The H.U.G.E. formula (hematocrit, urea, sex) for screening chronic kidney disease (CKD) in an age-stratified general population

Abstract

Objectives: Aim

To evaluate the screening power of the HUGE formula for the detection of chronic kidney disease (CKD) in a Spanish population sample obtained from the HERMEX study, a survey of cardiovascular risk factors carried out in the region of Extremadura, stratified by age.

Design and Methods

This was an observational, cross-sectional, population-based study. The final sample included 2,813 subjects selected from Health Care System records. Anthropometric data and cardiovascular risk factors were recorded. Hematocrit, urea, creatinine and microalbuminuria were analyzed, after which the HUGE formula was applied. Renal function, assessed as eGFR based on serum creatinine, was estimated following the MDRD-4 formula.

Results

Using the HUGE formula, the estimated prevalence of CKD was 2.2% (men 2.2%, women 2.1%). The prevalence of CKD increased with age (5.0% in persons aged 60–70 years and 9.6% in individuals over 70 years of age, p < 0.001) whereas with the MDRD formula the prevalence values were 9.8% and 15.5% respectively. The HUGE formula was seen to be highly specific (0.99). CKD was more common in persons >70 years, obese subjects, hypertensive patients, dyslipidemic subjects and those with microalbuminuria. Multivariate analysis revealed an independent negative association of CKD as the dependent variable with SBP, serum triglyceride levels and microalbuminuria.

Conclusions

The HUGE formula allows the prediction of CKD in the general population to be honed without relying on serum creatinine levels. This method was found to have a higher specificity than the MDRD-4 formula. Moreover, it could reduce the excessively extensive diagnostic suspicion of CKD in women.

Introduction

Chronic kidney disease (CKD), an emerging major global public health problem that affects 10% to 16% of the adult population in Asia, Australia, Europe and North America, is associated with an increased risk of mortality and morbidity across both the general and high-risk populations (1, 2). Laboratory testing using serum creatinine measurements has been used to identify patients in the earlier stages of CKD, but current clinical guidelines recommend estimation of the glomerular filtration rate for the diagnosis and staging of CKD (3).

The HUGE formula, based on values related to health instead of on the critical value of 60 ml/min as the threshold for CKD, with data obtained from a general population, offers a straightforward, readily available and inexpensive method based on haematocrit, plasma serum urea levels and gender (4). It is more accurate than the MDRD formulae for differentiating chronic renal failure (CRF) from eGFR < 60 ml/min/1.73 m². To date, the HUGE formula has been tested in 125,373 subjects. It is particularly useful in persons aged over 70 years of age, overcoming the disadvantages derived from the use of serum creatinine for eGFR calculation (4), since HUGE reduces the number of patients classified as CKD using the

MDRD equation by 13,114 that is 10.46% of the persons aged over 70 years studied, as previously published by one of us (4). In Spain, the prevalence of CRF according to the MDRD equation is 6.8% in the general population (2,992,000 subjects) (5); the HUGE formula would reduce this figure by more than 300,000 patients. If it is accepted that the threshold between the absence of renal impairment and CKD is set at an eGFR < 60 ml/min/1.73 m² (6) and that 17% of the population over 60 have an eGFR of less than 60 ml>/min/1.73, estimation of the number of people affected by CKD in the UK points to approximately 2,600,000 in this age bracket, of whom roughly 176,800 will be free of renal insufficiency.

HERMEX was an observational, longitudinal, population-based study which attempted to evaluate the relative weight of classic and non-classic cardiovascular risk factors in a population of Extremadura (Spain) in order to determine the specific characteristics of this area, which could explain the high cardiovascular morbid-mortality as compared with global data from Spain. Among the parameters studied as non-classic cardiovascular risk factors was CKD, as previously published (7). The present report using the HERMEX database, revealed the differences between MDRD-4 and HUGE formulas in a sample of more than 3,000 individuals.

Design and methods

Subjects

A sample of 3,402 subjects aged between 25 and 79 years living in the area of Don Benito-Villanueva de la Serena (Badajoz, Spain) were selected by random number generation from the database of the Health Care System. Non-residents, institutionalized and deceased persons, disabled subjects, pregnant women, and people unable to give written informed consent were excluded. A total of 2,833 persons were recruited and 2,831 were included in the survey (participation rate: 82.7%) with a mean age of 51.2+14.7 years, median 50 (RI 24). Of them, 53.5% were female. A detailed description of the randomization methods used and the sample characteristics has been published elsewhere (6).

