- Email: [email protected]
Hematocrit, urea and gender: The Hematocrit, Urea and GEnder formula for prognosing progressive renal failure in diabetic nephropathy
European Journal of Internal Medicine 23 (2012) 283–286
Contents lists available at SciVerse ScienceDirect
European Journal of Internal Medicine journal homepage: www.elsevier.com/locate/ejim
Original article
Hematocrit, urea and gender: The Hematocrit, Urea and GEnder formula for prognosing progressive renal failure in diabetic nephropathy Nicolás Roberto Robles a,⁎, Flavio Ferreira b, Rocio Martinez-Gallardo b, Joaquin Alvarez Gregori a, Emilio Sanchez-Casado a, Juan Jose Cubero b, Juan Macias a a b
Cardiovascular Risk Chair, University of Salamanca School of Medicine, Salamanca, Spain Nephrology Department, Hospital Infanta Cristina, Badajoz, Spain
a r t i c l e
i n f o
Article history: Received 26 July 2011 Received in revised form 25 September 2011 Accepted 20 October 2011 Available online 21 November 2011 Keywords: Diabetic nephropathy HUGE score Predictive model
a b s t r a c t Objective: Diabetic nephropathy is a common cause of end stage renal disease. Notwithstanding, wide interindividual variations in the speed of progression of diabetic nephropathy are frequent. We have used the score of the HUGE formula to predict progression of kidney disease in a group of diabetic nephropathy patients. Design and methods: The sample consisted of 84 type 2 diabetic patients. At treatment entry, the mean age was 62.1 ± 12.5 years and 59.5% were male. Blood pressure was measured at office at each visit. Serum creatinine, urea, hematocrit and 24 h proteinuria were analyzed every 6 months. HUGE score was calculated from gender, urea and hematocrit. Results: Mean HUGE score was 0.99 ± 3.88. Using as cut off point 1.5, those patients who had a score equal or higher (n = 31) showed a bigger increase in serum creatinine after one year (41.8 ± 62.1%) than those subjects with score b 1.5 (n = 53) (18.7 ± 38.6%, p = 0.041). 5 patients with low HUGE score reached end stage renal failure (9.4%) and 10 patients in the high HUGE score group (32.3, p = 0.008). When logistic regression analysis was performed only a HUGE score higher than 1.5 (p = 0.003) and proteinuria higher than 2 g/day (p = 0.041) were independently associated to CRF progression (creatinine increment > 25%). Conclusions: In diabetic nephropathy patients the HUGE equation may be useful to detect the subjects prone to progressive renal failure. Wider samples will be needed to confirm this finding and, most important, its applicability to other kinds of nephropathy. © 2011 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
1. Introduction Diabetic nephropathy occurs in 20–40% of patients with diabetes and is the single leading cause of end-stage renal disease (ESRD) in most developed countries. Persistent albuminuria in the range of 30–299 mg/24 h (microalbuminuria) has been shown to be the earliest stage of diabetic nephropathy in type 1 diabetes and a marker for development of nephropathy in type 2 diabetes [1]. Patients with microalbuminuria who progress to macroalbuminuria (≥300 mg/ 24 h) are likely to progress to ESRD over a period of years [2,3]. Notwithstanding, wide inter-individual variations in the speed of progression of diabetic nephropathy are frequent. In fact, estimated glomerular filtration rate (eGFR) is not sufficient for clinical decision making [4,5]. The HUGE formula with data obtained from a general population, offers a straightforward readily available and inexpensive method based on hematocrit plasma serum urea levels and gender [6]. It is ⁎ Corresponding author at: Servicio de Nefrologia, Hospital Infanta Cristina, Carretera de Portugal s/n. 06070, Badajoz, Spain. Tel./fax: + 34 924218117. E-mail address: [email protected] (N.R. Robles).
