Post on 13-May-2022
CREATININE CLEARANCE ESTIMATION
FROM SERUM CREATININE VALUES:
EVALUATION AND COMPARISON OF FIVE
PREDICTIVE FORMULAE IN NIGERIAN
SICKLE CELL DISEASE PATIENTS
BY
DR. J.C. ANEKE (MB.BS, PORTHARCOURT)
AF/008/08/005/600
DEPARTMENT OF HAEMATOLOGY AND
BLOOD TRANSFUSION, OBAFEMI AWOLOWO
UNIVERSITY TEACHING HOSPITAL COMPLEX,
ILE – IFE, OSUN STATE.
A DISSERTATION SUBMITTED TO THE
NATIONAL POSTGRADUATE MEDICAL COLLEGE
OF NIGERIA IN PARTIAL FULFILMENT FOR THE
PART II FINAL FELLOWSHIP EXAMINATION IN
HAEMATOLOGY AND BLOOD TRANSFUSION
NOVEMBER, 2010
ii
CERTIFICATION
We certify that this work Creatinine clearance estimation from Serum
creatinine values: evaluation and comparison of 5 prediction formulae in Nigerian
Sickle Cell Disease patients, was done under our supervision by Dr. John C. Aneke of
the Department of Haematology and Blood Transfusion, Obafemi Awolowo
University Teaching Hospitals Complex, Ile-Ife, Osun State.
…............................................
Dr. M.A. Durosinmi FMC Path, FWACP (Lab. Med)
Consultant Haematologist and Professor,
Department of Haematology, Obafemi Awolowo
University, Ile-Ife Osun State.
Co-supervisor ……………………………………..
Dr. A.O. Adegoke FMCPath
Consultant Chemical Pathologist and Lecturer,
Department of Chemical Pathology, Obafemi Awolowo
University, Ile-Ife, Osun State.
iii
DECLARATION
This hereby declared that this work is original unless otherwise acknowledged.
The work has neither been presented to any college for fellowship nor has it been
submitted elsewhere for publication.
____________________________
Dr. John C. Aneke MB, BS (PH)
iv
DEDICATION
This work is dedicated to the Almighty God, without whom I can do nothing
and to the well being and survival of individuals with sickle cell disorder.
v
ACKNOWLEDGEMENT
I want to whole-heartedly express my deep gratitude to my supervisor and my
teacher, Prof. M.A. Durosinmi for his unflinching support and interest in the course of
this work. I also thank my co-supervisor, Dr A.O. Adegoke for the unending
suggestions and guidance from the inception of this work to its completion.
I deeply appreciate Dr. (Mrs.) N.O. Akinola for her special interest in this
study, in my training and academic development. To Dr. A.A. Oyekunle, words will
fail me in thanking you for all the hours you spent with me to ensure that this work
comes out the way it did.
I am also indebted to Dr. A.A. Sanusi, Consultant Nephrologist, OAUTHC for
helping me negotiate some difficult terrains encountered during the course of this
work. Special thanks also goes to Drs. L Salawu (my HOD) and R.A.A. Bolarinwa
both consultant Haematologists for their advice and guidance not only during the
course of this work but throughout the period of my training.
To my colleagues in the Department (Drs Uche, Osho, Alabi, Oyelese,
Adelasoye, Ogbaro, Olufemi-Aworinde and Ajibade). I owe you all a world of thanks.
The immense support received from Mrs. F.O. Afolabi, Mrs. E. Fadoyin and Mrs.
Bakare, Matrons of the Haematology day ward; and the technical assistance of Mr.
Awe, a laboratory scientist in the Department of Haematology are well appreciated.
My thanks also go to Prince Ogundipe and Mrs. O.O Uchegbu both Deputy
Directors in Chemical Pathology and Haematology Departments respectively for their
scientific assistance.
vi
Finally to my pearl, my jewel of inestimable value, the very love of my life,
Mrs. Paulyn Aneke, you have been my motivator and a helpmate indeed. To Bridget
and Steve, thanks so much for your immense assistance. To Kamsi and Kelechi, you
helped me too in your own childish ways; I love and appreciate you all more than
words could ever say.
vii
TABLE OF CONTENTS
Page
Title Page i
Certification ii
Declaration iii
Dedication iv
Acknowledgement v
Table of Contents vii
Abbreviations ix
List of Tables xi
List of Figure xiii
Summary xiv
1.0 Introduction 1
2.0 Literature Review 3
2.1 Renal Abnormalities in Sickle Cell Disease 3
2.1.1 Glomerular Lesions 3
2.1.2 Renal Tubular Disease 4
2.1.3 Abnormalities in Renal Haemodynamics 5
2.2 Clinical Features 7
2.3 Investigations 8
2.3.1 Laboratory Studies 8
2.3.2 Imaging Studies 10
2.3.3 Procedures 11
2.3.4 Staging 11
viii
3.0 Research Objectives 12
3.1 General Objective 12
3.2 Specific Objective 12
4.0 Research Methodology 13
4.1 Calculation of Sample Size 13
4.2 Ethical Clearance 14
4.3 Patients and Method 14
4.3.1 Inclusion Criteria 14
4.3.2 Exclusion Criteria 14
4.3.3 Methods 14
4.3.4 Sample Collection 15
5.0 Data Analysis 17
6.0 Result 18
7.0 Discussion 24
8.0 Conclusion 56
9.0 Recommendations and Limitations 56
References 57
Appendix - I 66
Appendix – II 67
Appendix- III 68
ix
ABBREVIATIONS
ACE - Angiotensin Converting Enzyme
BMI - Body Mass Index
BSA - Body Surface Area
CC (r) - Coefficient of Correlation
CCF - Congestive Cardiac Failure
CD(r2) - Coefficient of Differentiation
CG - Cockcroft – Gault
CI - Confidence Interval
CKD - Chronic Kidney Disease
Cr cl - Creatinine Clearance
CRF - Chronic Renal Failure
CT - Computerized Tomography
dBP - Diastolic Blood Pressure
DMSA - Dimercaptosuccinic Acid
DTPA - Diethylene Triamine Pentaacetic Acid
EDTA - Ethylene Diamine tetraacetic Acid
ERBF - Effective Renal Blood Flow
ESRD - End Stage Renal Disease
FBC - Full Blood Count
FSGS - Focal Segmental Glomerulosclerosis
GFR - Glomerular Filteration Rate
HbSC - Haemoglobin SC
HbSS - Haemoglobin SS
HDW - Haematology Day Ward
x
HOPC - Haematology Outpatient Clinic
K/DOQI - Kidney Disease Outcome Quality Initiative
MDRD - Modification of Diet in Renal Disease
NaOH - Sodium Hydroxide
NO - Nitric Oxide
OAUTHC - Obafemi Awolowo University Teaching Hospitals Complex
PAH - Paraaminohippuric Acid
PCV - Packed cell volume
PgE2 - Prostaglandin E2
PgF2 - Prostaglandin F2
PMD - Paired Mean Difference
RTA - Renal Tubular Acidosis
sBP - Systolic Blood Pressure
SCD - Sickle Cell Disease
SPSS - Statistical Package for Social Sciences
UI - Ultrasound Imaging
WBC - White cell count
xi
LIST OF TABLE
Table Title Page
Table 1: Comparison of means of standard deviation of some
patient and control Parameters 40
Table 2A: Comparison of means of age by hemoglobin
type and gender in patients 41
Table 2B: Comparison of means of weight by hemoglobin
type and gender in patients 42
Table 3A: Comparison of means of serum creatinine by
hemoglobin type and gender in patients 43
Table 3B: Comparison of means of urinary creatinine by
hemoglobin type and gender in patients 44
Table 4A: Comparison of means of Systolic blood pressures by hemoglobin
type and gender in patients 45
Table 4B Comparison of means of diastolic blood pressures by hemoglobin
type and gender in patients 46
Table 5A. The means, SD of Haemogram and crises in all patients, SS, SC
and controls 47
Table 5B The means, SD of biochemical parameters and transfusion
history in all patients, SS, SC and controls. 48
Table 6A: Correlation between patient’s parameters and measured
creatinine clearance 49
Table 6B: Comparison of means of some measured parameters between
SS and SC 50
xii
Table 7: Correlation of measured parameter for SS with measured
creatinine clearance 51
Table 8: Regression parameters between measured and predicted
