***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
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    Input: Background In France , community pharmacists do not have free access to patients ’ lab results , and it is therefore impossible for them to identify patients with renal impairment . Objective ( 1 ) to evaluate the ability of community pharmacists ( CPs ) to identify drug related problems ( DRP ) in patients at risk for or suffering from renal impairment ; ( 2 ) to evaluate the proportions of recommendations by CPs that lead to a modification by GP . Setting A prospect i ve and observational study involving 24 community pharmacists in France . Methods Following special training , community pharmacists were asked to select 52 patients with the following characteristics : ≥65 years of age ; prescribed at least two diabetic and /or antihypertensive drugs . Serum creatinine value was obtained for each patient and glomerular filtration rate estimated ( eGFR ) with the aMDRD formula . Those with a eGFR 60 ml/min/1.73 m² were considered having chronic kidney disease ( CKD ) . Data was collected concerning whether the community pharmacists identified drug related problems and tried to inform the GP who prescribed the medications . Identified DRP were review ed by a team of nephrologists and hospital clinical pharmacists . Primary outcome The proportion of CKD patients and those without serum creatinine monitoring , the number of drug related problems identified by community pharmacists , and the proportion of drug related problems resolved by the community pharmacists intervention to the GP . Results Of the total 791 patients identified , 180 ( 22.8 % ) exhibited CKD , and 57 ( 7.2 % ) had not undergone serum creatinine monitoring . Among the 1297 drugs prescribed , 260 had to be adapted to eGFR . The proportion of DRP was 21.5 % ( 56/260 ) , of which 40 % ( 20 ) were identified by community pharmacists . Once the GP was informed , 33.3 % ( 6/18 ) of DRP were resolved . Conclusion Community pharmacists identified 40 % of DRP related to CKD prescriptions , leading to prescription modification by GPs in a third of the cases . These interventions are likely to decrease drug-related morbidity and mortality The glomerular filtration rate ( GFR ) is traditionally considered the best overall index of renal function in health and disease ( 1 ) . Because GFR is difficult to measure in clinical practice , most clinicians estimate the GFR from the serum creatinine concentration . However , the accuracy of this estimate is limited because the serum creatinine concentration is affected by factors other than creatinine filtration ( 2 , 3 ) . To circumvent these limitations , several formulas have been developed to estimate creatinine clearance from serum creatinine concentration , age , sex , and body size ( 4 - 12 ) . Despite more recent studies that have related serum creatinine concentration to GFR ( 13 - 24 ) , no formula is more widely used to predict creatinine clearance than that proposed by Cockcroft and Gault ( 4 ) . This formula is used to detect the onset of renal insufficiency , to adjust the dose of drugs excreted by the kidney , and to evaluate the effectiveness of therapy for progressive renal disease . More recently , it has been used to document eligibility for reimbursement from the Medicare End Stage Renal Disease Program ( 25 ) and for accrual of points for patients on the waiting list for cadaveric renal transplantation ( 26 ) . Major clinical decisions in general medicine , geriatrics , and oncology ( as well as nephrology ) are made by using the Cockcroft-Gault formula and other formulas to predict the level of renal function . Therefore , these formulas must predict GFR as accurately as possible . The Modification of Diet in Renal Disease ( MDRD ) Study , a multicenter , controlled trial , evaluated the effect of dietary protein restriction and strict blood pressure control on the progression of renal disease ( 27 - 30 ) . During the baseline period , GFR , serum creatinine , and several variables that affect the relation between them were measured in patients with chronic renal disease . The purpose of our study was to develop an equation from MDRD Study data that could improve the prediction of GFR from serum creatinine concentration . Methods Baseline Cohort and Measurement Methods in the Modification of Diet in Renal Disease Study The overall study design and methods of recruitment for the MDRD Study have been described elsewhere ( 31 , 32 ) . A total of 1785 patients entered