more accurate than MDRD formulae to differentiate chronic renal failure (CRF) from eGFR b 60 ml/min/1.73 m 2. It has tested in databases with a total of 125.373 subjects. It is particularly useful in persons aged over 70 years of age and overcoming the disadvantages derived from the use of serum creatinine to calculate eGFR, since it reduces the diagnosis of CKD by a 10.46% in elderly people. In Spain, where it has been estimated a CRF prevalence using MDRD equation about 6.8% in general population (2.992.000 subjects) [7], the HUGE formula would cut this figure by more than 300.000 persons. We have used the score of the HUGE formula, which can be easily implemented in clinical practice, to predict progression of chronic kidney disease (CKD) in a group of diabetic nephropathy patients. 2. Material and methods The sample consisted of 84 type 2 diabetic patients who were treated with ACE inhibitors or ARB due to diabetic nephropathy between January of 2004 and December of 2008. Data were evaluated retrospectively. At treatment entry, the mean age was 62.1 ± 12.5 years and 59.5% were male. Criteria for diagnosis of diabetic nephropathy were the presence of overt proteinuria (albuminuria > 300 mg/day)
0953-6205/$ – see front matter © 2011 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.ejim.2011.10.014
N.R. Robles et al. / European Journal of Internal Medicine 23 (2012) 283–286
associated to diabetic retinopathy lesions in fundoscopy. Whenever diabetic retinopathy was not present at renal biopsy was performed to confirm de diagnosis of diabetic nephropathy (3 patients needed this invasive method). Patients with isolated microalbuminuria (albuminuria b 300 mg/24 h) were not recruited. Follow up time was 35.8 ± 12.5 months (median). Blood pressure was measured at office at each visit. Three measurements, taken at 3-min intervals in the sitting position, were averaged and used as the clinical BP reference value. To evaluate the degree of blood pressure control all measurements during the follow up were averaged. Heart rate was measured from the radial pulse during 30 s. Serum creatinine, urea, hematocrit and 24 h proteinuria were analyzed every 6 months. eGFR was calculated using the MDRD-4 formula. The mathematical expression of HUGE formulation is: L ¼ 2:505458–ð0:264418 HematocritÞ þ ð0:118100 UreaÞ ½þ1:383960ifmale:
Survival Functions 1,0
HUGE score < 1.5
0,8
Cum Survival
284
HUGE score > 1.5
0,6
0,4
0,2
0,0 0,00
10,00
20,00
30,00
40,00
50,00
FOLLOW UP (months) If L is lower than “0”, it means that the individual does not have CRF. If L is higher than “0”, it means that the individual have CRF. Absolute score of HUGE formulation was used as predictor of progressive CRF. For comparison we have used 1.5 as cutoff point since it was the third quartile of HUGE score value for patients whose renal function decreased more than 25%. Results are expressed as mean ± 1 standard deviation. Continuous values have been compared through paired Student “t” test. The chi square test was used for discrete data. All statistical tests were twosided. Binary logistic regression analysis was used to find the clinical and biochemical characteristics associated to CRF progression (defined as an increase ≥25% of serum creatinine). p values lower than 0.05 were considered as significant. Analysis was developed with the statistical package PASW 17.0. 3. Results Mean HUGE score was 0.99 ± 3.88. Using as cutoff point 1.5, those patients who had a score equal or higher (n = 31) showed a bigger increase in serum creatinine after one year (41.8 ± 62.1%) than those subjects with score b 1.5 (n = 53) (18.7 ± 38.6%, p = 0.041, Student t test). The changes in serum creatinine have been plotted in Fig. 1. In the same way, eGFR was reduced by an 11.2 ± 23.4% in the group with score b 1.5 vs. 24.4 ± 21.5% in the other group (p = 0.012, Student t test). Renal survival before starting renal replacement therapy was calculated through the Kaplan–Meier method (Fig. 2) comparing patients with HUGE score b 1.5 with those with score ≥ 1.5. Only 5 patients with lower HUGE score reached ESRD (9.4%, 95%IC
3 2,5
Fig. 2. Kaplan–Meier analysis of renal survival before ESRD. Differences are significant (p = 0.008, Square chi test).
4.1–20.3), comparatively 10 patients in the high HUGE score group reached the same CKD stage (32.3%, 95%IC 18.6–49.9, p = 0.008, chi square test). Conversely, when patients were compared by a serum creatinine increase higher than 25% at one year, the HUGE score was lower in those with a lesser progression (n= 58, mean score 0.25± 3.65) than those which serum creatinine increased more than 25% (n= 26; mean score 2.70 ± 3.94, p = 0.007, Student t test). The evolution of changes in HUGE score has been plotted in Fig. 2. Contrariwise, eGFR was not different between both groups (serum creatinine increment≥ 25%, 52.3± 24.7 ml/min; serum creatinine incrementb 25%, 60.2 ± 25.2 ml/ min, p = 0.182, Student t test). Logistic regression analysis results are showed in Table 1. Systolic blood pressure ≥ 140 mm Hg, eGFR b 60 ml/min, proteinuria ≥ 2 g/day, gender, age ≥ 65 years and HUGE score were introduced as causative variables. Only a HUGE score higher than 1.5 (p= 0.003) and proteinuria higher than 2 g/day (p= 0.041) were independently associated to CRF progression (creatinine increment> 25%).
4. Discussion HUGE formula was developed and validated to improve the diagnosis of CRF in general population. This model use clinical and laboratory data that are obtained routinely in patients with CKD (hematocrit, serum urea and gender) and it could be easily integrated into a laboratory information system or a clinic health record. Our data suggest that this formulation could also be used for prognostic purposes in diabetic nephropathy patients.
2 1,5
Table 1 Logistic regression analysis.
