cr) in subjects with HIV infection by the artificial neural network is superior to that of the five equations that are currently used in clinical settings.     

Comparing two predictive methods for determining serum vancomycin concentrations at a Veterans Affairs Medical Center.

PURPOSE: Two predictive methods for determining serum vancomycin concentrations (SVCs) at a Veterans Affairs medical center were compared. METHODS: The data for inpatients at the San Francisco Veterans Affairs Medical Center who received i.v. vancomycin and had vancomycin concentrations recorded in 2003 were included in this retrospective study. Creatinine clearance was estimated by the Cockcroft and Gault equation. Volume of distribution and creatinine clearance were calculated for each patient, using the Leonard and Boro method and the Rushing and Ambrose method. The Sheiner and Beal method for determining precision and bias was used to evaluate whether the two methods significantly differed in their ability to predict SVCs. RESULTS: Of the 223 patients identified, 122 patients were included, and 212 SVCs were analyzed. The population was mostly male and had a mean age of 64.1 years. There were no significant differences in 95% confidence intervals for relative precision and relative bias between the two methods. In patients whose weight was within 120% of their ideal body weight (IBW), the Leonard and Boro method was significantly more precise and less biased in predicting SVCs. In patients whose weight exceeded 120% of their IBW, the Rushing and Ambrose method was less biased and tended to be more precise, although the difference in precision was not significant. CONCLUSION: Both methods yielded similar predictability for SVCs in a veterans population. The Leonard and Boro method better predicted SVCs in patients weighing within 120% of their IBW, while the Rushing and Ambrose method appeared to be more appropriate for calculating vancomycin dosages in patients whose weight exceeded 120% of their IBW.     

Evaluation of eight methods for estimating creatinine clearance in men

Eight methods for estimating creatinine clearance (CLcr) were compared in 65 men with serum creatinine concentrations (SCr) less than or equal to 1.5 mg/dL (group 1) and 65 men with SCr greater than 1.5 mg/dL (group 2). All patients had SCr values that did not fluctuate by more than +/- 10% for two weeks before and two weeks after measurement of CLcr. For each patient, predictions of CLcr by each of eight currently used formulas were compared with measured CLcr values; both regression analysis and predictive error analysis were used. Group 1 patients ranged in age from 32 to 64 years (mean, 53), weighed from 48 to 105 kg (mean, 73), and were from 63 to 79 inches in height (mean, 69). Group 2 patients ranged from 26 to 63 years of age (mean, 53), weighed from 34 to 141 kg (mean, 80), and were from 63 to 76 inches in height (mean, 70). Measured CLcr values ranged from 29.8 to 197 mL/min in group 1 and from 2.8 to 118 mL/min in group 2. Ranges of SCr values were 0.7-1.5 mg/dL (mean, 1.1) in group 1 and 1.6-7.1 mg/dL (mean, 2.8) in group 2; the formula of Cockcroft and Gault, which uses age, body weight, and SCr, had the highest correlation and the greatest accuracy in group 1, whereas the formula of Jelliffe, which uses body surface area and SCr, had the highest correlation and the greatest accuracy in group 2. Estimation of creatinine clearance can be improved by identification and use of the formula that is best suited to a specific patient population.     

Study of the predictive method of 24-hour creatinine clearance in calculating the appropriate dosage of Carboplatin

