Assessment of the hematological profile of children with chronic kidney disease on follow-up at St. Paul’s Hospital Millennium Medical College and Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia

doi:10.21203/rs.3.rs-1998213/v1

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Research Article

Assessment of the hematological profile of children with chronic kidney disease on follow-up at St. Paul’s Hospital Millennium Medical College and Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia

https://doi.org/10.21203/rs.3.rs-1998213/v1

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published 30 Jan, 2024

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Background: Chronic kidney disease (CKD) is a major public health issue with an increasing incidence and prevalence worldwide. In CKD, hematological parameters are influenced, and the effect increases with CKD stage. It is becoming more common in Ethiopia, and it is associated with high morbidity and mortality rates, but there have been very few studies on the hematological profile of children with chronic kidney disease, on follow-up in general, and particularly among chronic kidney disease patients.

Methods: At Tikur Anbessa Specialized Hospital and St. Paul's Hospital Millennium Medical College in Addis Ababa, a cross-sectional study was carried out from March to June 2021. EDTA tubes were used to collect a total of 238 blood samples, which were then examined on a Beckman Coulter automated hematology analyzer. SPSS Version 20 was used for statistical analysis, and a logistic regression model was applied.

Results: The total number of patients in the study was 238, with 42 (59.7%) of them being men. With a p-value of 0.05, there was a significant correlation and association with having co-morbidities such as hypertension and anemia, as well as a low range of Hgb in bivariate correlational analysis. In a multivariate regression analysis, eGFR-based CKD stages were strongly associated with a low range of hemoglobin concentration, with a p-value of 0.001. (0.000). Overall, 39 (or 16.4%) of CKD patients were anemic. In terms of age, 176 (73.9 %) of the children were over the age of five. The majority of the patients (81%) had CKD stage 1(34%). The study also found that having CKD for a longer period of time (> 5 years) and being hypertensive have an effect on the growth indicator MUAC, with a p-value of 0.0001 and 0.0001.

Conclusion: The study found that the majority of study participants were in stages 1 to 3 based on their eGFR, which is significantly related to RBC, HGB, and HCT count. There is a need to improve multiple aspects of CKD management, including routine hematological tests for children with chronic kidney disease, specifically RBCs, Hgb, PLT, WBCs, and differential count.

Children

Hematological profile

chronic renal failure

Chronic kidney disease (CKD) is a major public health issue worldwide. It is defined by persistent urine abnormalities, structural abnormalities, or impaired excretory renal function suggestive of a loss of functional nephrons [1]. Although CKD is defined by the presence of kidney damage or an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 m², it can last for 3 months or longer, regardless of the cause. Chronic kidney disease (CKD) is recognized as a major non-communicable disease with a growing epidemic global impact [2].

Chronic kidney disease (CKD), also known as Kidney or renal failure, refers to all five stages of kidney damage, beginning with very mild damage in stage 1 and progressing to complete kidney failure in stage 5. eGFR values were used for CKD staging in children over the age of two because GFR values in younger children were low due to on-going renal maturation. Children with CKD may present to clinicians with a variety of issues, including growth, nutrition, electrolyte imbalances, anemia, immunizations, hypertension, and renal transplantation [3]. Congenital kidney abnormalities, glomerulonephritis, systemic disease, hereditary, sepsis, and other rarer cases, such as drug (toxin)-related renal disease, were the causes of CKD in children. CKD can be caused by any of three disease processes: pre-renal (lower renal perfusion pressure), intrinsic renal (pathology of the vessels, glomeruli, or tubules-interstitial), or post-renal (obstructive). Anemia is the most common complication of chronic kidney disease, and it can develop in CKD patients due to low hemoglobin concentration and hematocrit. Anemia as a symptom of chronic kidney disease is mostly normochromic-normocytic followed by hypochromic-microcytic and moderate in severity in relation to impaired renal function in CKD patients.