1 0,5 0
0
12
24 Months HUGE < 1.5
36
48
HUGE > 1.5
Fig. 1. Changes in serum creatinine comparing subjects with HUGE score b 1.5 vs. those with score ≥ 1.5. Differences are significant (p = 0.041, Student t test).
HUGE score Age ≥ 65 years Gender SBP ≥ 140 mm Hg eGFR b 60 Proteinuria ≥ 2 g/24 h CI: Confidence interval.
B
CI low limit
CI upper limit
p
2.718 − 0.848 0.269 − 1.133 1.023 1.431
1.809 − 1.563 − 0.479 − 1.818 0.122 0.732
3.627 − 0.133 1.017 − 0.448 1.924 2.130
0.003 0.236 0.719 0.098 0.256 0.041
N.R. Robles et al. / European Journal of Internal Medicine 23 (2012) 283–286
Although diabetic nephropathy has a bad prognosis regarding renal survival [8], there can be considerable individual heterogeneity in the risk for progression to kidney failure. Risk prediction for CRF progression has gained increasing attention over the last years, with emerging literature suggesting improved patient outcomes with individualized risk prediction and with advances in information technology that allow for easy implementation of risk prediction models as components of electronic clinical records [9–11]. The availability of these risk prediction tools and their inclusion in clinical practice guidelines may get better adherence to treatment guidelines and have encouraged individual decision making [12]. Despite these benefits, the lack of easily applicable and externally validated models has delayed the widespread integration of risk prediction in all fields of medicine [13,14]. In this regards, HUGE formulation might offer a simple approach to identify the diabetic patients with worse prognosis in order to implement straighter treatment objectives and a closer follow up without any increased cost since this method does not need to add unusual clinical or biochemical measurements. Recently Tangri et al. [15] have developed and validated two predictive models for progression of CKD to kidney failure. The most accurate model included age, sex, estimated GFR, albuminuria, serum calcium, serum phosphate, serum bicarbonate, and serum albumin. This model was more accurate than a simple model that included age, sex, estimated GFR, and albuminuria. These models have been validated in a wide population and it must be recognized that our sample is rather smaller. Nevertheless, high statistical signification in a small sample reinforces the strong of results. Furthermore, HUGE equation is much simpler to calculate than the first Tangri's formulation, which needs more analytical parameters and, some of them, cumbersome to be measured (i.e. serum bicarbonate). The second Tangri's formula is easier to implement but still needs albuminuria measurement, an expensive issue. In this regard, HUGE formulation may have as advantage a reduced cost of implementation. It is well known that albuminuria provides additional prognostic information for progression to kidney failure [16]. Some studies have examined the use of estimated GFR and albuminuria in prediction models, with additional clinical and laboratory data, but these models are specific to a particular and different type of kidney disease [17–19]. Proteinuria was an independent baseline risk predictor for ESRD in this study. This predictive power has been found in other studies of patients with diabetic nephropathy [20–22]. In RENAAL, there was a nearly linear relationship between progression to ESRD and albuminuria at baseline and on treatment. Nevertheless, we found that the progression of CKD has a stronger statistical association with the HUGE score than with the severity of proteinuria. The anemia of chronic renal failure is not normally observed until the glomerular filtration rate drops to b40 ml/min and, as the renal function deteriorates, the anemia becomes more marked. [23,24]. The mechanisms that may contribute to this anemia include shortened red cell survival, decreased erythropoietin production, blood loss because of defective platelet function, and impaired erythropoiesis secondary to inhibitors or toxic metabolites [25]. The major explanation, however, is a relative erythropoietin deficiency resulting from an inability of the renal fibroblasts to produce erythropoietin to maintain the red cell mass in response to tissue hypoxia. Anemia has been recognized as a frequent complication of diabetic nephropathy, appearing earlier than in nondiabetic renal disease and amplifying the risks of cardiovascular and microvascular complications [26]. It is likely that the introduction of hemoglobin level in the equation have help to improve its prognostic efficacy, but it could made it less effective in non diabetic patients. This issue remains to be established in further studies. The HUGE equation may be useful to detect the subjects more prone to progressive renal failure, at least in diabetic nephropathy patients. Wider samples will be needed to confirm this finding and, most important, its applicability to other kinds of nephropathy.
285
Learning points • Diabetic nephropathy is the most common cause of end stage renal failure all over the world. • HUGE formulation was developed and validated to diagnosis renal failure without using serum creatinine and/or glomerular filtration rate. • HUGE score seems to be useful to detect those diabetic nephropathy patients prone to progressive renal failure.