BACKGROUND: The carboplatin (CBDCA) dosage is usually calculated using the formula of Calvert. Instead of the glomerular filtration rate (GFR), 24-h creatinine clearance (24 CLcr) is often used in this formula, which is calculated based on 24-h urine collection in clinical practice. OBJECTIVE: We studied the adequacy of 24 CLcr in calculating the appropriate dosage of CBDCA using the formula of Calvert and compared CLcr and GFR using various substitutable predictive formulas (the formulae of Cockcroft and Gault, Yasuda, Orita, Jellife, Mawer, MDRD, and modified MDRD) when we were not able to use 24 CLcr. METHODOLOGY: We retrospectively studied 193 patients who received CBDCA as chemotherapy during the period April 2004 through November 2006. We evaluated the adequacy of 24-h urine collection for measurement of creatinine production and excretion. We also evaluated the appropriate urine collection within a 15% range of the difference. The correlation between the appropriate 24 CLcr resulting in the urine collection and the CLcr or GFR was examined using past predictive formulae in the patients with appropriate urine collection. Results: The accuracy of 24 CLcr was evaluated in 83 patients (43%). There was a significant correlation between CLcr or GFR using various predictive formulas and the appropriate 24 CLcr. There was an especially close and significant correlation with the formulae of Cockcroft and Gault and Yasuda (r>0.950, p<0.001). CONCLUSION: When using the Calvert formula, the accuracy of 24 CLcr should be evaluated. Patients evaluated as having inaccurate urinary collection should use the formulae of Cockcroft and Gault and Yasuda.     

Pharmacokinetics of gentamicin in 957 patients with varying renal function dosed once daily

AIMS: 1. To determine the population pharmacokinetics of gentamicin in 957 patients with varying renal function dosed once daily. 2. To see if current starting doses for once daily aminoglycoside dosing are appropriate. 3. To test whether calculating creatinine clearance using an adjusted Cockcroft and Gault method (CLCr,adjusted ) was a better predictor of gentamicin clearance than the standard Cockcroft and Gault method (CLCr,unadjusted ). METHODS: Nine hundred and fifty-seven patients were dose-individualized for gentamicin using SeBA-GEN, a Bayesian dosing method. This method returns estimates of the values of gentamicin CL and V d from which the 24 h AUC can be estimated. The goal of therapy was to attain an AUC of 70-100 mg l-1 h depending on the severity of the infection. The population was divided into four groups of differing renal function. Linear regression analysis was performed to determine the relationship between V d and various indices of weight, and gentamicin CL and either CLCr,adjusted or CLCr,unadjusted. RESULTS: The mean V d (+/-s. d.) and CL (+/-s.d.) of gentamicin in our total population were 17.4 (+/-4.1) l and 4.0 (+/-1.8) l h-1, respectively. There was a decrease in V d with reducing renal function when comparing patients with normal renal function and patients with poor renal function. The lower of total body weight (TBW) and lean body weight (LBW), termed dosing weight (DWT), was a slightly better predictor of V d (r2=0.28) than either TBW (r2=0.21) or LBW (r2=0.21). CLCr,adjusted (r2=0.80) was a better predictor of gentamicin CL than CLCr, unadjusted (r2=0.57). CONCLUSIONS: The mean population values of V d and CL of gentamicin dosed once daily are similar to those described by others in relation to multiple daily dosing. Given that previous methods have been based on population values of V d and CL from multiple daily dosing, the currently recommended starting doses for once daily aminoglycoside dosing would seem appropriate. The V d reduced with decreasing renal function, with a maximum of 23% difference between patients with normal and poor renal function. The Cockcroft and Gault method of calculating creatinine clearance does not appear to perform well at low values of serum creatinine concentration. An adjustment of the Cockcroft and Gault method is proposed to allow for this.     

Comparison of aminoglycoside clearance and calculated serum creatinine clearances