creatinine clearance in patients and controls. 52
Controls 53
Table 9: T – test for paired samples with mean difference at 95% CI 54
Table 10: Comparison of the means and standard deviation of calculated
Crcl in patients with proteinuria 55
xiii
LIST OF FIGURES
Figure Title Pages
Figure 1: Frequency distribution of patients according
to stages of nephropathy 29
Figure 2: Correlation graph: Measured Crcl vs Hull formula- patients 30
Figure 3: Correlation graph: Measured Crcl vs Cockcroft–Gault formula–
patients 31
Figure 4: Correlation graph : Measured Cr cl vs Mawer formula – patients 32
Figure 5. Correlation graph: Measured Cr cl vs Gates formula – patients 33
Figure 6. Correlation graph : Measured Cr cl vs MDRD formula – patients 34
Figure 7: Correlation graph : Measured Cr cl vs Hull formula – control 35
Figure 8. Correlation graph : Measured Cr cl vs Mawer formula – control 36
Figure 9. Correlation graph : Measured Cr cl vs Cockcroft–Gault formula–
control 37
Figure 10. Correlation graph: Measured Crcl vs Gates formula – control 38
Figure 11. Correlation graph: Measured Cr cl vs MDRD formula – control 39
xiv
SUMMARY
INTRODUCTION
This study evaluated 5 predictive formulae for calculating creatinine clearance with a
view to determining which formula compares best with measured creatinine
clearance, obtained with a 24 hour urine collection in SCD patients and controls.
METHODS
Over a period of 7 months, 100 SCD patients (79 HbSS and 21 HBSC) and 50
controls participated in the study. A case record form was used to capture the clinical
details of patients, including the weight, age, and gender, frequency of crises,
transfusion and drug history. Sample collected included 7.0ml of venous blood for
serum creatinine, full blood count and reticulocyte count estimation. A 24-hr urine
and fresh urine samples were also collected for measurement of creatinine clearance
and dip stick proteinuria, respectively. Data collected were analysed using descriptive
and inferential statistics; a p-value < 0.05 was significant.
RESULTS
The study population had a mean age of 26.2±7.4 years and the controls 25.7±4.8
years. A mean PCV of 23.9±4.3%, 31.8±4.2% and 40.9 ± 4.7% were obtained for the
SS, SC and controls, respectively. Their mean WBC, platelet counts and reticulocyte
index were within normal reference ranges. Proteinuria was observed only in SS
patients with mean values of 0.52±1.20. The mean VOC reported, as number of
episodes in one year was 3.00±1.62 for SS and 1.67±1.24 for SC patients. Transfusion
episodes, reported as number of transfusions in 2 years, showed a mean value of
0.51± 0.71 for SS and 0.05±0.22 for SC. The mean values for the measured creatinine
xv
clearance in all patients, SS, SC, and controls were 66.80±26.37ml/minute,
66.28±27.52ml/minute, 68.76±21.95ml/minute and 97.04±17.47ml/minute,
respectively. No significant correlation was observed between haemogram,
proteinuria or transfusion episodes and measured creatinine clearance in the study
population(r values all ≤ 0.3). The Cockroft and Gault formula showed a superior
assessment of renal function in the patients as reflected by the higher
r(0.654),r2(0.428) values and lower predictive error(17.23). All the formulae
performed poorly in the controls.
CONCLUSION
The Cockcroft and Gault formula drawing from its satisfactory assessment of
renal function can substitute measured creatinine clearance as a means of monitoring
renal status in SCD patients.
1
CHAPTER ONE
1.0 INTRODUCTION
Sickle cell nephropathy is a term that encompasses the spectrum of
morphologic, laboratory and clinical changes associated with sickle cell disease.
These range from the passage of “increased urine volume of low specific gravity”
described by Herrick in 1910[1] to impaired urine acidification, haematuria,
proteinuria, renal failure syndromes[2] with distinct glomerular and tubular lesions and
renal cell carcinoma which was described recently as ‘another example of renal
disease’ associated with sickle cell disorder[3].
Cumulatively, these manifest as varying degrees of renal impairment, which
may be amenable to specific measures to delay the onset of End Stage Renal Disease
[ESRD] and mortality [4]. Estimation of the Glomerular Filteration Rate [GFR] using
exogenous or endogenous markers offers a good measure of renal function in health
and disease[5]. The use of serum creatinine for determination of GFR involves the
traditional 24 hour urine collection with it`s attendant problems, chiefly that of a 24
hour stretch urine collection.
Efforts to obviate this limitation of 24 hour urine collection heralded the
introduction of formulae which give a calculated creatinine clearance value from
parameters which are relatively easy and quick to assess[6,7,8,9,10]. How comparable
values calculated are to measured values have been variously studied in renal, cardiac
and diabetic patients but no similar study was sighted for SCD patients.
The magnitude of sickle cell nephropathy is high, a study carried out in Saudi
Arabia revealed that 41% of sickle cell disease patients had proteinuria, while 22.5%
had low creatinine clearance[11]. In another study, the median age of onset of
significant renal impairment was 23.1years while median age at the time of death was
2
27years, in spite of access to dialysis [12]. Creatinine clearance is perhaps the best
means to determine the severity of renal pathology and its progression and therefore
provides insight into the natural history of sickle cell nephropathy.
The need to find an alternative to a 24-hour urine collection for creatinine
clearance estimation in diagnosing and monitoring renal impairment in this subset of
our population was the motive for this study.
3
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 RENAL ABNORMALITIES IN SICKLE CELL DISEASE
2.1.1 GLOMERULAR LESIONS
Enlarged glomeruli have been noted both at autopsy and at biopsy in sickle
cell disease patients, they can sometimes be seen with naked eye[13]. In these patients,
glomerular size tend to increase with age, but by contrast, in normal individuals after
childhood, little relationship is seen between age and glomerular size[13].
On histological examination, these enlarged (markedly hypercellular)
glomeruli exhibit lobulation of the glomerular tuft. Reduplication of the basement
membrane and mesangial proliferation are also seen, and with increased frequency as
sickle cell disease patients age. Progressive glomerular fibrosis and obsolescence are
also seen with increasing age, accounting for the decline in GFR as individual ages
[14].
Also noted is effacement of foot processes on electron microscopy,
haemosiderosis and perihilar focal segmental glomerulosclerosis (FSGS)[15,16]. The
latter has been found to be amenable to therapy with angiotensin converting enzyme
(ACE) inhibitors, presumably through dilation of the efferent arterioles[17]. In more
advanced disease, other manifestations of glomerular injury may occur including
lesions resembling membranoproliferative glomerulonephropathy[18] and less
frequently, true immune complex nephropathy[19].
