***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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    ***EXAMPLES*** 
    Input: Treatment goals for patients with CKD are often unrealized for many reasons , but support by nurse practitioners may improve risk factor levels in these patients . Here , we analyzed renal endpoints of the Multifactorial Approach and Superior Treatment Efficacy in Renal Patients with the Aid of Nurse Practitioners ( MASTERPLAN ) study after extended follow-up to determine whether strict implementation of current CKD guidelines through the aid of nurse practitioners improves renal outcome . In total , 788 patients with moderate to severe CKD were r and omized to receive nurse practitioner support added to physician care ( intervention group ) or physician care alone ( control group ) . Median follow-up was 5.7 years . Renal outcome was a secondary endpoint of the MASTERPLAN study . We used a composite renal endpoint of death , ESRD , and 50 % increase in serum creatinine . Event rates were compared with adjustment for baseline serum creatinine concentration and changes in estimated GFR were determined . During the r and omized phase , there were small but significant differences between the groups in BP , proteinuria , LDL cholesterol , and use of aspirin , statins , active vitamin D , and antihypertensive medications  , in favor of the intervention group . The intervention reduced the incidence of the composite renal endpoint by 20 % ( hazard ratio , 0.80 ; 95 % confidence interval , 0.66 to 0.98 ; P=0.03 ) . In the intervention group , the decrease in estimated GFR was 0.45 ml/min per 1.73 m(2 ) per year less than in the control group ( P=0.01 ) . In conclusion , additional support by nurse practitioners attenuated the decline of kidney function and improved renal outcome in patients with CKD BACKGROUND AND OBJECTIVES It is unclear how to optimally care for chronic kidney disease ( CKD ) . This study compares a new coordinated model to usual care for CKD . DESIGN , SETTING , PARTICIPANTS , & MEASUREMENTS A r and omized trial in nephrology clinics and the community included 474 patients with median estimated GFR ( eGFR ) 42 ml/min per 1.73 m(2 ) identified by laboratory-based case finding compared care coordinated by a general practitioner ( controls ) with care by a nurse-coordinated team including a nephrologist ( intervention ) for a median ( interquartile range [ IQR ] ) of 742 days . 32 % were diabetic , 60 % had cardiovascular disease , and proteinuria was minimal . Guided by protocol s , the intervention team targeted risk factors for adverse kidney and cardiovascular outcomes . Serial eGFR and clinical events were tracked . RESULTS The average decline in eGFR over 20 months was -1.9 ml/min per 1.73 m(2 ) . eGFR declined by ≥4 ml/min per 1.73 m(2 ) within 20 months in 28 ( 17 % ) intervention patients versus 23 ( 13.9 % ) control patients . Control of BP , LDL , and diabetes were comparable across groups . In the intervention group there was a trend to greater use of renin-angiotensin blockers  and more use of statins  in those with initial LDL > 2.5 mmol/L. Treatment was rarely required for anemia , acidosis , or disordered mineral metabolism . Clinical events occurred in 5.2 % per year . CONCLUSIONS Patients with stage 3/4 CKD identified through community laboratories largely had nonprogressive kidney disease but had cardiovascular risk . Over a median of 24 months , the nurse-coordinated team did not affect rate of GFR decline or control of most risk factors compared with usual care 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 
    Output:
    
Conclusion To conclude , dialysis-based studies report faster mean annual eGFR decline during pre-dialysis than CKD 3 - 5 cohorts .