Calculated creatinine clearance (CrCL) estimates are frequently used as estimates of aminoglycoside clearance (AGCL), despite being inadequately studied. Thirty surgical intensive care unit (SICU) patients with stable serum creatinines (0.6-6.3 mg/dl) and steady-state aminoglycoside levels were studied. A one-compartment pharmacokinetic infusion model was used to calculate k and Vd; AGCL = (k) (Vd). CrCLs using the equations of Cockroft-Gault (CGCL), Jelliffe (JCL), and Jelliffe uncorrected for body surface area (JCLu) were calculated, then compared to the AGCL. The JCLu was a better fit to the data (y = 0.98x + 0.44, r = 0.91) with a superior regression correlation (p less than 0.02) than CGCL (y = 0.91x + 6.07, R = 0.89) and JCL (y = 1.11x + 2.11, R = 0.89) correlations. CGCL overpredicted the AGCL whereas JCL and JCLu underpredicted the AGCL. All three methods showed a precision of approximately 20 ml/min. Relative bias and precision show JCLu better than JCL, CGCL better than JCL only for bias, and CGCL and JCLu not different. The absolute percentage error of the CrCL estimates tended to be lower at higher AGCL and did not differ for CGCL, JCL, and JCLu. In the SICU setting, we suggest the use of the JCLu for estimating the AGCL.     

Relationship between pharmacokinetic parameters of gentamicin and patient characteristics and/or clinical data in patients with solid organ tumours

Gentamicin monitoring and the selection of the initial dosage are generally based on the relationship between pharmacokinetic parameters of gentamicin (GPP) and patient characteristics and/or clinical data (PC). However, the number of studies about this relationship in cancer patients is limited. Therefore, the main objective of the present study was to evaluate the relationship between GPP and PC in cancer patients and to identify different subgroups within this group of patients with unique relationship models between GPP and PC. A total of 198 cancer patients were included in the study. Firstly, GPP were estimated by the Sawchuk and Zaske regression method. Then, a linear regression analysis was performed to investigate the relationship between GPP and PC, and lastly subgroups with unique models were identified by comparing their regression models. The results revealed that the variable which was the best predictor of the distribution volume of gentamicine was the dosing weight (DW = IBW + (ABWIBW), ABW being the actual body weight and IBW the ideal body weight). Creatinine clearance (CL_CR) measured by a 24hour urine collection (CL^CRu) was the best predictor of gentamicin clearance (CL). When this value is not available, the CL^CR estimated by the formula of Crockcroft and Gault (CG), can be used. When the CG formula was used, unique models to predict CL from CL^CR were identified for patients who were obese, patients who had received highdose chemotherapy and, for subjects who had never developed aplasia following chemotherapy. Whichever the model used, the results showed that some variability in pharmacokinetic parameters of gentamicin was not explained by the models, especially in some groups of patients.     

Gentamicin pharmacokinetics in old age and frailty

AIMS

Frailty, a syndrome of decreased physiological reserve that is prevalent in old age, impacts on clinical pharmacology. The aims of the study were to (1) determine whether frailty affects the pharmacokinetics of gentamicin and (2) assess the accuracy of different estimates of body size and renal clearance as estimates of gentamicin pharmacokinetics in older inpatients.

METHODS

This was an observational study of gentamicin pharmacokinetics in a cohort of Australian hospital inpatients aged ≥65 years, who were administered prophylactic intravenous gentamicin.

RESULTS

Of the 31 participants, 14 were frail and 17 non frail on the Reported Edmonton Frail Scale. The mean volume of distribution of gentamicin was 14.8 ± 1.4 l in frail participants and 15.3 ± 2.2 l in non frail (NS). Volume of distribution correlated best with lean bodyweight. Gentamicin clearance was significantly lower in frail participants (46.6 ± 10.7 ml min⁻¹) than in non frail (58.2 ± 12.4 ml min⁻¹, P= 0.01). The Cockcroft Gault estimate of creatinine clearance calculated using ideal bodyweight gave the best estimate of gentamicin clearance (mean error – 0.15 ml min⁻¹, 95% CI −2.67, 2.39). The Cockcroft Gault creatinine clearance calculated using actual bodyweight and the estimated glomerular filtration rate from the modified diet in renal disease equation overestimated gentamicin clearance, with mean errors of −10.15 ml min⁻¹ (95%CI −13.60, −6.71) and −18.86 ml min⁻¹ (95% CI −22.45, −15.27), respectively. The Cockcroft Gault creatinine clearance calculated using lean bodyweight underestimated gentamicin clearance (mean error 6.54 ml min⁻¹, 95% CI 4.18, 8.90).