Biochemistry

Serum creatinine, urea, total cholesterol, high density lipoproteins (HDL), low density lipoproteins (LDL), glycated haemoglobin and glucose were analyzed after overnight fasting. The results are summarized in table 3. Urinary Albumin Excretion (UAE) was measured in the first morning urine sample. Blood pressure was measured with an electronic device (Omron 705 ™) according to standard guidelines (3 consecutive BP measurements were taken by the doctor caring for the patients with a mercury sphygmomanometer, with a 2-min interval, recording the averaged value of the 2nd and 3rd measurements). A BP value > 140/90 mmHg was considered to indicate hypertension. UAE was analyzed with an immunoturbidimetric assay (Roche Diagnostics, Mannheim, Germany).

Table 3.

Multivariant analyses

	B	LOWER LIMIT IC95%	HIGHER LIMIT IC95%	P
SBP	−0.022	−0.031	−0.013	0.008
AGE	0.090	0.077	0.103	<0.001
TRIGLYCERIDES	0.003	0.002	0.004	0.013
MICROALBUMINURIA	0.001	0.0001	0.002	0.008

Estimation of GFR

The GFR was estimated from serum creatinine levels measured with the standardized Jaffé method (IDMS), using the abbreviated CKD-EPI formula (adjusted for gender) (8). Only Caucasian patients were included in the study. Patients were classified according to K/DOQI stages of chronic renal disease following the results of the MDRD equation (9). Then, the individualized risk of renal disease progression was calculated using the KDIGO table (10). For statistical comparisons, CKD was defined as a GFR < 60 ml/min as estimated with the CKD-EPI formula.

The HUGE formula score was also calculated for all subjects, as previously published (4).

Statistics

Results are expressed as means + 1 standard deviation. All statistical tests were two-sided. P values lower than 0.05 were considered significant. For comparisons between groups, Student’s “t” test was used for continuous variables and the Chi-square test for categorical variables. Using binary logistic multivariate regression analysis, the association between microalbuminuria and those variables showing an association in the univariate analysis was studied. A two-sided P value > 0.05 was considered significant. All statistical tests were performed with the PASW 17.0 statistical package. Age standardization rates were calculated using the WHO standard (11).

Results

Using the HUGE formula 2.2% of subjects had CRF (95% CI: 0.015 - 0.032). There were no differences between men (2.2%; 95% CI: 0.015 - 0.030) and women (2.1%; 95% CI: 0.015 - 0.030). Only 1.1% of sample had a HUGE score higher than 1.5 (risk of renal disease progression). There was an increased prevalence in older people: 5.0% (IC95%, 3.3-7.6%) between 61 and 70 years, and 9.6% (IC95%, 6.4-14.2%) for those older than 70 years (p <0.001, Chi-square test). Despite this, although the prevalence of CKD increases with age the frequency detected with the HUGE formula is somewhat lower than that found with the eGFR MDRD formula (see figure 1) (p < 0.001, Chi-square test).

Figure 1.

Age distribution of CKD prevalence is showed. The frequency of CRF diagnosed by the HUGE formula increases with age (as using eGFR) but the final prevalence is much less with the former

Figure 2 shows the ROC curve for the HUGE formula vs. GFR < 60 ml/min. The AUC was 0.636.

Figure 2.

ROC curve for the HUGE formula vs. an eGFR < 60 ml/min is showed (area 0.789, 95%CI 0.740-0.838). The area represents the probability that a patient with a eGFR < 60 ml/min will have a HUGE score > 0 (CRF)

CRF was more common in obese subjects (6.3% vs. 2.2%, p < 0.001, Chi-square test), hypertensive patients (7.8% vs. 0.8%, p < 0.001, Chi-square test), dyslipidemic subjects (5.9% vs. 2.5%, p <0.001, Chi-square test) and diabetic individuals (8.8% vs. 2.8%, p <0.001, Chi-square test).

High blood pressure, dyslipidemia, obesity and microalbuminuria were more prevalent in patients classified as CKD (HUGE > 0), (see table 1).The CKD patients had higher SBP and PP values, as well as increased serum triglyceride levels and urinary albumin excretion (see table 2).

Table 1.