Conflict of interest There is not any conflict of interest to be declared by anyone of the authors. References [1] Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, Jensen G, Clausen P, Scharling H, et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation 2004;110:32–5. [2] Gall MA, Hougaard P, Borch-Johnsen K, Parving HH. Risk factors for development of incipient and overt diabetic nephropathy in patients with non-insulin dependent diabetes mellitus: prospective, observational study. BMJ 1997;14:783–8. [3] Ravid M, Lang R, Rachmani R, Lishner M. Long-term renoprotective effect of angiotensin-converting enzyme inhibition in non-insulin-dependent diabetes mellitus: a 7-year follow-up study. Arch Intern Med 1996;156:286–9. [4] Hemmelgarn BR, Zhang J, Manns BJ, James MT, Quinn RR, Ravani P, et al. Nephrology visits and health care resource use before and after reporting estimated glomerular filtration rate. JAMA 2010;303:1151–8. [5] Jain AK, McLeod I, Huo C, Cuerden MS, Akbari A, Tonelli M, et al. When laboratories report estimated glomerular filtration rates in addition to serum creatinines, nephrology consults increase. Kidney Int 2009;76:318–23. [6] Alvarez-Gregori JA, Robles NR, Mena C, Ardanuy R, Macias-Nuñez JF. The value of a formula including haematocrit, blood urea and gender (HUGE) as a screening test for chronic renal insufficiency. J Nutr Health Aging 2011;15:480–4. [7] Otero A, de Francisco A, Gayoso P, García F, EPIRCE Study Group. Prevalence of chronic renal disease in Spain: results of the EPIRCE study. Nefrologia 2010;30: 78–86. [8] Robles N, Fernández-Carbonero E, Sánchez Casado E, Cuberto J. Growing incidence of diabetic nephropathy in the region of Badajoz during the period 1991–2006. Nefrologia 2009;29:244–8. [9] D'Agostino Sr RB, Grundy S, Sullivan LM, Wilson P, CHD Risk Prediction Group. I. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001;286:180–7. [10] Leslie WD, Morin S, Lix LM. A before-and-after study of fracture risk reporting and osteoporosis treatment initiation. Ann Intern Med 2010;153:580–6. [11] Pozen MW, D'Agostino RB, Selker HP, Sytkowski PA, Hood WB Jr. A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. N Engl J Med 1984;310:1273–8. [12] Selker HP, Beshansky JR, Griffith JL, Aufderheide TP, Ballin DS, Bernard SA, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACITIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. Ann Intern Med 1998;129:845–55. [13] Müller-Riemenschneider F, Holmberg C, Rieckmann N, Kliems H, Rufer V, MüllerNordhorn J, et al. Barriers to routine risk-score use for healthy primary care patients. Arch Intern Med 2010;170:719–24. [14] van Steenkiste B, van derWeijden T, Stoffers HE, Grol R. Barriers to implementing cardiovascular risk tables in routine general practice. Scand J Prim Health Care 2004;22:32–7. [15] Tangri N, Stevens LA, Griffith J, Tighiourarst H, Djurdjev O, Naymark S, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011;305:1553–9. [16] Chronic Kidney Disease Prognosis Consortium, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts. Lancet 2010;375:2073–81. [17] Geddes CC, Fox JG, Allison ME, Boulton-Jones JM, Simpson K. An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol Dial Transplant 1998;13:67–71. [18] Johnson ES, Thorp ML, Platt RW, Smith DH. Predicting the risk of dialysis and transplant among patients with CKD. Am J Kidney Dis 2008;52:653–60. [19] Mackinnon B, Fraser EP, Cattran DC, Fox JG, Geddes CC. Validation of the Toronto formula to predict progression in IgA nephropathy. Nephron Clin Pract 2008;109: c148–53. [20] Keane WF, Zhang Z, Lyle PA, Cooper ME, de Zeeuw D, Grunfeld JP, et al. Risk scores for predicting outcomes in patients with type 2 diabetes and nephropathy: the RENAAL study. Clin J Am Soc Nephrol 2006;1:761–7.
286
N.R. Robles et al. / European Journal of Internal Medicine 23 (2012) 283–286
[21] de Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, et al. Proteinuria, a target for renoprotection in patients with type 2 diabetic nephropathy: lessons from RENAAL. Kidney Int 2004;65:2309–20. [22] Lewis EJ, Hunsicker LG, Clarke WR, Berl T, Pohl MA, Lewis JB, et al. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med 2001;345:851–60. [23] Chandra M, Clemons GK, McVicar MI. Relation of serum erythropoietin levels to renal excretory function: evidence for lowered set point for erythropoietin production in chronic renal failure. J Pediatr 1988;113:1015–21.
[24] Ratke HW, Claussner A, Erbes PM, Scheuermann EH, Scheppe W, Koch KM. Serum erythropoietin concentration in chronic renal failure: relationship to degree of anemia and excretory renal function. Blood 1979;54:877–84. [25] Eschbach JW, Adamson JW. Modern aspects of the pathophysiology of renal anemia (review). Contrib Nephrol 1988;66:63–70. [26] Ritz E. Anemia and diabetic nephropathy. Curr Diab Rep 2006;6:469–72.