CONCLUSIONS

Frail older people have significantly lower gentamicin clearance than non frail. The best estimate of gentamicin clearance is obtained from the Cockcroft Gault creatinine clearance calculated using ideal bodyweight.     

Prediction of creatinine clearance from plasma creatinine: comparison of five formulae.

1. Glomerular filtration rate is commonly assessed by measurement of endogenous creatinine clearance (CLcr). Several formulae have been described to calculate CLcr from plasma creatinine and we have evaluated the effectiveness of five formulae in 95 healthy subjects (17 F and 78 M, 20-25 years) and in 72 patients attending a renal clinic (48F and 24 M, 14-69 years). Endogenous CLcr was measured and the CLcr was also estimated by five formulae. 2. In healthy subjects CLcr calculated by the formulae of Cockcroft & Gault (1976) and Mawer et al. (1972) gave higher correlation coefficients than the other three formulae. 3. When all of the subjects with a wide range of CLcr (3.1-164) were considered all formulae gave good correlations, but the formula of Cockcroft & Gault (1976) gave the best estimate.     

Carboplatin dosage formulae can generate inaccurate predictions of Carboplatin exposure in carboplatin/paclitaxel combination regimens

Carboplatin is a frequently used antitumour agent recommended to be administered according to the Calvert formula: dose = AUC x (GFR+25), where GFR is the glomerular filtration rate as measured by (51)Cr-EDTA clearance and AUC is the targeted area under the carboplatin concentration versus time curve. In several modified Calvert formulae, the GFR is estimated on the basis of serum creatinine levels. We compared AUCs of carboplatin that were predicted by modified Calvert formulae with actual measured AUCs in 75 courses in patients with non-small cell lung cancer or ovarian cancer who were treated with the combination of carboplatin-paclitaxel. Predictions were made using two modified Calvert formulae, in which the GFR was calculated by serum creatinine level-based equations, according to Jelliffe (Eq. 1) and Cockroft-Gault (Eq. 2). We also studied the performance of a formula for the clearance of carboplatin, as proposed by Chatelut (Eq. 3). The actual measured mean AUC was 4.6 mg/ml.min (range 1.9 to 10.4 mg/ml.min, SD 1.7). Equation 1 overestimated the AUC by 32.9% with an imprecision of 43.0%, and equation 2 overestimated the AUC by 27.6% with an imprecision of 33.4%. For equation 3, an AUC overestimation of only 10.2%, but with an imprecision of 25.3%, was observed. In conclusion, all three equations overestimated the carboplatin AUCs and had poor precisions. We concluded that the real carboplatin AUCs were lower than calculated, using the three tested formulae. This may have important consequences for ongoing and future phase II and III studies with carboplatin-paclitaxel combinations, utilising these formulae to calculate the carboplatin dose. Thus far, the original Calvert dosage formula remains the 'golden standard'.     

Differences in antimicrobial drug exposure in patients with various degrees of renal function based on recommendations from dosing references

STUDY OBJECTIVE: To calculate and compare 24-hour area under the unbound drug concentration-time curves (AUC(0-24)) of antimicrobials with dosing recommendations from six commonly used dosing references. INTERVENTION: Unbound plasma concentration-time profiles of 13 antimicrobial agents (4 penicillins, 3 cephalosporins, 2 carbapenems, aztreonam, 3 fluoroquinolones) were simulated at steady state using a one-compartment open model for a 70-kg adult based on pharmacokinetic parameters obtained from peer-reviewed literature. Simulations were performed at five levels of creatinine clearance (Cl(cr)). MEASUREMENTS AND MAIN RESULTS: Differences in AUC(0-24) for each antimicrobial agent were noted among the six references at each level of Cl(cr) as well as within references across the range of Cl(cr). In addition, up to 16-fold and 3-fold ranges in AUC(0-24) values were observed for beta-lactams and fluoroquinolones, respectively, in one reference based on dosing recommendations at a single level of Cl(cr) (due to more than one dose and/or dosing interval). CONCLUSION: Clinicians should be aware of differences among common references when selecting dosages of antimicrobial agents, especially for patients with moderate-to-severe renal impairment.     