The exact pathogenesis of this glomerular abnormality still remains to be
defined. Possible explanations include; mesangial phagocytosis of sickled cells[18],
immune complex glomerulonephritis due to autoantigens released from ischaemic
4
tubules,[20] glomerular injury caused by hyperfiltration[21] and glomerular
hypertrophy[22].
The most common clinical manifestation of glomerular injury in sickle cell
disease is proteinuria which may progress to full blown nephrotic syndrome[19]. Up to
40% of HbSS patients with nephrotic syndrome eventually went on to develop
ESRD[I2].
2.1.2 RENAL TUBULAR DISEASE:
Abnormalities of Proximal Tubular Function:
Proximal tubular activity, both secretory and reabsorptive is supernormal in
sickle cell disease patients. Increase in secretory capacity is supported by findings of
increased tubular transport maximum of urate as well as increased creatinine
clearance[23] in them.
In the same vein, increase in reabsorptive capacity is supported by findings of
increase in proximal tubular absorption for phosphate, sodim and beta-2
microglobulin (β2-m)[24]. The increased phosphate absorption is responsible for the
higher levels of serum phosphate seen in them than in controls while the increased
sodium reabsorption is thought to be a secondary mechanism to correct for defects in
medullary function[15] which could cause a reduced serum sodium concentration in
them.
These proximal tubular defects appear to have no significant pathological
effect.[15] It is however yet to be determined whether they produce clinically important
changes in the pharmacokinetics of drugs in which renal tubular secretion is a major
pathway of elimination such as penicillin and cimetidine[25].
5
Abnormalities of Distal Tubular Function:
Sickling of erythrocytes within the medullary capillaries is promoted by the
hypoxic, acidotic and hyperosmolar environment of the inner medulla, which leads to
impairment in renal medullary blood flow, causes ischaemia, leading to microinfarcts
and papillary necrosis. Microradioangiographic sequelae of these include a
significantly reduced numbers of vasa recta, abnormal dilation or obliteration of the
remaining medullary capillaries with consequent loss in counter current mechanisms
of the inner medulla[26]. This is manifested clinically as inability to concentrate urine,
which worsens progressively with age and this defect, may become irreversible after
the age of 15years[26,27]. This defect is not corrected following vasopressin
administration, thereby ruling out central diabetes insipidus[28].
In addition, sickle cell patients also have impaired renal acidification and
potassium secretion. The former is manifested as an incomplete form of distal renal
tubular acidosis (RTA) which in the setting of a background renal insufficiency can
progress to hyperchloraemic metabolic acidosis[29]. Similarly, hyperkalaemia only
occurs in these patients in the setting of renal function impairment, stress as in volume
contraction during sickle cell crises[29] or following the administration of drugs such
as Angiotensin converting enzyme (ACE) inhibitors, β – blockers and potassium
sparing diuretics[25].
2.1.3 ABNORMALITIES OF RENAL HAEMODYNAMICS:
Infants and young adults with sickle cell disease have both increased effective
renal blood flow (ERBF) and glomerular filtration rate (GFR)[30] leading to
hyperfiltration. Persistent hyperfiltration predisposes these patients to the
development of glomerulosclerosis[30] and gradual decline in renal function.
6
Renal blood flow, glomerular filtration and solute handling are all regulated by
the equilibrium between renal vasoconstrictor (renin-angiotensin) and vasodilator
(prostaglandin) substances. Prostaglandin production in the kidney is increased in
sickle cell anaemia patients [23], which may explain the super normal GFR and ERBF
with increased proximal tubular activity. Renal prostaglandin production is known to
be promoted by various, often vasoconstrictor stimuli. It thus appears that ischaemic
damage to the inner medulla induces the synthesis of vasodilator prostaglandins [31].
A normal diluting capacity of the distal nephrons was abrogated following
indomethacin administration [28], this finding suggests that this function depends on
adequate prostaglandin synthesis.
Much interest has been focused over the past decade on a possible causal
relationship between increased nitric oxide (NO) synthesis and glomerular
hyperfiltration of sickle cell nephropathy. Bank et al[32] have demonstrated that
inducible NO synthase is increased in the glomeruli and distal nephron of transgenic
sickle cell mice but not in control mice. Chronic hypoxia could result in inducible NO
synthase activation[33]. It is thus postulated that chronic hypoxia of sickle cell
nephropathy may increase NO synthesis, leading to vasodilation which in turn may
contribute to renal hyperperfusion.
A casual role has recently been ascribed to free haemoglobin, a product of
intravascular haemolysis in the actiopathogenesis of sickle cell nephropathy. Free
haemoglobin scavenges and inactivates Nitric oxide (a potent vasodilator) thus
leading to vasoconstriction, reduced renal blood flow and end organ injury [34]. In
addition to causing vasoconstriction, free haemoglobin also causes platelet activation
and increased endothelin 1 expression [35] both mechanisms further exacerbate the
vasculopathy of sickle cell disease.
7
2.2 CLINICAL FEATURES:
Hyposthenuria is the first clinical evidence of defective medullary tonicity.
This can be reversed in younger children (less than 10years) by multiple transfusions,
but however, in patients older than 15years the process is often irreversible.
Hyposthenuria can produce a higher than usual obligatory urine output, thereby
increasing the risk of dehydration.
Proteinuria is commoner in HbSS than in other haemoglobinopathies[36]. It
occurred in up to 41% of patients with SCD11. Prowars et al showed that proteinuria,
nephrotic syndrome, microscopic haematuria and hypertension may actually be a
significant predictor of chronic renal failure[12]. It is believed that glomerular capillary
hypertension, thought to be present in sickle cell nephropathy causes proteinuria. This
concept is supported by the decrease in protein excretion that is seen with
administration of Angiotension-Converting Enzyme Inhibitors [36].
Acute renal failure may be triggered by concomitant infection or
rhabdomyolysis, renal vein thrombosis or intravascular haemolysis[38]. Chronic renal
failure with progression to end stage renal disease is clinically evidenced by
proteinuria and progressive worsening of renal function parameters. The underlying
histopathology is usually FSGS and glomerular hypertrophy.
Haematuria occurs in both sickle cell trait and disease. It is thought to be
related to sickling of erythrocytes in the vasa recta of inner medulla and renal papillae
which results in increased blood viscosity, microthrombi formation and ischaemic
necrosis with subsequent structural changes leading to haematuria[26,27]. It may
originate from either kidney, although a preponderance of left-sided renal bleeding
has been observed[27].
8
2.3 INVESTIGATIONS:
2.3.1 LABORATORY STUDIES:
a. Urinalysis and Microscopy:
Fresh urine samples are needed. Multi test detection strip test may
detect proteinuria, haematuria, reduced specific gravity and/or pyuria.
Urine microscopy of centrifuge-spun urine specimen may show red
cells, white cells and/or casts.
b. Serum Chemistry Profile: Serum urea and creatinine assessments are
important laboratory parameters. Elevation of serum levels of these parameters
are sufficient evidence of renal impairment. Creatinine is the anhydrous form
of the parent compound creatine phosphate. The latter serves as a high energy
source to tissues such as muscles. Creatinine is excreted into the circulation at
relatively constant rate (proportional to the muscle mass) and is removed from
the circulation almost entirely by glomerular filteration. Except in individuals
in whom meat constitutes a large proportion of the diet, its plasma levels are
relatively unaffected by diet (unlike urea) and reflect mainly endogenous
production and the GFR [39]. Small amounts may be secreted by the proximal
tubules, especially at markedly elevated levels thus creatinine clearance may
exceed inulin GFR[39]. Serum creatinine estimation is most frequently based
on the Jaffe’s method. Either serum or plasma can be used and samples remain
stable for up to 3months at –20o C [40].
c. A full blood count is an important investigation both in the initial evaluation
and the subsequent follow up of these patients. Anaemia is an important
finding and may indicate chronicity. Reticulocytopaenia reflects reduced
erythropoietin production with subsequent marrow hypoactivity.
