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NDT Advance Access first published online on October 7, 2008
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Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfn547
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© The Author [2008]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org


Predictive factors of progression to chronic kidney disease stage 5 in a predialysis interdisciplinary programme

Cristina M. Soares1, José Silvério S. Diniz1, Eleonora M. Lima1, Gilce R. Oliveira1, Monica R. Canhestro1, Enrico A. Colosimo2, Ana Cristina Simoes e Silva1 and Eduardo A. Oliveira1

1 Pediatric Nephrourology Unit 2 Department of Statistics, Hospital das Clínicas, Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil

Correspondence and offprint requests to: Eduardo A. Oliveira, Rua Engenheiro Amaro Lanari 389/501, Belo Horizonte-Minas Gerais, Postal Code: 30.310.580. Tel: +55-31-32851056; Fax: +55-31-32851056; E-mail: eduolive{at}medicina.ufmg.br



   Abstract
Top
 Abstract
Introduction
 Patients and methods
 Results
 Discussion
 References
 
Background. The clinical course of chronic kidney disease (CKD) in children is heterogeneous and has not been fully established. The aim of this retrospective cohort study was to identify predictive factors associated with the progression of CKD among the children and adolescents admitted to a Predialysis Interdisciplinary Management Programme (PDIMP).

Methods. We analysed the following variables at admission: age, gender, race, blood pressure, primary renal disease, Z-scores for weight and height, CKD stage and degree of proteinuria. Two time-dependent covariates were considered: hypertension and proteinuria. CKD stage 5 was assigned as a dependent variable. Time-fixed and time-dependent Cox regression analyses were applied to evaluate renal survival.

Results. One hundred and seven patients with CKD stage 2–4 were followed up for a median time of 94 months. Fifty-seven patients (53.3%) progressed to CKD stage 5. After adjustment for time-fixed model, three baseline variables were found to be independent predictors of CKD stage 5: glomerular disease (hazard ratio, HR = 3.0, P = 0.015), CKD stage 4 (HR = 2.6, P = 0.001) and severe proteinuria (HR = 4.1, P = 0.006). After adjustment for the time-dependent model, three variables were found to be independent predictors of CKD stage 5: proteinuria as time-dependent covariate (HR = 1.9, P = 0.041), CKD stage 4 (HR = 2, P = 0.0086) and baseline serum albumin <3.5 g/dl (HR = 2.6, P = 0.0015).

Conclusions. Taking into account manageable factors, further prospective controlled studies are necessary to assess intervention measures in order to possibly modify the clinical course of CKD in children.

Keywords: chronic kidney disease; glomerulonephritis; hypertension; outcome; proteinuria



   Introduction
Top
 Abstract
 Introduction
Patients and methods
 Results
 Discussion
 References
 
The clinical course of chronic kidney disease (CKD) in children is heterogeneous and has not been fully established. In addition, CKD in children still carries a significant clinical and social impact [1,2]. Identification of modifiable risk factors to slow progression of CKD in children possibly will help to reduce the burden of CKD and may yield further insights into risk factors for progression in adults before the onset of multiple comorbidities [3–5]. Few sizable studies of CKD have been performed in children, and relatively little is known about the natural history at early stages of CKD in this population [6]. Information about predictors of stable or deteriorating kidney function in children is also scarce [7]. Some risk factors such as the degree of proteinuria and the severity of hypertension are well-established variables associated with kidney function deterioration in adults [4,8]. In paediatric populations, there are also some studies supporting these findings [9,10].

We have recently described in a retrospective cohort study the clinical course of 107 children and adolescents admitted to our Predialysis Interdisciplinary Management Programme (PDIMP) [11]. In the present study, we extended our analysis by using time-fixed and time-dependent multivariable statistical models with the aim to identify variables that are independent predictors of progression to CKD stage 5.



   Patients and methods
Top
 Abstract
 Introduction
 Patients and methods
Results
 Discussion
 References
 
Patients
In this retrospective cohort study, the records of 107 patients diagnosed with CKD who were admitted to a PDIMP from 1990 to 2005 were retrospectively analysed. Inclusion criteria were an estimated glomerular filtration rate (eGFR) ≤75% of the value expected for their age according to normal reference data [2] and at least 6 months of follow-up. Estimated GFR was calculated by the formula of Schwartz et al. [12]. The programme consisted of conservative management of children and adolescents with CKD and was conducted by an interdisciplinary team including paediatric nephrologists, paediatricians, nurses, psychologists, nutritionists and social workers. After the initial investigation, patients were followed according to a systematic clinical protocol described in detail in previous reports [11,13]. Primary renal diseases were managed according to protocols described elsewhere [14,15].