Relationship of serum thiocyanate concentrations to smoking characteristics

Thiocyanate (SCN) concentrations were determined in serum samples from 130 young healthy persons (60 smokers) and related to their smoking and physiologic characteristics. Serum thiocyanate correlated strongly and approximately equally with the number of cigarettes/d X kg of ideal body weight (IBW) (r = 0.748), total nicotine intake in mg/d X kg IBW (r = 0.735), and total tar intake in mg/d X kg IBW (r = 0.731). Multiple linear regression analysis that included these factors as well as sex, marijuana use, menthol, and degree of inhalation only increased the multiple r to 0.803. A more sensitive statistical method (NYBAID) was also used to determine the most significant influences of these variables on serum SCN. The association with depth of inhalation (i.e., smoking versus nonsmoking) was dominant among the relationships considered. The highest SCN levels were exhibited in heavy nicotine users (8.58 +/- 3.00 mg/l), while average users had slightly lower concentrations (6.49 +/- 2.37 mg/l) (p less than 0.03). In nontobacco smokers, those who smoked marijuana several times weekly had higher SCN levels (4.66 +/- 2.16 mg/l) than noncannabis users (2.38 +/- 1.38 mg/l) (p less than 0.03). These studies confirm the utility of serum SCN as an index of smoking rate and demonstrate the role of secondary variables in accounting for the chemical in serum.     

Evaluation of two prediction models for digoxin dosing

The relationship between the digoxin elimination parameter (A%) and creatinine clearance (CL_Cr) was determined, from blood level data of 160 hospital patients receiving digoxin tablets. The linear regression equation obtained, which varied only slightly from that reported by Jelliffe previously, was used to predict serum digoxin concentrations in 140 patients of four age groups (50–60, 60–70, 70–80 and 80–90 years). The predictions made were found to be less biased and more precise, irrespective of the age of the patients, than those produced using another predictive method known as Dobbs method. However, correlation coefficients of predicted versus measured serum digoxin concentrations for each method did not differ significantly and frequency distribution analyses of prediction errors gave poor results (up to 63% only). Therefore, neither method can be considered to be superior to the other nor can they be said to ensure accurate predictions of serum digoxin concentrations.     

Population pharmacokinetics of flucytosine: comparison and validation of three models using STS, NPEM, and NONMEM

The objective of this study is to compare and validate three models of flucytosine (5-FC) population pharmacokinetics using three methods of analysis to elucidate which model describes 5-FC pharmacokinetics most accurately and which method is the most suitable for this purpose. Retrospectively, demographic and clinical data of two similar sets of a total of 88 intensive care unit (ICU) patients were gathered for calculation and validation of 5-FC pharmacokinetics respectively. Three pharmacokinetic models were analyzed: a one-compartment with renal elimination (renal model), a one-compartment with renal and metabolic elimination (mixed model), and a two-compartment with renal elimination (two-compartment model). Population pharmacokinetic parameters were calculated using the standard two-stage method (STS), NONMEM, and NPEM. Furthermore, a covariate model was built by NONMEM. Validation of the 10 calculated pharmacokinetic models showed that NONMEM is most suitable for predicting 5-FC population pharmacokinetics. Based upon AIC values, bias and precision, the best results are obtained using a two-compartment model with renal elimination (k(elr) = 0.000858 +/- 0.000143 l/h per mL per min, k12 = 0.0313 +/- 0.0168 h(-1), k21 = 0.0353 +/- 0.0145 h(-1), and Vd = 0.541 +/- 0.084 L/kg; bias = -13.16; 95% CI = -16.77; -9.55; precision = 30.50; 95% CI = 27.47; 33.26) or a two-compartment covariate model as built by NONMEM [Vd (L) = 0.572 x WT, Cl(5FC) (L/h) = 1.69 + 0.0273 x (Cl(cr) (mL/min) - 52.5), k12 = 0.0235 +/- 0.0107 h(-1), and k21 = 0.0375 +/- 0.0147 h(-1); bias = -8.29; 95% CI = -11.63; -4.95; precision = 26.77; 95% CI = 24.24; 29.07]. In conclusion, this study shows that a two-compartment model with renal elimination best describes 5-FC population pharmacokinetics and NONMEM is able to build a two-compartment covariate model that predicts 5-FC levels equally well in our population of ICU patients. Furthermore, NONMEM appeared to be the most suitable method of population pharmacokinetics in our population and for this purpose it offers more reliable and accurate results than NPEM or the STS method.     