Hematological parameters such as white blood cell (WBC), platelet (PLT), and red blood cell (RBC) parameters are usually affected in CKD. These are common in CKD due to erythropoietin (EPO) deficiency/reduction and other factors such as increased hemolysis, suppression of bone marrow erythropoiesis, hematuria, and gastro intestinal blood loss. Other hematological parameters affected by CKD include total leukocyte and differential counts, platelets count, bleeding time, and pro-thrombin time. CKD appears late in ESRD and is the terminal stage of chronic renal failure with irreversible progressive deterioration in renal function [4]. However, compared to the general population, children with CKD continue to have very high rates of morbidity and mortality. Cardiovascular disease and infections were the leading causes of death in children with CKD, accounting for 30-40% of deaths [5].

Chronic kidney disease (CKD) is a global public health issue, with rising healthcare costs, particularly in developing countries such as Ethiopia. Its burden is epidemic, affecting both developed and developing countries. According to the global burden of disease 2015 study, CKD was the 17th leading cause of death worldwide in 2015 and one of the fastest growing major causes of death [1]. Lack of advanced diagnostic infrastructure, treatment facilities, and human resources, particularly in Africa, frequently leads to inaccurate diagnosis and suboptimal treatment of children with renal diseases.

There had never been any research done in Ethiopia. This is the primary research gap that has been identified. Since no evidence was found in Ethiopia during the follow-up renal unit for children with chronic kidney disease, In order to better understand the hematological profile of children with chronic kidney disease, this study follow them as they receive follow-up care at Tikur Anbessa Specialized Hospital and St. Paul's Hospital Millennium Medical College in Addis Ababa, Ethiopia.

The study's data findings could be useful in preventing unnecessary complications, enhancing the quality of life for people with chronic renal diseases, and extending their life expectancies. The information from this study was helpful for decision-makers in the planning, implementation, and evaluation of various interventions related to improving the care provided in the follow-up set up as well as health care workers who work in follow-up units through identification and improving the service quality. Additionally, by describing the experience and results of centres managing patients with chronic kidney disease, and by offering recommendations and useful tools to assist clinicians in providing quality service for children, including infrastructural, staffing needs equipment and prescriptions, and patient selection and training, it is important for the community, other researchers, policy makers (planning, revision of guideline)/ stakeholders.

From March to June 2021, a cross-sectional study was carried out at Tikur Anbessa Specialized Hospital (TASH) and St. Paul's Hospital Millennium Medical College (SPHMMC) in Addis Ababa., Ethiopia. The study was carried out in Addis Ababa, the largest city and capital of Ethiopia, at SPHMMC and TASH.

The study population consisted of all children with chronic kidney diseases who were enrolled in the follow-up program at SPHMMC and TASH in Addis Ababa City during the research period. The sample size is determined using a single sample size estimation formula. The value of P is taken from a previous study conducted in Nigeria [6] and is 0.83 (82.9 %) estimated prevalence. The initial sample size was estimated to be 216 participants, and the final sample size was calculated as 238 (n=238) by computing a 10% (22 subjects) non-response rate. The proportionate allocation formula is used to calculate the number of samples to be included in the study from each hospital. As a result, the study requires 20 samples from SPHMMC and 218 samples from TASH.

To collect socio-demographic and other related data, a pre-designed questionnaire was used. Aseptically, 3-5 mL of blood was collected into a lavender-top vacationer tube containing K2-Ethylene diamine tetra acetic acid. To avoid blood clot formation, the blood was gently mixed with EDTA anticoagulant. All venous blood samples were analyzed within 24 hours of being collected. The specimen was then transported immediately to the laboratory for hematologic analysis. Beckman Coulter's automated hematology analyzer was used to examine the sample. The assigned professional and principal investigator oversaw the entire data collection process.