Baseline covariates
The following variables were included in univariate analysis: age at admission, gender, race, decade of admission, systolic and diastolic blood pressure, primary renal disease, weight for age Z-score (WAZ), height for age Z-score (HAZ), body mass index (BMI), CKD at entry, degree of proteinuria (absent/mild versus severe) and use of angiotensin-converting enzyme inhibitors (ACEI). The variables studied are shown in Table 1. The codes used for dichotomous variables were 1 (presence) and 0 (absence). Continuous variables were dichotomized either using traditional cut-off levels (blood pressure, WAZ, HAZ) or by the first quartile cut point (haemoglobin, albumin).


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  		Table 1 Baseline clinical characteristics of 107 children and adolescents admitted to a Predialysis Interdisciplinary Programme

 
Time-dependent covariates
Two time-dependent covariates were included in the analysis: proteinuria and hypertension. During the follow-up period, a value of 0 was assigned to the variable if the individual had no proteinuria or hypertension during that period. Otherwise, a value of 1 was assigned after the onset of persistent proteinuria and/or hypertension.

Definitions
Patients were classified into four groups according to primary renal disease: uropathies, glomerulonephritis, cystic/tubular disorders and miscellaneous. For analysis purposes, primary renal disease was dichotomized into two groups (glomerulonephritis versus others). Ethnicity (race variable in analysis) was established by clinical examination according to skin colour, hair colour and texture. According to the Brazilian Institute of Geography and Statistics (IBGE), 98.3% of the Brazilian population has been categorized into three ‘races’: white (53.7%), black (6.2%) and an intermediate colour (38.4%) [16]. For analysis purposes, the black and intermediate colour categories were merged into a ‘non-white’ group. Blood pressure was measured according to the recommendations of the Fourth Task Force on Blood Pressure in Children [17]. Blood pressure was standardized for age, gender and height using the Task Force tables, and the 95th percentile was used as the cut-off point [17]. Proteinuria at baseline was classified into three categories: absent, mild [urinary protein excretion of <1 g/day or a urinary albumin/creatinine (UaUc) ratio < 2] and severe (urinary protein excretion of >1 g/day or a UaUc ratio >2). WAZ and HAZ scores were used to assess weight and stature. These parameters were calculated using the public domain software Epi Info (version 3.4.1) provided on the World Wide Web by the Centers for Disease Control—Atlanta (CDC) [18].

Outcome
CKD stage 5 was assigned as a dependent variable. CKD stage 5 was defined as eGFR < 15 ml/min in three consecutives tests and/or the need for renal replacement therapy.

Statistical analysis
Renal survival was measured from the date of patient enrolment to the date of initiation of dialysis or to the date of first test of eGFR <15 ml/min. Potential prognostic variables were evaluated as predictors of survival in both time-fixed and time-dependent Cox models. In the time-fixed model, only the initial records at the time of entry into the PDIMP were applied. The time-fixed model analysis was conducted in two steps. In the first step, univariate analysis was performed to identify variables that were significantly associated with adverse outcome. Univariate analyses were performed using the Kaplan–Meier nonparametric survival function estimator [19]. Differences between patient subgroups were assessed by the two-sided log rank test. For the purpose of plotting the survival curves, continuous variables were categorized using either clinical traditional cut-off levels or levels dichotomized according to the third quartile. Cox's regression model was applied to identify variables that were independently associated with adverse outcome [20]. Only those variables that were found to be associated with adverse outcome by univariate analysis (P < 0.25) were included in Cox's regression model. Variables that met this criterion were entered into the multivariable model. Inclusion in the final model was determined by a backward stepwise process with the use of the likelihood ratio to evaluate the effect of omitting variables. SPSS software was used for the analysis. Values of P < 0.05 were considered significant, and 95% confidence intervals were provided when appropriate. Next, we fitted a multivariable time-dependent Cox model, including the time-dependent covariates. Variables selected for multivariable analyses were used to build a final model after checking for interactions and proportionality assumptions. Possible interactions between variables that remained in the final model were evaluated, including interaction terms in the model. Proportional hazard assumption was checked graphically by log-minus-log versus time plots for each variable [21,22].

Ethical aspects
The study was approved by the Ethics Committee of UFMG, and the parents or persons responsible for the children gave written informed consent to participate.