Predictive performance of renal function estimate equations in renal allografts

Aims Various mathematical models have been developed to estimate glomerular filtration rate (GFR) incorporating variables such as age, gender, height, weight, serum creatinine, and body surface area (BSA). Because adjustments in drug dosing are often based on estimated values of renal function, it is important to define which, if any, of the available models, is appropriate for a specific patient population. A study was undertaken to determine the bias and precision of four mathematical models to estimate GFR in renal allograft recipients.
Methods A retrospective review of 142 stable renal allograft patients, using iohexol clearance as a determinant of GFR, was performed. Renal allograft recipients followed in an outpatient clinic setting underwent iohexol clearance studies as part of clinical monitoring in the post-transplant period. Measured GFR values were compared with four mathematical models used to estimate GFR: the Cockcroft-Gault equation, the Jelliffe equation, the Walser equation, and the Mawer equation. Bias and precision were determined for each model as the mean squared error and the mean squared error, respectively.
Results Patients had a mean age of 44±13 years, 92 were male, and 50 were female. The serum creatinine concentration was 176.8±88.4 μmol l⁻¹ (mean±s.d.). The mean time post-transplant was 5.1±5.0 years and 38% of patients had insulin-requiring diabetes mellitus. The bias and precision results for the Jelliffe, Walser, Cockcroft-Gault, and Mawer models were: −3 and 414; −5 and 381; 16 and 688; and 23 and 1084, respectively.
Conclusions The Jelliffe and Walser equations gave the least biased and most precise estimations of GFR when compared with iohexol-derived measures in patients with renal allografts.     

Influence of body fat on the volume of distribution of theophylline

Twenty-six acutely bronchospastic patients admitted to the emergency room of an acute care hospital were studied to determine the influence of body fat on the volume of distribution (Vd) of theophylline. Total body weight (TBW), height, skinfold thickness (at three sites), and theophylline levels around an intravenous aminophylline dose were measured. Ideal body weight (IBW), percentage of body fat, and absolute and relative Vd of theophylline were calculated. Vd of theophylline correlated better with IBW than TBW. No relationship was identified between Vd normalized to IBW and either percentage of body fat or the ratio of TBW to IBW. This indicates that in our sample (0.8 IBW less than TBW less than 1.4 IBW), distribution of theophylline into adipose tissue was not an important determinant of Vd. Theophylline's Vd was best estimated by the formula Vd (L) = 0.419(L/kg) X IBW(kg).     

Evaluation of two prediction models for digoxin dosing

The relationship between the digoxin elimination parameter (A%) and creatinine clearance (CL_Cr) was determined, from blood level data of 160 hospital patients receiving digoxin tablets. The linear regression equation obtained, which varied only slightly from that reported by Jelliffe previously, was used to predict serum digoxin concentrations in 140 patients of four age groups (50–60, 60–70, 70–80 and 80–90 years). The predictions made were found to be less biased and more precise, irrespective of the age of the patients, than those produced using another predictive method known as Dobbs method. However, correlation coefficients of predicted versus measured serum digoxin concentrations for each method did not differ significantly and frequency distribution analyses of prediction errors gave poor results (up to 63% only). Therefore, neither method can be considered to be superior to the other nor can they be said to ensure accurate predictions of serum digoxin concentrations.     