9
d. Glomerular Filtration Rate (GFR): This gives a rough measure of the
number of functioning nephrons. GFR determination involves measurement of
renal clearance of ideal filtration markers such as Inulin 99mTc diethylene
triamine penta acetic acid (99mTc DTPA), Iothalamate, 51Cr ethylene diamine
tetra acetic acid 51Cr – EDTA[41]. These are radionuclide-based procedures.
Other methods include creatinine clearance, para aminohippuric acid (PAH)
and recently serum cystatin C. The use of the latter (a low molecular weight
protein of the cystatin superfamily of cysteine protease inhibitors) to estimate
the GFR is gaining wide acceptance [42,43] and is currently the preferred
endogenous parameter for GFR[44].
e. Creatinine Clearance Determination: This traditionally has been commonly
carried out using a 24hour urine sample collection. Clearance is calculated
from the urine volume, urinary creatinine and serum creatinine.
Over the years, however, formulae (predictive equations) have been proposed
for calculating creatinine clearance. The most commonly used ones include
Cockcroft–Gault (CG)[6], Edward–Whyte[45], Jellife–1[10], Marwer[8], Jellife–2[46],
Bjornsson[47], Hull[9], Gates[7], Salazer–Corcotan[48], Davis Chandler[49]. These
formulae make use of serum creatinine and avoid urine collection. Other variables
such as gender, age, weight and height are utilized in estimating creatinine clearance.
Recently, another formular was developed by the Modification of Diet in Renal
Disease (MDRD) study group[50]. This formular takes into account patient’s age,
gender, race and serum creatinine in estimating the creatinine clearance. This is called
4-variables MDRD. An extended (6-variables MDRD) version has serum urea and
serum albumin levels incorporated into the above. Both versions however tend to
underestimate the GFR in patient with GFRs over 60ml/min[51,52]. The Schwartz
10
formular was developed for children [53], it employs serum creatinine, child’s height
and a constant to estimate the GFR, and the value of the latter is dependent on age of
the child.
In comparing some of these predictive formulae in a cross section of normal
and sick individuals, the following observations were obtained. Ajayi noted a high
correlation between measured and predicted creatinine clearance (from the C–G
formular) in Nigerian patients with hypertension, congestive cardiac failure (CCF)
and chronic renal failure (CRF)[54].
Sanusi and team alluded to the adequacy of five predictive formulae for
determining creatinine clearance in Nigeria patients with CRF[55] but concluded that
CG is the most preferred.
In patients with ESRD, MDRD equations were found to be more accurate in
predicting GFR than the C–G formular[56]. Also working on patients with ESRD,
Kuzminsky et al found that the MDRD and C – G formulae correlated better with true
GFR in stages 3 and 4 of CKD[57].
Muhajan et al [58] found the MDRD–1 the most accurate predictive formulae in
healthy Indians. In the same vein, another study noted that renal function assessment
in diabetics was more accurate with the MDRD equation than the C–G equation [59].
2.3.2 IMAGING STUDIES
Imaging studies may help in confirming the diagnosis of renal disease and
may provide clues to the presence of other causes aside sickle cell disease.
Ultrasonography (USS) of the abdomen may show concurrent abnormalities
causing renal impairment such as renal tumour, polycystic kidney disease or an
11
obstructive uropathy. A computed tomography scan is superior to USS in this
light.
Radionuclide Studies: This may detect renal scarring, done usually with 99m-
technetium dimercaptosuccinic acid (DMSA).
Retrograde or anterograde pyelography may reveal an obstructive uropathy,
though their use has been largely supplanted by USS and CT scan.
Skeletal survey may be useful in evaluating for secondary hyperparathyroidism, a
component of osteodystrophy.
2.3.3 PROCEDURES:
Biopsy and histology may show FSGS, with associated renal parenchymal
atrophy and collapse or isolated glomerular hypertrophy.
2.3.4 STAGING:
The following is the Kidney Disease Outcome Quality Initiative (K/DOQI)
recommended classification of chronic renal disease by stage [60].
- Stage 1 disease is defined as normal GFR (>90ml/min per 1.73m2) and
persistent albuminuria.
- Stage 2 disease is characterized by a GFR of 60 – 89ml/min per 1.73m2 and
persistent albuminuria.
- Stage 3 disease by a GFR of 30.59ml/min per 1.73m2.
- Stage 4 disease is characterized by a GFR of 15 – 29ml/min per 1.73m2.
- Stage 5 disease is characterized by a GFR of less than 15ml/min per 1.73m2 or
End Stage Renal Disease (ESRD).
12
3.0 RESEARCH OBJECTIVES
3.1 GENERAL OBJECTIVE:
To evaluate and compare five predictive formulae for creatinine clearance
with measured value in Nigerian sickle cell disease patients.
3.2 SPECIFIC OBJECTIVE:
To determine whether calculated creatinine clearance correlates well with the
measured value(which involves a 24hour urine collection) and can be a substitute.
13
4.0 RESEARCH METHODOLOGY
The study was prospective over a period of 7 months. Subjects were
confirmed cases of sickle cell disease (ages ≥ 15 years) presenting for routine follow
up at Haematology Day Ward (HDW) or Haematology Outpatient Clinic (HOPC) of
Obafemi Awolowo University Teaching Hospitals Complex (OAUTHC), Ile – Ife.
4.1 CALCULATION OF SAMPLE SIZE:
Sample size[61] was determined using the formular:
n = z2pq
d2
where; n = the desired sample size.
z = the standard normal deviate set at 1.96, which corresponds to 95
percent confidence level.
p = the proportion in the population estimated to have the desired
characteristics (i.e. sickle cell disease. The prevalence of SCD in
Nigeria is approximated to 3 percent).
q = 1.0 – P
d = the degree of accuracy desired at P value <0.05.
thus
n =
6.44
05.0
03.00.103.096.12
2
However, sample size was increased to 100 for better representation. Control
subjects were fifty (50) in number,confirmed to have HbAA genotype.
14
4.2 ETHICAL CLEARANCE:
Ethical clearance was obtained from the research and ethics committee of the
OAUTHC, Ile – Ife,before commencing the study.
4.3 PATIENTS AND METHOD:
4.3.1 Inclusion Criteria:
All consenting adult patients with sickle cell disease, ages > 15years.
4.3.2 Exclusion Criteria:
1. Patients on Co-trimoxazole, Cimetidine, Probenecid or Cephalosporins (these
drugs interfere with both the tubular secretion of creatinine and the assay
(Jaffe) reaction).
2. Patients already on dialysis.
3. Patients with massive oedema or ketosis.
4. Patients who refuse to participate.
5. Sickle Cell Disease patients in crises
4.3.3 Methods:
Informed consent was secured from each participant. Clinical information
obtained from each participant included;name (initials only), sex, age (approximated
to their nearest birthday), Hospital number, frequency of crises in the one year, a
brief drug history, and transfusion history, in 2 years. These were all captured on a
case record form.