the baseline period . Of these patients , 1628 ( 91 % ) also underwent measurement of GFR  and the other variables described below ; these patients constitute the study group for these analyses . Glomerular filtration rate was measured as the renal clearance of 125I-iothalamate ( 33 , 34 ) . Creatinine clearance was computed from creatinine excretion in a 24-hour urine collection and a single measurement of serum creatinine . Serum and urine creatinine were measured by using a kinetic alkaline picrate assay with a normal range in serum of 62 to 124 mol/L ( 0.7 to 1.4 mg/dL ) ( 35 ) . Glomerular filtration rate and creatinine clearance were expressed per 1.73 m2 of body surface area by multiplying measured values by 1.73/body surface area ( 36 ) . The serum and urine specimens were also used for other measurements , including serum albumin ( bromcresol green method [ 35 ] ) , serum urea nitrogen ( urease method [ 35 ] ) , and urine urea nitrogen ( urease method [ 35 ] ) . Protein intake ( g/d ) was estimated as 6.25 [ UUN ( g/d ) + 0.031 ( g/kg per day ) SBW ( kg ) ] , where UUN is urine urea nitrogen , SBW is st and ard body weight , and 0.031 g/kg per day is a constant reflecting the rate of excretion of nitrogen in compounds other than urine urea ( 37 , 38 ) . The diagnosis of diabetes and the cause of renal disease were assigned on the basis of chart review at the clinical center ( 39 ) . Statistical Analysis Descriptive Statistics The relation of renal function measurements to other baseline characteristics was assessed by using contingency tables , t-tests , analysis of variance , and linear regression , as appropriate . Nonparametric tests ( Wilcoxon rank-sum tests and Kruskal-Wallis tests ) gave consistent results . A P value less than 0.01 was considered statistically significant . Multivariable Analysis of Glomerular Filtration Rate We used stepwise multiple regression to determine a set of variables that jointly predicted GFR . The stepwise regression models were developed by using a training sample consisting of a r and om sample of 1070 of the 1628 patients . We found that the variability of the difference between the observed and predicted GFR values was greater for higher GFR values . This increase was eliminated by performing multiple regressions on log-transformed data . To facilitate clinical interpretation , the results were re-expressed in terms of the original units . Consequently , the prediction equation is a multiplicative model ; regression coefficients refer to the change in geometric mean GFR associated with unit changes in the independent variable . Predicted GFR is expressed in mL/min per 1.73 m2 . The following variables were considered for possible inclusion in the regression model : weight , height , sex , ethnicity , age , diagnosis of diabetes , serum creatinine concentration , serum urea nitrogen level , serum albumin level , serum phosphorus level , serum calcium level , mean arterial pressure , urine creatinine level , urine urea nitrogen level , urine protein level , and urine phosphorus level . The cause of renal disease was not included because in clinical practice , the cause may be unknown or clinicians may not use the same classification method as the investigators in the MDRD Study . A P value less than 0.001 was used as the criterion for entry of a variable into the model . Because of the difficulty in collecting complete 24-hour urine sample s in clinical practice , an additional stepwise regression was performed to develop a prediction model that did not include urine biochemistry variables . Finally , because of the interest in developing a prediction equation to assess eligibility for Medicare reimbursement and listing for cadaveric renal transplantation , we repeated the analysis restricting the population to the subgroup of patients with higher serum creatinine concentrations ( > 221 mol/L [ 2.5 mg/dL ] ; n=509 in the training sample ) . Methods for Comparing Equations To Predict Glomerular Filtration Rate We first developed coefficients for each prediction equation ( including the selection of the predictor variables for the stepwise regressions ) using the data from the training sample to predict log GFR . Each prediction equation also included a multiplicative constant to account for any consistent bias in the application of that equation in the MDRD Study Group . This was particularly important for equations that are intended to estimate creatinine clearance , which is known to be higher than GFR . The regression coefficients determined in the