Clinical and biochemical diferences

	HUGE < 0	MDRD > 60	HUGE > 0	MDRD < 60
Glycemia	104.5 (28.8)	103.9 (25.6)	116.8 (37.0)*	114.8 (36.5)
Creatinine	0.82 (0.17)	0.81 (0.16)	1.32 (0.70)*	1.31 (0.51)
Total Cholesterol	207.6 (38.1)	207.6 (38.2)	203.2 (43.6)	206.7 (36.5)
HDL Cholesterol	56.5 (14.5)	56.5 (14.5)	53.8 (13.4)	54.7 (14.8)
LDL Cholesterol	120.9 (31.7)	120.8 (31.7)	116.5 (36.9)	122.5 (33.3)
Triglycerides	112.4 (78.3)	112.0 (78.5)	137.6 (72.2)*	135.7 (70.6)
HbAlc (%)	5.2 (0.8)	5.2 (0.8)	5.7 (1.1)*	5.6 (1.0)
Microalbuminuria	11.5 (82.0)	11.1 (81.9)	76.4 (275.9)**	55.7 (210.5)
SBP (mmHg)	127.0 (22.1)	126.7 (22.0)	134.3 (23.9)*	138.1 (24.4)
DBP (mmHg)	76.5 (10.9)	76.4 (10.8)	74.6 (9.4)	78.6 (11.8)
PP (mmHg)	50.5 (17.1)	50.3 (17.1)	59.7 (22.6)*	59.5 (20.9)
BMI (kg/m2)	28.5 (5.3)	28.5 (5.3)	31.0 (6.0)*	31.6 (6.1)
Waist perimeter (cm)	97.4 (13.6)	97.3 (13.5)	105.3 (14.4)*	104.7 (15.2)

Values expressed as mean (SD).

^*p < 0.05 (Student t test).

^**p < 0.01 (Mann-Whitney test). Biochemical parameters expressed as mg/dl but HbAlc (as %).

Table 2.

Prevalence of cardiovascular risk factors

	HUGE < 0	HUGE > 0
Age > 50 ys.	49.9	82.0*
BMI ≥ 30	34.6	52.5*
Hypertension	38.8	80.3*
Diabetes	11.7	36.1*
Dyslipemia	38.3	59.0*
Smoking	31.9	13.1*
BP > 140/90 mmHg	28.7	36.1
PP ≥ 50 mmHg	44.1	51.7*
Cardiovascular disease	0.1	19.7*

All values expressed as percentage.

^*p < 0,05, X2 test.

The multivariate analysis revealed an independent association of CKD as the dependent variable with age, SBP, serum triglyceride levels and microalbuminuria. All values are shown in table 3.

In comparison with the GFR estimated with the MDRD-4 equation, the HUGE formula revealed higher specificity (0.99, CI95% 0.98-0.99) with low sensitivity (0.28, CI95% 0.20-0.38). The positive predictive value was 0.52 (CI95% 0.40-0.65) and the negative predictive value was 0.97 (CI95% 0.97-0.98).

Discussion

The main findings of the present work are as follows: 1) the HUGE formula reduces the number of patients classified as CKD by the critical screening value of eGFR < 60 ml/min, regardless of age. 2) Since HUGE shows higher specificity than other formulas analyzed, it may be useful for honing the diagnosis of CRF in the general population. 3) HUGE may prevent an excessively extensive diagnosis of CKD in females.

In brief, HUGE allowed people to be classified as CKD or as being free of renal impairment based on health-related values (diagnosed by an assessment physician, and according to clinical, biochemical, and image data) instead of on the critical value of 60 ml/min 1.73 m², regardless of age. The mathematical expression of the HUGE formula is as follows: L= 2.505458 - (0.264418 x Haematocrit) + (0.118100 x Urea [+ 1.383960 if male].

If L is lower than “0”, this means that the individual does not have CKD. If L is > 0, it means that the individual does have CKD (4).

Since early renal disease is usually asymptomatic, its detection tends to appear by chance in routine health checkups or during the follow-up of patients at risk of cardiovascular events: i.e. hypertensive or diabetic patients. Several authors have reported that prompt and thorough treatment of CKD, the treatment of comorbidities, education and multidisciplinary treatment are necessary to delay or (ideally) prevent the progression of the disease. Often, patients are not diagnosed early enough for the proper treatments that delay the progression of the disease to be implemented in order to reduce death and disability. Most CKD comorbidities develop during the early stages (12), but increases in severity during later stages of CKD and earlier treatment of these complications could potentially delay or prevent the progression of CKD (13, 14). The most common measure used to assess overall kidney function is the blood creatinine concentration. Interpretation of this index is complicated, since it is inversely proportional to the GFR and varies among individuals, depending on age, gender and muscle mass. Furthermore, the serum creatinine concentration is affected by factors other than GFR, such as tubular handling and the generation and extrarenal excretion of creatinine. To avoid these pitfalls, the K/DOQI guidelines recommend that the GFR should be estimated by using prediction equations based on serum creatinine determinations (4). The abbreviated Modification of Diet in Renal Disease (MDRD) equation was advocated because it correlated well with the GFR as measured by iothalamate clearance (15). It also performed as well as a more complicated MDRD equation that required serum urea nitrogen and albumin determinations (16).