Each participant was physically examined and weighed on light clothing using
portable way master weighing scale (with a sensitivity of 50gram) and height in
15
metres was determined. The Body Surface Area (BSA) was calculated using the
Mosteller formular[62], while Body Mass Index was calculated using the standard
formular, .
4.3.4 Sample Collection:
After a thorough education on the procedure, a 24hour urine collection was
commenced for each patient between 7am and 7am of the following day. A 4 litre
wide bored container with boric acid added was provided to each patient for this
procedure. Upon completion of the collection, the following day, 7.0ml of venous
blood was collected from each participant following standard procedure [63], 4.0ml
was captured in lithium heparin specimen bottles for serum creatinine estimation.
The remaining 3.0ml of venous blood was captured in Potassium Ethylene
diamine tetra acetic Acid (K-EDTA) bottle for Full Blood Count (FBC) and
Reticulocyte count estimation. FBC was carried out with the aid of a Sysmex coulter
counting machine while reticulocyte percentage was done following standard
methodology [63]. Thereafter the reticulocyte index was calculated using the
formular[64].
The 24hour urine specimen collected was analyzed for total volume and
urinary creatinine. A fresh urine sample was collected from each participant for
urinalysis using Combi-9 dipstiks.
16
Control participants who were age-matched with the subjects, included House
Officers, Nurses, Medical and Nursing Students and other consenting individuals.
Haemoglobin electrophoresis was used to confirm their haemoglobin type and they
went through similar procedures and investigations as above. Serum and urine
creatinine estimations was assessed using Jaffe method [39]. In this method, creatinine
in the serum or plasma directly reacts with picric acid in an alkaline medium to form a
deep yellow complex. The amount of complex formed is directly proportional to the
level of creatinine in the sample.
Working Reagent:
A mix of equal volumes of sodium hydroxide (NaOH) and picric acid constitutes the
working reagent. This is stable for eight (8) hours at room temperature [65].
Procedure:
One ml of working reagent is added to 100µl of standard/sample, mix well and
read the absorbance after thirty (30) seconds and then after one hundred and twenty
(120) seconds at wavelength of 500nm. The reaction temperature is at 370C[65].
Calculation:
Change in absorbance of the sample or standard = absorbance at 120seconds –
absorbance at 30seconds.
For Urinary Samples:
Dilute sample 1 in 50 with distilled water and proceed as above [65].
17
5.0 DATA ANALYSIS:
The Statistical Programme for Social Sciences (SPSS) 17 and Microsoft Excel
2007 computer software were used for all data analysis. The mean, standard
deviations, correlation and linear regression analysis were done. The prediction error
was determined by the use of paired mean difference at 95% CI between the measured
and predicted creatinine clearance and student t test for paired samples shall be used
for statistical significance.
18
RESULTS
6.0 Demographic Data
A total of 100 patients and 50 controls consented and were enrolled into the
study over a 7 months period.
Table 1 shows a comparison of mean values between the patients and
controls (n = 50; 29 males and 21 female) with respect to the following variables;
age, weight, serum creatinine, urinary creatinine and blood pressure. The mean
age of patients was 26.22 + 7.42 years, that of controls was 25.66 + 4.77 years, p =
0.63. The mean weight for patients and controls were 51.88 + 10.74kg and 61.04 +
10.21kg, respectively, p= 0.000.
The mean serum creatinine for patients was 83.39 + 22.14 µmol/L and 91.26
+ 27.25 µmol/L for controls, p= 0.06. The mean urinary creatinine excretion for
patients was 3949 + 2190 µmol/24hours and 6205 + 3345 µmol/24hours, for
controls, with mean difference of 2256 µmol/24hours and p= 0.00.
The mean systolic BP for patients and controls were 105.81 + 12.48mmHg
and 113.20 + 7.94mmHg respectively, and mean difference was 7.39mmHg, P =
0.00. The mean diastolic BP for patients was 63.25 + 9.36mmHg, 75.40 +
5.70mmHg for controls, and mean difference was 12.15mmHg, p=0.00.
Tables 2-4 show the data of the 100 patients (40 males and 60 females),
stratified further into HbSS (n=79)and HbSC (n=21) and a summary of the means,
standard deviations and p-values at 95% CI of some evaluated parameters.The
median age for all patients (n= 100) was 25 years (range,15-56 years).The mean
ages for HbSS and HbSC were 25.3+6.7 years and 29.3+8.9 years respectively,
p=0.03.
19
The mean weight for all patients was 51.88 +10.74kg, that of controls was
61.04+10.21kg (p=0.000). The mean weights for HbSS and HbSC patients were
49.39+9.01kg and 61.24+ 11.73kg respectively, p=0.000. The mean serum
creatinine for all patients was 83.39 + 22.14 µmol/L, and 91.26+27.25µmol/L
(p=0.060) for controls. It was 81.99 + 20.40µmol/L for HbSS patients and 88.67 +
27.68 + µmol/L for HbSC patients, (p=0.221).
The mean urinary creatinine excretion for all patients was 3949 + 2190
µmol/24hours, and 6205+ 3344µmol/24hours for the controls. The mean urinary
creatinine excretion for male patients was 4631 + 2668 µmol/ 24hours, 3493 + 1679
µmol/24hours for female patients, and mean difference was 1138 µmol/24hours at
95% CI, p= 0.010. In the same vein, the mean urinary creatinine for HbSS patients
was 3813+2120umol/24hours while that of the HbSC was 4457+2422umol/24hours,
p=0.23.
The mean systolic blood pressure for all patients was 105.81 + 11.89mmHg
and 113.20+7.94 mmHg for controls (p=0.000). it was 109.75+ 11.87 mmHg and
103.18 + 12.28mmHg for males and females respectively. The mean difference at
95% CI was 6.57mmHg, P= 0.001.The HbSS arm gave a mean systolic blood
pressure of 105.52+11.75mmHg while it was 106.90+15.20mmHg for the HbSC
arm, p=0.23.
The mean diastolic blood pressure for all patients was 63.25 + 9.36mmHg
while that of controls was 75.40+ 5.70 mmHg (p=0.000). It was 64.39 +
10.37mmHg and 62.42 + 8.61mmHg respectively, for males and females. The mean
difference was 1.97mmHg at 95% CI, P= 0.28, it was 62.59+9.33mmHg and
65.71+9.26mmHg respectively for the HbSS and HbSC patients.
20
Fig 1 shows the stratification of patients based on the K/DOQI
recommended staging of CKD [60]. The mean value of measured creatinine clearance
for patients was 66.80 + 26.36 µmol/L.
BLOOD COUNTS
Tables 5A and B show the means and SD of haemogram and biochemical
parameters for SS, SC and controls. A mean PCV value of 25.6 ± 5.4% was
observed in all patients, 23.9±4.3%, 31.8±4.2% and 40.9±4.7% for the SS,SC and
controls, respectively. The mean value for WBC for all patients was
10,476±5,396/cmm and 11,612±5,448/cmm, 6,200±2013/cmm and
4094±1061/cmm for SS, SC and controls respectively. The mean platelet count for
all patients, SS,SC and controls were observed to be 258,380 ± 127,079/cmm,
274,113 ± 117,713/cmm, 199,190 ± 145,745/cmm and 198,580 + 68.490/cmm
respectively.
Correlation between patient’s PCV, reticulocyte index, crises frequency,
transfusion episodes and measured creatinine clearance is as in Table 6A. PCV
with measured creatinine clearance yielded, r = 0.19 , p =0.06.