肾小球滤过率测算公式的计算机智能系统建立及临床应用研究

目的 建立肾小球滤过率(GFR)测算公式的计算机智能系统(CIS),并进行临床应用研究,评估其临床应用价值.方法 用VB编程,创建GFR测算公式的CIS,安装于专用计算机内使用.采用99mTc-DTPA清除率准确测定140例慢性肾衰竭(CRF)住院患者(肾功能不全组)与135例肾功能尚正常的其它肾病患者(肾功能正常组)的GFR(Tc-GFR),同时测血清胱抑素C(SCys-C)及肌酐(SCr)等指标,分别用Cockcroft/Gault公式计算内生肌酐清除率(CG-CCr)、CKDEPI等公式测算出GFR(CKDEPI · SCr-eGFR)等,对所得数据进行统计学分析.结果 CG-CCr、Filler G,et al· SCysC-eGFR、Rule·SCysC-eGFR与Tc-GFR差异有统计学意义(P＜0.05),肾功能不全者中,国内健康人·SCr&SCysC-eGFR与Tc-GFR也有统计学差异,其它公式eGFR与Tc-GFR均无统计学差异(均P＞0.05).结论 CIS较手工计算GFR简便、快速、数值精准,临床工作中应选择准确可靠的公式.     

Predictive performance study of two digoxin assays in subjects with various degrees of renal function

This prospective study was conducted to compare the predictive performance of fluorescence polarization immunoassay (FPIA, Abbott TDx Digoxin II) and radioimmunoassay (RIA, Kallestad Labs) with combined low-pressure liquid chromatography/RIA (LPLC/RIA) digoxin assay in measuring 15-17 serum digoxin concentrations (SDC) obtained after a single 10 microg/kg intravenous digoxin dose in patients with various degrees of renal function and at different SDC ranges. Eighteen men and women were stratified into 3 age- and gender-matched groups based upon renal function [N = 6 in each, group I (Cl(cr) < 10 mL/min), group II (Cl(cr) = 10-50 mL/min), and group III (Cl(cr) > 50 mL/min)]. Serum digoxin concentrations were measured at time zero; at 0.25, 0.5, 0.75, 1, 2, 3, 4, 6, 8, and 12 hours; and at 2, 3, 4, and 5-7 days after the digoxin dose, using the three different digoxin assays. TDx Digoxin II was unbiased [mean error -0.09 (95% CI -0.19, 0.01)] and RIA biased [mean error -0.29 (95% CI -0.36, -0.21)] to over-predict SDC by 14.2%. In group I patients, the analysis revealed a bias to over-predict SDC by 6.0% for TDx Digoxin II [mean error -0.16 (95% CI -0.29, -0.07)] and an unbiased performance by RIA. In groups II and III, both TDx Digoxin II and RIA showed biased performance, the mean magnitude of bias was low (< 20%). For intermediate SDC range (> 0.5 ng/mL and < or = 3.0 ng/mL), TDx Digoxin II was unbiased in predicting SDC, whereas RIA was biased to under-predict SDC [mean error 0.13 (95% CI 0.10, 0.16)] by 9.9%. The magnitude of bias observed in all cases was less than 20%. Both assays, TDx Digoxin II and RIA, imprecisely measured SDC for all samples combined, different groups and SDC ranges. In all time-paired samples, TDx Digoxin II (FPIA) performed better than the RIA. In conclusion, the magnitude of bias observed with either assay at different groups and SDC ranges was not likely to be clinically relevant. Therefore, either assay may be used to measure SDC in clinical practice.