   Results
Top
 Abstract
 Introduction
 Patients and methods
 Results
Discussion
 References
 
A total of 107 patients were included in the analysis. The main baseline clinical characteristics are summarized in Table 1. The median age at admission to the PDIMP was 8.3 years (IQ range, 2.6–13.2). Median follow-up time was 10 years (IQ range, 3–12), and a total of 49 (46%) patients were followed up for >5 years. Fifty-seven patients (53.3%) progressed to CKD stage 5. As a whole, the estimated median renal survival time was 7.9 years (95% CI = 5.4–10.3). However, there was a remarkable difference among distinct primary renal diseases. The probability of reaching CKD stage 5 for patients with glomerular diseases was estimated to be 87% (95% CI = 42–100%), by 10 years after admission to the PDIMP. The probabilities of CKD stage 5 for patients with uropathies, cystic/tubular disorders and miscellaneous diseases were 60% (95% CI = 32–88%), 39% (95% CI = 18–60%) and 33% (95% CI = 12–54%), respectively.

Time-fixed model
As shown in Table 2, in univariate survival analysis, nine variables were suitable for inclusion in the time-fixed multivariable model: non-white race, age at admission >30 months, glomerulonephritis as primary renal disease, CKD stage 4, HAZ <1.88, severe proteinuria, haemoglobin <10 mg/dl, serum albumin <3.5 g/dl and use of ACEI. The model derived from the time-fixed approach is given in Table 3. After adjustment, only three variables were found to be independent predictors of CKD stage 5: glomerulonephritis as primary renal disease, CKD stage 4 and severe proteinuria. There was no interaction between the remaining variables in the final model. The following interactions were tested: primary renal disease versus CKD stage, primary renal disease versus proteinuria and proteinuria versus CKD stage. Figure 1 illustrates the combined effect of renal primary disease and CKD stage on renal survival. Patients with CKD stage 2/3 and non-glomerular disease had an estimated median renal survival time of 10 years (95% CI = 7–13.3 years) whereas for children with CKD stage 4 and non-glomerular disease the estimated median renal survival time was 6.7 years (95% CI = 2–11.3 years). On the other hand, patients with CKD stage 2/3 and glomerular disease had an estimated median renal survival time of 3.3 years (95% CI = 1.8–4.8 years) whereas for children with CKD stage 4 and glomerular disease the estimated median renal survival time was only ~6 months (95% CI = 5–7 months) (log-rank = 68, P < 0.001). Figure 2 illustrates the combined effect of the three factors on renal survival. The median renal survival time was 12 years for patients without any of the three predictive factors (CI 95% = 8.5–15.7), 6.7 years (CI 95% = 2.9–10.4) for patients who presented one factor, 1.9 years (CI 95% = 0.1–4.4) for patients who presented two predictive factors and only 7 months (CI 95% = 0.1–1.2) for children with all three predictive factors (Figure 2). All six patients who presented the three factors progressed to CKD stage 5 whereas only 34% (5 out of 17) of patients without any predictive factors presented CKD stage 5. As shown in Table 4, the relative risk of CKD stage 5 increased according to the presence of the predictive factors. Patients with a single factor had a risk 2.5 times greater to progress to CKD stage 5 than patients without any predictive factor whereas in patients with all three factors the risk increased 25 times.


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  		Table 2 Univariate analysis of predictive factors of CKD stage 5

 

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  		Table 3 Multivariable model based on a time-fixed approach (baseline covariates)

 

Figure 1
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  		Fig. 1 Kaplan–Meier survival curves showing the combined effect of renal primary disease and CKD stage on renal survival.

 

Figure 2
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  		Fig. 2 Kaplan–Meier survival curves showing the combined effect of the presence of glomerular disease, CKD stage 4 and severe proteinuria on renal survival.

 

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  		Table 4 Relative risk of progression to CKD stage 5

 
Time-dependent model
During follow-up of 43 patients without proteinuria at baseline, 13 (30%) developed mild proteinuria at a median time of 6 years (IQ range, 2.6–9.5). Twenty-six (40.6%) of 64 normotensive patients at entry developed hypertension during follow-up at a median time of 5.5 years (IQ range, 2.3–8). The model derived from the time-dependent approach is given in Table 5. After adjustment for the time-dependent model, three variables were found to be independent predictors of CKD stage 5: proteinuria (as a time-dependent covariate), CKD stage 4 and baseline serum albumin < 3.5 g/dl. There was no interaction between the remaining variables in the final model. The following interactions were tested: albumin versus CKD stage, albumin versus proteinuria and proteinuria versus CKD stage.