Mean reticulocyte indexes of 1.84 + 0.70 were obtained for all the patients,
while values of 1.90±0.76, 1.60±0.50 and 2.00±0.71 were obtained for SS, SC and
controls respectively. Values of 0.32 and 0.04 where obtained for r and p
respectively when a correlation analysis was done with the measured creatinine
clearance for all patients.
A comparison of the means of haemogram parameters and proteinuria
between HbSS and SC cohorts is as in Table 6B. Table 7 is a correlation of
haemogram and proteinuria with measured creatinine clearance in the HbSS
Cohort.
21
PROTEINURIA
Varying degree of proteinuria was observed in 16 patients, mean value
was 0.41 + 1.08, Table 5B.This was 0.52±1.20 in SS patients, no proteinuria was
observed in SC and controls. When compared with the measured creatinine
clearance for all patients,-0.05 and 0.62 were obtained for the ‘r’ and p values
respectively, Table 6A.
VASO-OCCLUSIVE CRISES (VOC)
A mean crises episodes of 2.72 + 1.63 per year was observed in all the
patients, Table 5B.Mean values of 3.00±1.62 and 1.67±1.24 were observed in SS
and SC patients. When correlated with measured creatinine clearance ‘r’ was -0.22,
while p= 0.03, Table 6A.
HISTORY OF TRANSFUSION
Table 5B shows the mean transfusion episode in the previous 2 years. In all
the patients this was 0.41+ 0 .668 while it was 0.51±0.71 and 0.05±0.22 in SS and
SC patients. Regression parameters between transfusion episodes and measured
creatinine clearance for all patients yielded r and p values of 0.02, and 0.86
respectively, Table 6B.
22
REGRESSION PARAMETERS BETWEEN MEASURED AND PREDICTED
CREATININE
Table 8 shows the regression parameters between the measured and
predicted creatinine clearance in patients and controls. There linear regression
equations are as hereunder;
Patients (n= 100, p = < 0.001, in all )
Cockcroft and Gault : y =0.674x + 38.97 r2 = 0. 428
Mawer : y = 0.704x + 39.95 r2 = 0.426
Hull: y= 0. 824x + 64.52 r2 = 0.3 86
Gates: y= 0.873x +38.00 r2 = 0. 398
MDRD : y = 0. 962x + 50. 84 r2 = 0. 405
Controls (n = 50, p= < 0. 001 in all )
Cockcroft and Gault: y = 0. 483x + 48.2 1 r2 = 0. 075
Mawer: y = 0. 497x + 49.68 r2 = 0. 072
Hull : y = 0.609x + 55.45 r2 = 0.088
Gates :Y = 0.665x + 29.30 r2 = 0.112
MDRD: y = 0.707x + 42.14 r2 = 0.101
Where x = predicted creatinine clearance.
Figures 2-6 show correlation graphs between the measured value and each of the
formula in patients, while figures 7-11 show similar graphs for controls.
23
PREDICTION ERROR
The paired mean differences at 95% CI between the measured and predicted
creatinine clearance were calculated using the students –t test (Table 9). The
prediction error, PE for the 5 equations in patients and controls are as in Table 8.
COMPARISON OF THE EQUATIONS
The following criteria were used to compare the equations in patients and
controls; Table 8;
1. The closer the r value to 1 the better the equation .
2. The higher the r2 value, the better the equation .
3. The closer the slope values to 1, the better the equation.
4. The lower the intercept, the better the equation
5. The lower the prediction error at 95% CI, the better the equation.
Table 10 shows a comparison of the means and standard deviation of calculated
Crcl in patients with proteinuria
24
7.0 DISCUSSION
This study attempted to demonstrate how comparable measured values of
creatinine clearance are to the calculated values derived from a set of predictive
formulae in SCD patients and controls. It has also in addition explored other pertinent
issues related to sickle cell nephropathy.
The majority of the patients that took part in this 7-month study were less than
30years of age. This is a reflection of the cohort of patients seen regularly at the sickle
cell clinic and agrees with an earlier report from this centre [66].
There was no significant difference between the mean weight of male and
female patients and their serum creatinine values. This is most probably explained by
the general asthenia seen in SCD patients. Expectedly, there was a significant
difference in the weights of SS and SC patients (p=0.000),the small cohort of SC
patients studied however may explain the absence of significant difference in their
serum creatinine values. Serum creatinine is known to increase with increasing body
mass.[39].
Male SCD patients had a significantly higher urinary creatinine excretion (p=
0.0102). This is in keeping with previously observed trend [39]. More so, a
significantly higher mean BP (systolic) of male patients, (109.75+11.87) compared
with that of their female counterparts, (103.18+ 12.28mmHg) (P=0.009) is quite in
keeping with variation of BP values by gender, even in health. No significant
difference was observed in the urinary creatinine excretion and systolic BP in both the
SS and SC arms of the study, p=0.233.
The mean measured creatinine value of 66.80+26.36mol/L obtained for
patients in this study alludes to the severity of renal impairment in SCD. The K/DOQI
staging of patients indicates that 64% of patients had sub- normal creatinine
25
clearance. A similar study carried out in Saudi Arabia [11] reported that a lower
proportion of patients (22.5%) had subnormal creatinine creatinine. A comparison of
some of the evaluated parameters in patients and controls expectedly showed
significant differences in the two populations with respect to weight, urinary
creatinine and blood pressure (p values ≤ 0.000). There was however no significant
difference in the ages (p=0.63) as both cohorts were age matched. Worthy of note is
the lack of significant difference between the serum creatinine values in disease and
in health. Alluding to the inadequacy of serum creatinine values estimation alone as a
marker of renal disease [42].
Blood Counts:
The blood count for SS patients (n= 79) showed that majority of the patients
had moderate anaemia (mean PCV =23.9 + 4.3%). Expectedly SC patients had mainly
a mild anaemia while majority of the controls had normal PCV. All patients were
steady state, however, severe anaemia (PCV<18%) was the predominant findings in
patients in crises, as reported in an earlier work done in this centre [66].
All the study had a mean WBC and platelet count values that were within
normal reference intervals. A comparison of the means of haemogram parameters
between HbSS and SC showed a significant difference in PCV, WBC and platelet
counts in the two cohorts (P values < 0.05).This is not an unusual finding as SC
patients are known to have a higher base line haemogram which follows a less
severe disease observed in them. There was however no significant difference in
reticulocyte index in the two populations, indicating a good bone marrow response in
both groups.
There was no significant correlation between the full blood count and
measured creatinine clearance in the HbSS cohorts. This is not surprising as renal
26
impairment becomes more profound with advancing age13. Cohorts studied were
predominantly young adults, mean age of 26.2+ 7.4 years.
The reticulocyte index in SS,SC and controls were within normal limits. There
was no significant correlation when compared with measured creatinine clearance
both for all patients and for the SS cohorts. Reticulocytopaenia is a reflection of
worsening nephropathy resulting in reduced erythropoietin production. Majority of
the patients studied however had normal creatinine clearance (31%) and stage 2
nephropathy (39%), which may contribute to the poor correlation noted above.
PROTEINURIA
Proteinuria was seen in 16% of the patients studied, but this was not observed
in either the SC or control. There was poor correlation with measured creatinine
clearance even in SS patients. A higher rate of proteinuria (41% ) was noted in a
similar study in SCD patients in Saudi Arabia 11.