To get hematological parameters all samples were immediately processed for analysis using Beckman Coulter's automated hematology analyzer. The DxH 800 analyzes 24 parameters quickly, including a 5-part WBC differential and histograms for RBC, PLT, and WBC in blood. The Coulter Principle underpins the DxH 800 CBC analysis. In whole blood mode, this device can analyze factors such as reticulocyte count with peripheral morphology, WBC, RBC, HGB, HCT, MCV, MCH, MCHC, NEUT %, LYM %, PLT, LYM #, NEU #, MPV, RDW-CV and RDW-SD, and P-LCR.

To keep data quality, all pre-analytical and analytical variables that can influence test results must be reflected in analytical results from reference populations. As a result, all pre-analytical factors are carefully defined and used for testing, including subject preparation, sample collection and processing, the analytical method, and instrumentation (daily, weekly, and monthly preventive maintenance) check-ups. The reliability of the data generated is critical because the methods imprecision and accuracy will determine its diagnostic utility. The results of the entire CBC were recorded, handled appropriately, and secured.

Data analysis and interpretation

SPSS Version 20 statistical software was used to enter and analyze the data. To analyze, descriptive statistics were used. To identify factors that have an association with the dependent variable, Pearson correlation, linear, bivariate, and multivariate logistic regression analysis were performed. The odds ratio (OR) was used to assess the strength of the association.

Socio-demographic characteristics

A total of 238 children with chronic kidney disease were included in the study, with a 100% response rate. Of them 142 (59.7 %) of these were men whereas 176 (73.9 %) of the children were over the age of five. Children in this study had a mean weight of 24.25 ± 10.97kg, a mean height of 1.24 ± 0.257cm, a mean MUAC of 16.43 ± 4.187, and a mean BMI of 15.23 2.94. In terms of education, 152 (63.9%) of the patients have a primary education. 77 (32.4 %) of patients with CKD had been suffering from the disease for 1 to 3 years. (Table 1) Hematologic profile of children with chronic kidney disease

The mean WBC count was 8.9± 3.32 (thousand/ L), with mean differential counts of monocyte 0.93± 3.02 (thousand/ L), eosinophil 0.3± 0.51 (thousand/ L), basophil 0.09± 0.24 (thousand/ L), neutrophil 4.59± 8.30 (thousand/ L), and lymphocyte 2.78 ± 1.62 (thousand/ L).

The mean RBC count was 4.73± 0.86 (million/L), and it was found to be positively correlated with eGFR with a P-value of 0.000. The average hemoglobin concentration was discovered to be 12.± 8 2.76(g/dL). The mean HCT was 38.2± 7.53%, which was significantly correlated with the eGFR with a P-value of 0.001. The mean red cell indices (MCV, MCH, and MCHC) were 80.3± 79.899 (fL), 27.6± 6.01 (Pg), and 32.9± 3.77 (g/dL), respectively.

The mean PLT count of children with CKD was 349.3± 130.1(thousand/L), a mean MPV of 9.03± 4.30 (fL) which was significantly correlated with the eGFR with a P -value of 0.0001 and negative correlation with RBC, HGB and HCT with a p-value of 0.010, 0.006, and 0.004 (Table 2 &3).

Health conditions and co-morbidities of children with chronic kidney disease

The stages of CKD were determined by calculating the estimated glomerular filtration rate using the modified Schwartz formula of 0.413*Ht(cm)/(Serum creatinine in mg/dl) and 81(34%) of children with CKD were in stage 1, 58(24.4%) were in stage 2, 63(26.5%) were in stage 3, 9(3.8%) were in stage 4, and 9(3.8%) were in stage 5. According to 128(53.8 %), 30(12.6 %), 14(5.9 %), and 2(0.8%) respondents, the causes of their kidney disease were congenital abnormality, urinary tract infection, hypertension, and kidney stones. According to 43(18.1 %) of the study participants, co-morbidities such as congenital renal abnormalities with UTI, congenital renal abnormalities with hypertension, UTI with kidney stones, and hypertension with UTI contribute to the progression of their kidney problem into stages of CKD. In addition, 17(7.1%) of children have other health conditions such as congenital heart defect 9(3.8%), neurogenic bladder 3(1.3%), hydro ureter 2(0.8%), and hypertension 3(1.3%). Because of their kidney disease, 74(31.1 %) of children have hypertension for more than a year, as 16(6.7 %), 1 - 3 years 27(11.3 %), 3-5 years 13(5.5 %), and 5 - 10 years 18(7.6 %). Despite this, only 16 (6.7 %) of them monitor their hypertension at home.