Contents lists available at SciVerse ScienceDirect
European Journal of Internal Medicine journal homepage: www.elsevier.com/locate/ejim
Original article
Hematocrit, urea and gender: The Hematocrit, Urea and GEnder formula for prognosing progressive renal failure in diabetic nephropathy Nicolás Roberto Robles a,⁎, Flavio Ferreira b, Rocio Martinez-Gallardo b, Joaquin Alvarez Gregori a, Emilio Sanchez-Casado a, Juan Jose Cubero b, Juan Macias a a b
Cardiovascular Risk Chair, University of Salamanca School of Medicine, Salamanca, Spain Nephrology Department, Hospital Infanta Cristina, Badajoz, Spain
a r t i c l e
i n f o
Article history: Received 26 July 2011 Received in revised form 25 September 2011 Accepted 20 October 2011 Available online 21 November 2011 Keywords: Diabetic nephropathy HUGE score Predictive model
a b s t r a c t Objective: Diabetic nephropathy is a common cause of end stage renal disease. Notwithstanding, wide interindividual variations in the speed of progression of diabetic nephropathy are frequent. We have used the score of the HUGE formula to predict progression of kidney disease in a group of diabetic nephropathy patients. Design and methods: The sample consisted of 84 type 2 diabetic patients. At treatment entry, the mean age was 62.1 ± 12.5 years and 59.5% were male. Blood pressure was measured at office at each visit. Serum creatinine, urea, hematocrit and 24 h proteinuria were analyzed every 6 months. HUGE score was calculated from gender, urea and hematocrit. Results: Mean HUGE score was 0.99 ± 3.88. Using as cut off point 1.5, those patients who had a score equal or higher (n = 31) showed a bigger increase in serum creatinine after one year (41.8 ± 62.1%) than those subjects with score b 1.5 (n = 53) (18.7 ± 38.6%, p = 0.041). 5 patients with low HUGE score reached end stage renal failure (9.4%) and 10 patients in the high HUGE score group (32.3, p = 0.008). When logistic regression analysis was performed only a HUGE score higher than 1.5 (p = 0.003) and proteinuria higher than 2 g/day (p = 0.041) were independently associated to CRF progression (creatinine increment > 25%). Conclusions: In diabetic nephropathy patients the HUGE equation may be useful to detect the subjects prone to progressive renal failure. Wider samples will be needed to confirm this finding and, most important, its applicability to other kinds of nephropathy. © 2011 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
1. Introduction Diabetic nephropathy occurs in 20–40% of patients with diabetes and is the single leading cause of end-stage renal disease (ESRD) in most developed countries. Persistent albuminuria in the range of 30–299 mg/24 h (microalbuminuria) has been shown to be the earliest stage of diabetic nephropathy in type 1 diabetes and a marker for development of nephropathy in type 2 diabetes [1]. Patients with microalbuminuria who progress to macroalbuminuria (≥300 mg/ 24 h) are likely to progress to ESRD over a period of years [2,3]. Notwithstanding, wide inter-individual variations in the speed of progression of diabetic nephropathy are frequent. In fact, estimated glomerular filtration rate (eGFR) is not sufficient for clinical decision making [4,5]. The HUGE formula with data obtained from a general population, offers a straightforward readily available and inexpensive method based on hematocrit plasma serum urea levels and gender [6]. It is ⁎ Corresponding author at: Servicio de Nefrologia, Hospital Infanta Cristina, Carretera de Portugal s/n. 06070, Badajoz, Spain. Tel./fax: + 34 924218117. E-mail address: [email protected] (N.R. Robles).