VASO-OCCLUSIVE CRISES (VOC)
A mean VOC episode of 3.00 ± 1.62 in one year was observed in SS patients.
Previous study in SCD patients had documented that painful crises greater than three
episodes or more per year is an indicator of severe disease (with probably more end
organ damage) and consequent reduced survival [66]. Moreso, in the cooperative study
of Platt et al, in American black population, 5% of patients experienced 3-10 pain
crises in a year [67]. Pain frequency was noted to be a predictor of early death in the
same study. No correlation however was noted between VOC episodes and measured
creatinine clearance in the patients.
27
HISTORY OF TRANSFUSION
A mean transfusion demand of 0.41 in two years was observed in the patients.
Transfusion requirement in SCD patients is minimal in steady state conditions [68]. An
average blood transfusion requirement of 0.5 units /patient/year was reported by
Lucio Luzzato in these patients[68]. This however rises sharply with occurrence of
crises like aplastic, haemolytic and/or sequestration crises [68]. No significant
correlation was found between transfusion episodes and measured creatinine
clearance in this study.
COMPARISON OF FORMULAE
In comparing each of the five formulae with measured creatinine clearance in
patients and controls, the Pearson correlation was used to generate the following;
1. The slope and intercept of their correlation graphs
2. The coefficient of correlation (r)
3. The coefficient of differentiation (r2)
The prediction error for each formular relative to the measured creatinine
clearance was also assessed using the paired mean differences at 95% CI.
More so, a comparison of the means of calculated creatinine clearance in patients
with proteinuria confirmed that the Cockcroft-Gault formular was able to establish a
more profound impairment in renal function more than the others.
The best formular was one whose r and slope values are closest to one, has the
highest r2 value and with lowest intercept and prediction error. The Cockcroft -Gault
formula provided a satisfactory assessment of renal function in the patients. This is
reflected by the high r, r2 values and lower predictive error. All the formulae however
performed poorly when applied to the control arm.
28
This finding is similar to the work done by Lamb et al [69] and at variance with
reports by Sanusi et al [55] and Mahajan et al [58]. Both noted the adequacy of predictive
equations in assessing renal functions in healthy cohorts.
The Cockcroft-Gault formula can thus reliably predict the creatinine clearance in
SCD patients. More so, its ease of recall makes it a very useful clinical tool that can be
used to monitor renal function in SCD patients.
29
Figure 1
K/DOQI STAGE PERCENTAGE
Normal 31
Stage 1 5
Stage 2 39
Stage 3 23
Stage 4 2
Stage 5 0
30
Figure 2: Correlation graph: Measured Creatinine Clearance vs Hull formula-
patients
Calculated CrCl
Mea
sured
CrC
l (Ml/M
in
31
Figure 3: Correlation graph: Measured Creatinine Clearance vs Cockroft- Gault
formula-patients
Calculated Cr Cl
M
easu
red C
rCl
(Ml/
Min
)
32
Figure 4: Correlation graph: Measured Creatinine Clearance vs Mawer
formula– patients
y = 0.704x + 39.95R² = 0.426
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
0.00 50.00 100.00 150.00 200.00
Mawer
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
33
Figure 5: Correlation graph: Measured Creatinine Clearance vs Gates formula –
patients
Calculated Cr Cl
M
easu
red
C
rCl
(Ml/
Min
)
34
Figure 6: Correlation graph: Measured Creatinine Clearance vs MDRD
formula– patients
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
35
Figure 7: Correlation graph: Measured Creatinine Clearance vs Hull formula –
control
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
36
Figure 8: Correlation graph: Measured Creatinine Clearance vs Mawer
formula– control
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
37
Figure 9: Correlation graph: Measured Creatinine Clearance vs Cockroft -Gault
formula – control
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
38
Figure 10: Correlation graph: Measured Creatinine Clearance vs Gates formula
control
y = 0.665x + 29.30R² = 0.112
0.00
50.00
100.00
150.00
200.00
250.00
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00
Gates
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
39
Figure 11: Correlation Graph: Measured Creatinine Clearance Vs MDRD
Formula – Control
Calculated Cr Cl
M
easu
red
C
rCl (
Ml/
Min
)
40
Table 1 Comparison of the Means and Standard Deviations of Some Patients and
Control Parameters.
VARIABLES N MEAN SD P
Age Study 100 26.22 7.42
0.628 Control 50 25.66 4.77
Weight Study 100 51.88 10.74
0.000 Control 50 61.04 10.21
SCr Study 100 83.39 22.14
0.060 Control 50 91.26 27.25
UCr Study 100 3949 2190
0.000 Control 50 6205 3345
SBP Study 100 105.81 12.48
0.000 Control 50 113.20 7.94
DBP Study 100 63.25 9.36
0.000 Control 50 75.40 5.70
41
Table 2A: Comparison of Means of Age by Haemoglobin Type and Gender
VARIABLES N MEAN
Age (year)
SD P
SS 79 25.34 6.77
0.030 SC 21 29.33 8.97
Male 40 27.08 8.21 0.349
Female 60 25.65 6.85
42
Table 2B: Comparison of Means of Weight by Haemoglobin Type and Gender
VARIABLES N MEAN
Weight (Kg)
SD P
SS 79 49.39 9.01 0.000
SC 21 61.24 11.73
Male 40 50.51 12.47
0.299 Female 60 52.80 9.41
43
Table 3A: Comparison of Means of Serum Creatinine by Haemoglobin Type and
Gender
VARIABLES N MEAN SD P
SS 79 81.99 20.40 0.221
SC 21 88.67 27.68
Male 40 84.00 22.43 0.823
Female 60 82.98 22.13
44
Table 3B: Comparison of Means of Urinary Creatinine by Haemoglobin Type and
Gender
VARIABLES N MEAN SD P
SS 79 3813.42 2120.41 0.233
SC 21 4457.10 2422.40
Male 40 4631.43 2668.04 0.010
Female 60 3493.37 1678.52
45
Table 4A: Comparison of Mean of Systolic Blood Pressures by Haemoglobin Type
and Gender
VARIABLES N MEAN SD P
SS 79 105.52 11.75 0.233
SC 21 106.90 15.20
Male 40 109.75 11.87 0.009
Female 60 103.18 12.28
46
Table 4B: Comparison of Mean of Diastolic Blood Pressures by Haemoglobin Type
and Gender
VARIABLES N MEAN SD P
SS 79 62.59 9.33 0.176
SC 21 65.71 9.26
Male 40 64.39 10.37 0.278
Female 60 62.42 8.61
47
Table 5A: The Means, SD Of Haemogram, Crises Frequency and Reticulocyte
Index of all Patients, SS, SC, and Controls
VARIABLE SUBJECTS N MEAN SD
PCV All patients
HBSS
HBSC
Control
100
79
21
50
25.6
23.9
31.8
40.9
5.4.
4.3
4.2
4.7
WBC All patients
HBSS
HBSC
Control
100
29
21
50
10,476
11,612
6,200
4094
5,396
5,448
2013
1061
Platelet
Count
All patients
HBSS
HBSC
Control
100
79
21
50
258,380
274,113
199,190
198,580
127,099
117,713
145,745
68.490
Reticulocyte
Index
All patients
HBSS
HBSC
Control
100
79
21
50
1.84
1.90
1.60
2.00
0.70
0.76
0.50
0.71
Frequency
of crises
(in previous
one year)
All patients
HBSS
HBSC
Control
100
79
21
50
2.72
3.00
1.67
0.00
1.63
1.62
1.24
0.00
48
Table 5B: The Means of Biochemical Parameters and Transfusion History in all
Patients, SS, SC and Controls.