In the United Kingdom, the ratio of prevalent CKD treated end-stage renal disease (ESRD) is 1:100 and, for incident it is 1:1,000. It is worth noting that the prevalence of CKD using the current definitions is dissimilar in diverse populations but the incidence of treated ESRD is not. This implies that there are populations in which the risk of disease progression is much higher. The USA is a good example: the incidence of treated ESRD is 2-2.5 times that of most European countries, which report a similar prevalence of CKD. The problem of CKD as defined is therefore not primarily about ESRD. There has to be a better way of identifying subjects at risk of needing treatment for ESRD: trawling by GFR is using too fine a net (17). In a previous report addressing a sample of patients receiving attention at medical facilities, HUGE achieved better specificity than the GFR in the diagnosis of CKD (4). In the same report, the authors stated that the use of the HUGE formula to screen for CKD could cut the presumption of diagnosis of CKD by 10.5%. Using this tool in the general population, as done here, the figure would be nearly half, since the prevalence of GFR < 60 ml/min described in the HERMEX study was 3.6% using the CKD-EPI equation and 4.0% using the MDRD equation.

Thus, a very high number of elderly persons are diagnosed with CKD on the basis only of an estimated -not measured-GFR, using a formula that includes age as one of the terms but without making any check of the individual’s true renal function or calculating the GFR According to Age (GFRAA). Calculation is very easy and useful in physiological conditions since the GFR declines at a rate of 0.8 ml/min from 25-30 years of age up to the age of 70; from this age onwards, the GFRAA decline rate is 1.05 ml/min/1.73 m² /year. Since the maximum GFR (MGFR) at the age of 25 may range from 90-130 ml /min/1.73 m², for the purposes of calculation a value of 110 ml/min/1.73 m² can be accepted: FRAA (25 to 70 years) = 110 + 20 - 0. 8 x age in years, and GFRAA > 70 years = 110 + 37.5 - 1.05 x age in years

In fact the risk of disease progression in elderly patients with a reduced GFR is very low. The HUGE formula helps to discriminate true renal dysfunction in this age group even though the individuals do have different clinical or biochemical characteristics, as shown in table 1. Furthermore, according to Grimes and Poggio, it seems logical that any approach to CRF should include a clear cut between screenings and diagnose tests. The accuracy of a screening test should be evaluated as the results of sensitivity, specificity and the predictive values of positive and negative results, otherwise, eGFR as a screening method may be a “double-edged sword”, sometimes wielded clumsily by the well-intended. According to this, findings such as proteinuria, haematuria, a reduced eGFR or high serum creatinine should in principle be considered valid elements for a screening test. Supporting this possibility is the observation that sensitivity in diagnosing early CKD has been enhanced at the expense of specificity; this is why some healthy subjects are wrongly classified, based on eGFR testing, as having CKD. The number of individuals misclassified will become greater and greater as testing becomes more widespread (18).

When GFR is used to define CKD, a higher prevalence is always seen in women. This bias is due to the lower GFR in women than in males. Contrariwise, gender differences in studies of CKD progression have been reported, with higher rates of progression for men (19., 20., 21.). Thus, clinical tools that improve and refine diagnoses of CKD would be necessary to select patients truly at risk of progression. In this regard, the HUGE formula has shown a good capacity to predict the progression of renal disease in a group of diabetic nephropathy patients (22). Using the criteria described in that study, the risk of progression in the sample group was extremely low. Moreover, although the HUGE formula is corrected by gender, like the MDRD and CKD-EPI equations, no differences in the prevalence by gender of CKD were found. Thus, the HUGE formula could be useful to predict the future course of renal disease with risk of progression. A further advantage is that it avoids excessively numerous classifications of CKD in female populations.

Strengths and limitations

The main strengths of this cross-sectional study include the use of a large, representative, randomly selected group of the general Spanish population, although this might not be representative of the entirety of the Spanish population. Additionally, the study was only carried out with Caucasian subjects and hence the results cannot be extrapolated to other ethnicities. Extremadura has one of the highest cardiovascular mortality and morbidity rates in Spain (23) and hence the combination of high prevalence of cardiovascular disease and a low frequency of CKD using the HUGE formula becomes particularly useful, especially if it is taken into account that this formula is well correlated with a background of cardiovascular risk and indeed better correlated than with the results obtained with MDRD-estimated eGFR (24).

Conclusions

The HUGE formula reduces the number of patients classified as suffering from chronic kidney disease using the critical screening value of eGFR < 60 ml/min, regardless of age. This effect is more evident in the general population than in people attending medical facilities. The gender of the patient does not affect the rate of diagnosis. The HUGE formula may be a helpful tool to refine the diagnosis of chronic kidney disease in the general population.

Conflict of Interest

The authors state that there are no conflicts of interest to be declared.

Ethical standards

Reporting of the study conforms to STROBE statement along with references to STROBE statement and the broader EQUATOR guidelines.