more accurate than MDRD formulae to differentiate chronic renal failure (CRF) from eGFR b 60 ml/min/1.73 m 2. It has tested in databases with a total of 125.373 subjects. It is particularly useful in persons aged over 70 years of age and overcoming the disadvantages derived from the use of serum creatinine to calculate eGFR, since it reduces the diagnosis of CKD by a 10.46% in elderly people. In Spain, where it has been estimated a CRF prevalence using MDRD equation about 6.8% in general population (2.992.000 subjects) [7], the HUGE formula would cut this figure by more than 300.000 persons. We have used the score of the HUGE formula, which can be easily implemented in clinical practice, to predict progression of chronic kidney disease (CKD) in a group of diabetic nephropathy patients. 2. Material and methods The sample consisted of 84 type 2 diabetic patients who were treated with ACE inhibitors or ARB due to diabetic nephropathy between January of 2004 and December of 2008. Data were evaluated retrospectively. At treatment entry, the mean age was 62.1 ± 12.5 years and 59.5% were male. Criteria for diagnosis of diabetic nephropathy were the presence of overt proteinuria (albuminuria > 300 mg/day)
0953-6205/$ – see front matter © 2011 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.ejim.2011.10.014
N.R. Robles et al. / European Journal of Internal Medicine 23 (2012) 283–286
associated to diabetic retinopathy lesions in fundoscopy. Whenever diabetic retinopathy was not present at renal biopsy was performed to confirm de diagnosis of diabetic nephropathy (3 patients needed this invasive method). Patients with isolated microalbuminuria (albuminuria b 300 mg/24 h) were not recruited. Follow up time was 35.8 ± 12.5 months (median). Blood pressure was measured at office at each visit. Three measurements, taken at 3-min intervals in the sitting position, were averaged and used as the clinical BP reference value. To evaluate the degree of blood pressure control all measurements during the follow up were averaged. Heart rate was measured from the radial pulse during 30 s. Serum creatinine, urea, hematocrit and 24 h proteinuria were analyzed every 6 months. eGFR was calculated using the MDRD-4 formula. The mathematical expression of HUGE formulation is: L ¼ 2:505458–ð0:264418 HematocritÞ þ ð0:118100 UreaÞ ½þ1:383960ifmale:
Survival Functions 1,0
HUGE score < 1.5
0,8
Cum Survival
284
HUGE score > 1.5
0,6
0,4
0,2
0,0 0,00
10,00
20,00
30,00
40,00
50,00
FOLLOW UP (months) If L is lower than “0”, it means that the individual does not have CRF. If L is higher than “0”, it means that the individual have CRF. Absolute score of HUGE formulation was used as predictor of progressive CRF. For comparison we have used 1.5 as cutoff point since it was the third quartile of HUGE score value for patients whose renal function decreased more than 25%. Results are expressed as mean ± 1 standard deviation. Continuous values have been compared through paired Student “t” test. The chi square test was used for discrete data. All statistical tests were twosided. Binary logistic regression analysis was used to find the clinical and biochemical characteristics associated to CRF progression (defined as an increase ≥25% of serum creatinine). p values lower than 0.05 were considered as significant. Analysis was developed with the statistical package PASW 17.0. 3. Results Mean HUGE score was 0.99 ± 3.88. Using as cutoff point 1.5, those patients who had a score equal or higher (n = 31) showed a bigger increase in serum creatinine after one year (41.8 ± 62.1%) than those subjects with score b 1.5 (n = 53) (18.7 ± 38.6%, p = 0.041, Student t test). The changes in serum creatinine have been plotted in Fig. 1. In the same way, eGFR was reduced by an 11.2 ± 23.4% in the group with score b 1.5 vs. 24.4 ± 21.5% in the other group (p = 0.012, Student t test). Renal survival before starting renal replacement therapy was calculated through the Kaplan–Meier method (Fig. 2) comparing patients with HUGE score b 1.5 with those with score ≥ 1.5. Only 5 patients with lower HUGE score reached ESRD (9.4%, 95%IC
3 2,5
Fig. 2. Kaplan–Meier analysis of renal survival before ESRD. Differences are significant (p = 0.008, Square chi test).
4.1–20.3), comparatively 10 patients in the high HUGE score group reached the same CKD stage (32.3%, 95%IC 18.6–49.9, p = 0.008, chi square test). Conversely, when patients were compared by a serum creatinine increase higher than 25% at one year, the HUGE score was lower in those with a lesser progression (n= 58, mean score 0.25± 3.65) than those which serum creatinine increased more than 25% (n= 26; mean score 2.70 ± 3.94, p = 0.007, Student t test). The evolution of changes in HUGE score has been plotted in Fig. 2. Contrariwise, eGFR was not different between both groups (serum creatinine increment≥ 25%, 52.3± 24.7 ml/min; serum creatinine incrementb 25%, 60.2 ± 25.2 ml/ min, p = 0.182, Student t test). Logistic regression analysis results are showed in Table 1. Systolic blood pressure ≥ 140 mm Hg, eGFR b 60 ml/min, proteinuria ≥ 2 g/day, gender, age ≥ 65 years and HUGE score were introduced as causative variables. Only a HUGE score higher than 1.5 (p= 0.003) and proteinuria higher than 2 g/day (p= 0.041) were independently associated to CRF progression (creatinine increment> 25%).
4. Discussion HUGE formula was developed and validated to improve the diagnosis of CRF in general population. This model use clinical and laboratory data that are obtained routinely in patients with CKD (hematocrit, serum urea and gender) and it could be easily integrated into a laboratory information system or a clinic health record. Our data suggest that this formulation could also be used for prognostic purposes in diabetic nephropathy patients.
2 1,5
Table 1 Logistic regression analysis.
1 0,5 0
0
12
24 Months HUGE < 1.5
36
48
HUGE > 1.5
Fig. 1. Changes in serum creatinine comparing subjects with HUGE score b 1.5 vs. those with score ≥ 1.5. Differences are significant (p = 0.041, Student t test).