VARIABLE SUBJECTS N MEAN SD
Creatinine
clearance
(ml/minute)
All patients
HBSS
HBSC
Control
100
79
21
50
66.80
66.28
68.76
97.04
26.37
27.52
21.95
17.47
History of
transfusion
(in previous
2years)
All patients
HBSS
HBSC
Control
100
29
21
50
0.41
0.51
0.05
0.00
0.67
0.71
0.22
0.00
Proteinuria All patients
HBSS
HBSC
Control
100
79
21
50
0.41
0.52
0.00
0.00
1.08
1.20
0.00
0.00
49
Table 6A: Correlation between Patient’s Parameters and Measured Creatinine
Clearance
Measured
Creatinine
Crises History of
Transfusion
Reticulocyte
Index (%)
PCV Proteinuria
r value -0.222 -0.018 0.322 0.190 -0.050
p value 0.026 0.862 0.038 0.059 0.623
50
Table 6B: Comparison of Means of Some Measured Parameters Between SS and
SC
Variable Reticulocyte
Index
PCV WBC
(x 109/L)
Platelet
Count
(x 109/L)
Proteinuria
SS 1.90 23.9 11,613 274,114 0.519
SC 1.60 31.8 6,200 199,190 0.000
P value 0.102 < 0.001 < 0.001 0.0155 0.051
51
Table 7: Correlation of Some Measured Parameter for SS with Measured
Creatinine Clearance
Reticulocyte
Index
PCV WBC Platelet
Count
Proteinuria
Measured CrCl r=0.366 r=0.196 r= 0.094 r= 0.0119 r=0.046
P values 0.03 0.042 0.205 0.460 0.342
52
Table 8: Regression Parameters between measured and predicted Creatinine
Clearance in patients and controls.
PATIENTS
Formulae CC(r) CD(r
2
) Slope Intercept PE Remarks
Cockcroft
Gault
0.654 0.428 0.6745 38.971 17.23 Most
predictive
Mawer 0.653 0.426 0.7043 39.954 20.20 2nd most
predictive
Hull 0.621 0.386 0.8241 64.52 52.77 Least
predictive
Gates 0.631 0.398 0.8733 38.005 29.54 4th most
predictive
MDRD 0.636 0.405 0.9625 50.849 48.35 3rd most
predictive
53
CONTROLS
Formulae CC(r) CD(r
2
) Slope Intercept Remarks
Cockcroft-
Gault
0.274 0.075 0.483 48.219 Poor correlation
Mawer 0.268 0.072 0.498 49.682 Poor correlation
Hull 0.297 0.088 0.610 55.458 Poor correlation
Gates 0.335 0.112 0.660 29.304 Poor correlation
MDRD 0.318 0.101 0.708 42.146 Poor correlation
54
TABLE 9: T – Test for paired samples with mean difference at 95% CI
n-= 100 Mean SD PMD P value
Hull (Crcl)
Measured (Crcl)
119.57
66.80
34.96
26.36
52.77
0.000
Mawer (Crcl)
Measured (Crcl)
87.00
66.80
28.44
26.36
20.20 0.000
Cockcroft/Gualt (Crcl)
Measured (Crcl)
84.03
66.80
27.16
26.36
17.23 0.000
Gates (Crcl)
Measured (Crcl)
96.34
66.80
36.49
26.36
29.54 0.000
MDRD (Crcl)
Measured (Crcl)
115.15
66.80
39.84
26.36
48.35 0.000
55
Table 10: Comparison of the Means and Standard Deviation of calculated Crcl in
patients with Proteinuria
Variable N Mean Std. Deviation
HULL 63 124.41 34.780
MAWER 63 87.54 26.385
C-G 63 84.56 25.174
GATES 63 100.67 38.003
MDRD 63 120.21 41.074
56
8.0 CONCLUSION
This study has highlighted the magnitude of sickle cell nephropathy in this
environment and the need for monitoring of patients to initiate proactive
interventions that could halt progression to ESRD and reduce mortality.
The correlation between the Cockcroft and Gault formular makes it a good
predictor of Sickle cell nephropathy.
9.0 RECOMMENDATIONS
Based on the observation of this study, the following recommendations are
hereby suggested:
1. Calculated creatinine clearance using the Cockcroft and Gault formular should
be used to evaluate and subsequently monitor renal function status in SCD
patients.
2. Further study is necessary to find appropriate formulae for similar healthy
controls as evaluated in this study.
LIMITATIONS
1. Estimation of total urinary protein would have shown the pattern of excretion.
Urinary protein excretion in the nephrotic range may indicate a higher risk of
progression to ESRD.
2. Microalbuminuria should have been a better index of early nephropathy than
dipstick proteinuria.
3. A supervised 24-hour urine collection should have been better in this study,
but this would have entailed having to admit all 150 participants.
57
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66
APPENDIX I
CASE RECORD FORM
Patient’s name (initials only) –
Age (approximated to the nearest birthday) –
Weight (kg) –
Sex –
Height (m) –
Body Surface Area -
Body mass Index –
Frequency of major crises in the past one year –
Transfusion history (Number of transfusions in the last two years) –
Drugs patient is presently one or has taken within the past one week –
67
APPENDIX II
INFORMED CONSENT SHEET
CREATININE CLEARANCE ESTIMATION FROM SERUM CREATININE
VALUES: EVALUATION AND COMPARISON OF FIVE PREDICTION
FORMULAE IN NIGERIA SICKLE CELL DISEASE PATIENTS
Client’s Agreement:
I have been informed of the above study and i have had the opportunity to ask
questions and my questions have been answered to my satisfaction. I agree that blood
investigations shall be carried out on me with collection of 7.0mls of blood. I also
agree that some tests shall be carried out on my urine sample.
I have the right to withdraw from the study at anytime.
Yes ………………………… No …………………………
………………………………………………………… ….....……..……...
Signature/Thumbprint of Research Respondent Date
……………………………………………………………… …….....……..……..
Signature/Thumbprint of Person Obtaining Consent Date
………………………………………… …….....……..……..
Name of Research Respondent Date
68
APPENDIX III
List of formulae used in study
1. Mawer et al 1972
Cr Clmale =
4.14
)203.0(3.29(
Scrx
xagewt ( 1-0.03 x Scr )
Cr clFemale =
4.14
)175.0(3.25(
Scrx
xagewt( 1-0.03 x Scr )
Scr = serum creatinine in mg/dl
wt = Weight in Kg
Age is in years
2. Cockcroft & Gault 1976
Cr Clmale =
xScr
xwtage
72
)140
Cr clFemale = Cr Cl male x 0.85
Scr = serum creatinine in mg /dl
3. Hull et al 1981
Cr Cl male = 88 (148-age) – 3
Scr
Cr Cl female = 75 (145 – age ) – 3
Scr
Scr = Serum Creatinine in mol /L
age is in years .
69
4. Gates 1985
Cr Cl male = 89.4 (Scr-1.2) + ( 55 – A) ( 0.005) (89.4) (Scr-1.1)
Cr Cl female = 60(Scr-1.1) + (56 –A) ( 0.005) (60) (Scr-1.1)
Scr = Serum creatinine in mg/dl; A = age in years
5.MDRD (2000)=
186 x (Scr/88.4)-1.158 x (age) –0.203 x (0.742 if female)