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  		Table 5 Multivariable model based on a time-dependent approach

 


   Discussion
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
References
 
In this retrospective cohort study, we investigated possible independent predictive factors of CKD stage 5 in a paediatric population admitted to a PDIMP. Two Cox models were used to analyse these data. Both models consider CDK stage 4 at entry to be associated with an increased relative risk of the need for renal replacement therapy or progression to CKD stage 5. In addition, the time-fixed Cox model showed that glomerular disease and severe proteinuria were also associated with this outcome. In the time-dependent Cox model, two other variables were associated with the outcome: proteinuria as a time-dependent covariate and baseline serum albumin <3.5 g/dl.

The main limitation of this study is its retrospective design. Consequently, there was a limited control over the measurements of baseline variables included in the analysis. For instance, proteinuria was not uniformly obtained by the determination of 24-h proteinuria excretion in all patients at baseline. Thus, this fact precluded the insertion of this important predictor factor as a continuous variable in the Cox model. We are also aware of the limitations of the eGFR formula, especially in infants and for higher GFR values [23]. There are some intrinsic variabilities and limitations of indirect measures of renal function that are based on serum creatinine. For example, Hellerstein and co-workers [24] have compared an optimized form of the Schwartz equation versus a concurrent iothalamate GFR and showed that the standard deviation (SD) of the difference of these two methods was ~10 ml/min/1.73 m2; in other words, the relative error of the GFR (defined as ±2 SD) estimated by the Schwartz formula approximat- ed ±20 ml/min/1.73 m2. More recently, Mattmann et al. [25] undertook a retrospective review of data on patients who had both a two-point single injection of technetium-99m-diethylenetriaminepentaacetic acid (99mTc-DTPA) nuclear GFR measurement and a serum Cr measurement. They compared their findings with seven previously published formulas with respect to their performance in estimating the true or measured GFR. The authors concluded that the best accuracy of any of the formulas currently used to estimate GFR in adult or paediatric patients rarely reaches the level of 80% of values closer than ±30% to the real GFR. Therefore, for a GFR of 75 ml/min/1.73 m2, this degree of uncertainty encompasses a wide range of GFR values from <60 to >90 ml/min/1.73 m2, or CKD stages 1–3. In our series, at baseline 68 patients (63.5%) were classified into CKD stage 2/3 and the remaining 39 children were classified into CKD stage 4. Nevertheless, we cannot preclude some misclassification of CKD stage at baseline, mainly involving patients with borderline values. However, we believe that some features of our study design may increase the strength of our findings. All patients underwent sequential serum creatinine measurements at intervals of ~3 months, and a complete physical examination was performed at the same time, including height and weight evaluation. Thus, considering a median follow-up time of 10 years, each patient underwent ~30–40 serum creatinine measurements and eGFR calculations. For this reason, we believe that serial evaluations of eGFR could minimize the misclassification of CKD stage of each patient at the end of follow-up.

A number of studies on adult populations have demonstrated that some factors are consistently associated with progression to end-stage renal disease (ESRD) such as hypertension, proteinuria, renal function at baseline, primary renal disease and male gender [8,26–32]. Nevertheless, few studies have investigated predictive factors of progression of CKD in the paediatric population [7,9,10].

