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)