training sample were then applied to obtain predicted GFRs in a separate validation sample consisting of the remaining 558 patients ( 172 patients with serum creatinine concentration>221 mol/L [ 2.5 mg/dL ] ) . These predicted GFR values were compared with the actual GFRs in the validation sample to evaluate the performance of each prediction equation . In this way , separate data sets were used to construct the equations and assess their accuracy after removal of systematic bias . For each equation , we computed overall R 2 ( percentage of variability in log GFR explained by the regression model ) and the 50th , 75th , and 90th percentiles of the distribution of the percentage absolute difference between measured and predicted GFRs in the validation sample . The 50th percentiles indicate the typical size of the errors in prediction of GFR , and the 75th and 90th percentiles assess the sizes of the larger errors that occurred for each model . Development of Final Prediction Equations To improve the accuracy of the final MDRD Study prediction equations , the regression coefficients derived from the training sample were up date d on the basis of data from all 1628 patients . As a result , the st and ard errors of the regression coefficients in the final MDRD Study prediction equations are slightly smaller than those derived from the training sample ; thus , the accuracy of the final prediction equations may be slightly better ( by about 0.1 % to 0.2 % ) than their accuracy as assessed in the validation sample . Results Demographic and Clinical Characteristics The mean age ( SD ) of the cohort was 50.6 12.7 years . Sixty percent of patients were male , 88 % were white , and 6 % were diabetic . Causes of renal disease were glomerular disease ( 32 % ) , polycystic kidney disease ( 22 % ) , tubulointerstitial disease ( 7 % ) , and other or unknown renal diseases ( 40 % ) . Mean protein intake was 0.99 0.24 g/kg of body weight per day and mean arterial pressure was 99.4 12.2 mm Hg . Mean weight was 79.6 16.8 kg , body surface area was 1.91 0.23 m2 , serum urea nitrogen concentration was 11.4 5.7 mmol/L [ 32 16 mg/dL ] , and serum albumin concentration was 40.0 4.0 g/L [ 4.0 0.4 g/dL ] , respectively . Glomerular Filtration Rate , Creatinine Clearance , and Serum Creatinine Concentration Renal function measurements for the study group and for various subgroups are shown in Table 1 . Mean GFR for the population was 0.38 mL s 2 m 2 ( 39.8 mL/min per 1.73 m2 ) , with lower values in patients with lower protein intake , white patients compared with black patients , and older patients ( 55 years ) compared with younger patients ( P<0.01 ) . The mean value of creatinine clearance was 0.81 mL s 2 m 2 ( 48.6 mL/min per 1.73 m2 ) and was lower in older patients and patients with lower protein intake ( P 0.01 ) . The mean serum creatinine concentration was 203 mol/L ( 2.3 mg/dL ) and was higher in men , patients with lower protein intake , and patients with higher mean arterial pressure ( P 0.01 ) . Figure 1 shows the well-known reciprocal relation of serum creatinine concentration to GFR for subgroups based on sex and ethnicity . At any given GFR , the serum creatinine concentration is significantly higher in men than in women and in black persons than in white persons ( P<0.001 ) . Table 1 . Association of Renal BACKGROUND Appropriate antimicrobial dosing for patients receiving continuous venovenous hemofiltration ( CVVH ) is complex . Pharmacist participation in antimicrobial dosing adjustment for patients receiving CVVH may be advantageous . METHODS A comparative study was performed in a China hospital intensive care unit ( ICU ) . Patients receiving CVVH in the intervention group received antimicrobial dosing adjustment service by pharmacists from January 2012 to June 2012 , whereas patients in the control group received routine medical care between July 2012 and December 2012 . The primary outcomes including patients ' length of ICU stay , mortality in ICU , ICU hospitalization costs , and the occurrence of adverse drug events ( ADEs ) were then compared . RESULTS 87 and 93 patients were included in the control and intervention groups . During the intervention period , pharmacists made 256 antimicrobial dosing adjustment recommendations for 93 enrolled patients receiving CVVH , of which 224 ( 87.5 % ) recommendations were accepted by physicians . Changing in CVVH-related variables ( 175 , 68.4 % ) were the most common risk factors for dosing errors , whereas  
    Output:
    
IP has led to poor patient outcomes .