The possibility that proteinuria may accelerate kidney disease progression to end-stage renal failure has received support from the results of increasing numbers of experimental and clinical studies [33]. Proteinuria at baseline has been postulated as a strong independent predictor of the progression of CKD in adult and paediatric populations [30,34–36]. Ruggenenti et al. [37] have shown that the urinary protein excretion rate is the best independent predictor of both CKD progression and ESRD in non-diabetic proteinuric chronic nephropathies. In our analysis, children with severe proteinuria had a median renal survival time of only ~1.9 years as compared with a median renal survival time of ~9.9 years for those with mild or absent proteinuria at baseline (Table 2). Wingen et al. [9] reported the results of a European multicentre randomized study designed to assess the effects of a protein-restricted diet on the rate of CKD progression. In that study, multivariable regression analysis showed that proteinuria was the most important predictor of an increase in creatinine. Locatelli et al. [4] have shown that the probability of ESRD became progressively higher in patients with 24-h protein excretion >3 g compared with patients with 24-h proteinuria <1 g. This effect seemed to occur irrespective of the underlying type of kidney disease. Although severe proteinuria is more frequently related to glomerular diseases, in our multivariable analysis, both variables remained in the final time-fixed model predictive of progression to CKD stage 5 (Table 3). This finding is consistent with studies designed to assess the role of underlying nephropathy in the progression of renal disease, in which chronic glomerulonephritis emerged as an independent predictor of CKD progression with chronic glomerulonephritis also being an independent predictor of CKD progression [8]. Of note, in our analysis proteinuria remained as an independent predictor of CKD stage 5 in both final models. In the time-fixed model, only severe proteinuria at baseline was associated with progression to CKD stage 5. Nevertheless, in the time-dependent model, the presence of any degree of proteinuria, independently of whether it occurred at entry or during follow-up, was significantly associated with the outcome. Proteinuria is a marker of renal injury, reflecting loss of normal permselectivity. Whether proteinuria is merely a marker of injury or a contributor to progressive injury has been debated [38,39]. Randomized controlled trials have revealed consistent effects of ACEI in slowing the progression of non-diabetic kidney disease, although the effect of treatment was modified by the degree of urinary protein excretion [35,40–42]. Nevertheless, studies on the paediatric population regarding the effect of ACEI in slowing the progression of CKD are scarce. Some clinical studies have shown that ramipril appears to be an effective and safe antiproteinuric agent in children with CKD associated with hypertension or proteinuria, or both [43,44]. However, in a select paediatric population, Ardissino et al. [45] concluded that ACEI treatment does not significantly modify the naturally progressive course of hypodysplastic nephropathy.

Our findings support previous studies that found renal function at baseline as an independent predictor of the progression of CKD [2]. In the present study, patients admitted to our PDIMP with CKD stage 4 had a shorter median renal survival time. Children in stage 2 or stage 3 had about double the median renal survival time compared to patients with CKD stage 4 (Table 2). There is no surprise in finding that patients with stage 4 CKD reach stage 5 CKD more rapidly than patients with stage 2 or 3 CKD. However, we believe that it is important to control for the effect of this variable in statistical modelling. In our analysis, baseline renal function was also an independent predictor of progression to CKD stage 5 as demonstrated by multivariable analysis (Tables 3 and 5). Some studies have shown that, irrespective of the underlying kidney disease or the presence of additional risk factors, it is clear that the risk of progression of CKD in childhood is inversely proportional to baseline creatinine clearance [2,7,36,46].

We also evaluated the combined effect of primary renal disease and CKD stage at baseline on renal survival. Patients with glomerular disease had a shorter median renal survival than children with non-glomerular diseases. In our series, the median eGFR at admission was 37 ml/min/ 1.73 m2 and 36% of patients were admitted in CKD stage 4. As a whole, we estimated that the median renal survival time was ~4.4 years for children with CKD stage 4 but only ~6 months for patients with glomerular diseases. These findings emphasize the necessity of prompt recognition of children with CKD, especially those with associated high risk features such as severe proteinuria and glomerular disease. Therefore, the health team could plan a timely and appropriate intervention to possibly delay the decline of renal function. Nevertheless, this is a complex question in the paediatric population since many children, particularly those with glomerular disorders, may present a rapid progressive disease or an advanced disease since the beginning.

Studies on adults have shown that hypertension is associated with a faster rate of decline in renal function [4,30]. Moreover, a beneficial effect of lowering blood pressure has been demonstrated for the preservation of renal function [34,47]. In the paediatric setting, Mitsnefes et al. [10] have shown that hypertension is a highly significant and independent predictor of progression of CKD. However, in our models neither blood pressure levels at baseline nor hypertension as a time-dependent cofactor was significantly related to renal survival. This may have been due to the size of the sample or more probably to the effect of antihypertensive drugs not included in our analysis.

In conclusion, we assessed many potential predictive factors by the proportional hazard model, and at baseline, primary renal disease, CKD stage and proteinuria were identified as significant predictors of progression to CKD stage 5. On the other hand, in a time-dependent model, besides CKD stage, proteinuria as the time-dependent variable and albumin at entry were also associated with adverse outcome.



   Acknowledgments
 
This study was partially supported by FAPEMIG and CNPq. The authors owe special thanks to the team of the Predialysis Interdisciplinary Management Programme (HC-UFMG), Vanessa Rodrigues da Silva, Andréa Marques Chiaretti Munair and Marilene Moreira for their inestimable participation in the care of our patients.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 

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Received for publication: 22.11.07
Accepted in revised form: 5. 9.08


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