HUGE score Age ≥ 65 years Gender SBP ≥ 140 mm Hg eGFR b 60 Proteinuria ≥ 2 g/24 h CI: Confidence interval.
B
CI low limit
CI upper limit
p
2.718 − 0.848 0.269 − 1.133 1.023 1.431
1.809 − 1.563 − 0.479 − 1.818 0.122 0.732
3.627 − 0.133 1.017 − 0.448 1.924 2.130
0.003 0.236 0.719 0.098 0.256 0.041
N.R. Robles et al. / European Journal of Internal Medicine 23 (2012) 283–286
Although diabetic nephropathy has a bad prognosis regarding renal survival [8], there can be considerable individual heterogeneity in the risk for progression to kidney failure. Risk prediction for CRF progression has gained increasing attention over the last years, with emerging literature suggesting improved patient outcomes with individualized risk prediction and with advances in information technology that allow for easy implementation of risk prediction models as components of electronic clinical records [9–11]. The availability of these risk prediction tools and their inclusion in clinical practice guidelines may get better adherence to treatment guidelines and have encouraged individual decision making [12]. Despite these benefits, the lack of easily applicable and externally validated models has delayed the widespread integration of risk prediction in all fields of medicine [13,14]. In this regards, HUGE formulation might offer a simple approach to identify the diabetic patients with worse prognosis in order to implement straighter treatment objectives and a closer follow up without any increased cost since this method does not need to add unusual clinical or biochemical measurements. Recently Tangri et al. [15] have developed and validated two predictive models for progression of CKD to kidney failure. The most accurate model included age, sex, estimated GFR, albuminuria, serum calcium, serum phosphate, serum bicarbonate, and serum albumin. This model was more accurate than a simple model that included age, sex, estimated GFR, and albuminuria. These models have been validated in a wide population and it must be recognized that our sample is rather smaller. Nevertheless, high statistical signification in a small sample reinforces the strong of results. Furthermore, HUGE equation is much simpler to calculate than the first Tangri's formulation, which needs more analytical parameters and, some of them, cumbersome to be measured (i.e. serum bicarbonate). The second Tangri's formula is easier to implement but still needs albuminuria measurement, an expensive issue. In this regard, HUGE formulation may have as advantage a reduced cost of implementation. It is well known that albuminuria provides additional prognostic information for progression to kidney failure [16]. Some studies have examined the use of estimated GFR and albuminuria in prediction models, with additional clinical and laboratory data, but these models are specific to a particular and different type of kidney disease [17–19]. Proteinuria was an independent baseline risk predictor for ESRD in this study. This predictive power has been found in other studies of patients with diabetic nephropathy [20–22]. In RENAAL, there was a nearly linear relationship between progression to ESRD and albuminuria at baseline and on treatment. Nevertheless, we found that the progression of CKD has a stronger statistical association with the HUGE score than with the severity of proteinuria. The anemia of chronic renal failure is not normally observed until the glomerular filtration rate drops to b40 ml/min and, as the renal function deteriorates, the anemia becomes more marked. [23,24]. The mechanisms that may contribute to this anemia include shortened red cell survival, decreased erythropoietin production, blood loss because of defective platelet function, and impaired erythropoiesis secondary to inhibitors or toxic metabolites [25]. The major explanation, however, is a relative erythropoietin deficiency resulting from an inability of the renal fibroblasts to produce erythropoietin to maintain the red cell mass in response to tissue hypoxia. Anemia has been recognized as a frequent complication of diabetic nephropathy, appearing earlier than in nondiabetic renal disease and amplifying the risks of cardiovascular and microvascular complications [26]. It is likely that the introduction of hemoglobin level in the equation have help to improve its prognostic efficacy, but it could made it less effective in non diabetic patients. This issue remains to be established in further studies. The HUGE equation may be useful to detect the subjects more prone to progressive renal failure, at least in diabetic nephropathy patients. Wider samples will be needed to confirm this finding and, most important, its applicability to other kinds of nephropathy.
285
Learning points • Diabetic nephropathy is the most common cause of end stage renal failure all over the world. • HUGE formulation was developed and validated to diagnosis renal failure without using serum creatinine and/or glomerular filtration rate. • HUGE score seems to be useful to detect those diabetic nephropathy patients prone to progressive renal failure.
Conflict of interest There is not any conflict of interest to be declared by anyone of the authors. References [1] Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, Jensen G, Clausen P, Scharling H, et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation 2004;110:32–5. [2] Gall MA, Hougaard P, Borch-Johnsen K, Parving HH. Risk factors for development of incipient and overt diabetic nephropathy in patients with non-insulin dependent diabetes mellitus: prospective, observational study. BMJ 1997;14:783–8. [3] Ravid M, Lang R, Rachmani R, Lishner M. Long-term renoprotective effect of angiotensin-converting enzyme inhibition in non-insulin-dependent diabetes mellitus: a 7-year follow-up study. Arch Intern Med 1996;156:286–9. [4] Hemmelgarn BR, Zhang J, Manns BJ, James MT, Quinn RR, Ravani P, et al. Nephrology visits and health care resource use before and after reporting estimated glomerular filtration rate. JAMA 2010;303:1151–8. [5] Jain AK, McLeod I, Huo C, Cuerden MS, Akbari A, Tonelli M, et al. When laboratories report estimated glomerular filtration rates in addition to serum creatinines, nephrology consults increase. Kidney Int 2009;76:318–23. [6] Alvarez-Gregori JA, Robles NR, Mena C, Ardanuy R, Macias-Nuñez JF. The value of a formula including haematocrit, blood urea and gender (HUGE) as a screening test for chronic renal insufficiency. J Nutr Health Aging 2011;15:480–4. [7] Otero A, de Francisco A, Gayoso P, García F, EPIRCE Study Group. Prevalence of chronic renal disease in Spain: results of the EPIRCE study. Nefrologia 2010;30: 78–86. [8] Robles N, Fernández-Carbonero E, Sánchez Casado E, Cuberto J. Growing incidence of diabetic nephropathy in the region of Badajoz during the period 1991–2006. Nefrologia 2009;29:244–8. [9] D'Agostino Sr RB, Grundy S, Sullivan LM, Wilson P, CHD Risk Prediction Group. I. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001;286:180–7. [10] Leslie WD, Morin S, Lix LM. A before-and-after study of fracture risk reporting and osteoporosis treatment initiation. Ann Intern Med 2010;153:580–6. [11] Pozen MW, D'Agostino RB, Selker HP, Sytkowski PA, Hood WB Jr. A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. N Engl J Med 1984;310:1273–8. [12] Selker HP, Beshansky JR, Griffith JL, Aufderheide TP, Ballin DS, Bernard SA, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACITIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. Ann Intern Med 1998;129:845–55. [13] Müller-Riemenschneider F, Holmberg C, Rieckmann N, Kliems H, Rufer V, MüllerNordhorn J, et al. Barriers to routine risk-score use for healthy primary care patients. Arch Intern Med 2010;170:719–24. [14] van Steenkiste B, van derWeijden T, Stoffers HE, Grol R. Barriers to implementing cardiovascular risk tables in routine general practice. Scand J Prim Health Care 2004;22:32–7. [15] Tangri N, Stevens LA, Griffith J, Tighiourarst H, Djurdjev O, Naymark S, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011;305:1553–9. [16] Chronic Kidney Disease Prognosis Consortium, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts. Lancet 2010;375:2073–81. [17] Geddes CC, Fox JG, Allison ME, Boulton-Jones JM, Simpson K. An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol Dial Transplant 1998;13:67–71. [18] Johnson ES, Thorp ML, Platt RW, Smith DH. Predicting the risk of dialysis and transplant among patients with CKD. Am J Kidney Dis 2008;52:653–60. [19] Mackinnon B, Fraser EP, Cattran DC, Fox JG, Geddes CC. Validation of the Toronto formula to predict progression in IgA nephropathy. Nephron Clin Pract 2008;109: c148–53. [20] Keane WF, Zhang Z, Lyle PA, Cooper ME, de Zeeuw D, Grunfeld JP, et al. Risk scores for predicting outcomes in patients with type 2 diabetes and nephropathy: the RENAAL study. Clin J Am Soc Nephrol 2006;1:761–7.
286
N.R. Robles et al. / European Journal of Internal Medicine 23 (2012) 283–286
[21] de Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, et al. Proteinuria, a target for renoprotection in patients with type 2 diabetic nephropathy: lessons from RENAAL. Kidney Int 2004;65:2309–20. [22] Lewis EJ, Hunsicker LG, Clarke WR, Berl T, Pohl MA, Lewis JB, et al. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med 2001;345:851–60. [23] Chandra M, Clemons GK, McVicar MI. Relation of serum erythropoietin levels to renal excretory function: evidence for lowered set point for erythropoietin production in chronic renal failure. J Pediatr 1988;113:1015–21.
[24] Ratke HW, Claussner A, Erbes PM, Scheuermann EH, Scheppe W, Koch KM. Serum erythropoietin concentration in chronic renal failure: relationship to degree of anemia and excretory renal function. Blood 1979;54:877–84. [25] Eschbach JW, Adamson JW. Modern aspects of the pathophysiology of renal anemia (review). Contrib Nephrol 1988;66:63–70. [26] Ritz E. Anemia and diabetic nephropathy. Curr Diab Rep 2006;6:469–72.