In this study, 39(16.4 %) of the children were anemic and had lived with it for more than a year. And 24(10.1%) of them are currently being treated with anemia medications. Only 5% (.1%) had been on dialysis before (Table4).

Regarding growth indicators, based on the WHO growth chart for BMI for age 5 – 19 years and MUAC for age less than 5 years, out of 176 children 5 - 19 years, 49(27.8 %) have a body mass index of - 1SD, 47(26.7 %) have a normal body mass index, and 32 have a low body mass index (18.1 % ) -2SD, and 19(10.7 %) -3SD, which is associated with anemia with a p-value of 0.009 and medication with a p-value of 0.026. About 19 (36.5 %) of children under the age of 5 have an arm circumference of - 2SD, 11 (21.1 %) have a - 1SD, 7 (13.4 %) have a normal MUAC, and 2 have a large MUAC (3.84 %) - 3SD is associated with an increased length of stay in patients with CKD, hypertension, low platelet count, and RDW, with p-values of 0.0001, 0.001, 0.013, and 0.022, respectively (Table 5).

With p-values of 0.001, 0.007, and 0.001, a positive correlation was found between eGFR and a low range of RBC, HGB, and HCT. With a p-value of 0.002, this finding strengthens the relationship between anemia and the severity of the need for medication. With a p-value of 0.003, hypertension was also found to be significantly related to CKD stages based on eGFR (Table 6)

In this study, the hematological profile of children with chronic kidney disease was assessed on follow-up at SPHMMC and TASH, and it was discovered that 34% of stage1, 24.4 % of stage2, 26.5 % stage3, 3.8 % stage4, and 3.8 % stage5 of CKD based on their estimated glomerular filtration rate. With a p-value of 0.05, this finding was significantly correlated and associated with having co-morbidities such as hypertension and anemia. Congenital abnormalities were the leading cause of kidney disease in children based on the study participant family and guardians’ respond. The study also found that having CKD for a longer period of time (> 5 years) and being hypertensive have an effect on the growth indicator MUAC with p-values of 0.000 and 0.001, respectively.

According to this study, 81 (34%) of the participants had stage 1 and 9 (3.8%) stage5 CKD; it’s consistent with a study conducted in Nigeria, which found that the majority of CKD patients (82.9 %) were in stage1 [6]. In contrast, another North American study found that 68 % of CKD patients were in stage 5[7, 8]. Similarly, another study conducted in Cameroon found that the majority of CKD patients (81.3 %) were in stage 5 and 18.3 % were in stage 4 [9]. This contradicts our findings, which found that the majority of CKD patients 81 (34 %) were in stage 1 and 9 (3.8 %) were in stage 5. This disparity could be attributed to the late diagnosis of children with CKD, with the majority of patients having more than one indication, as well as the late presentation of sick children to the hospital.

The current study found that the total RBC count (4.73± 0.86 (million/L)), hemoglobin concentration (12.± 8 2.76(g/dL), and percentage hematocrit (38.2± 7.53%) had a positive correlation with CKD stages, indicating that the RBCs parameters were higher than the study conducted in Cameroon, which found that the hemoglobin level ranged from 5.26 -10.1 gm/dl and the mean hemoglobin value was 7.68gm/dl [9]. Another study conducted in North America found that the mean hemoglobin level for the entire cohort was 11.81.8 g/dl, which is lower than the level found in this study. This difference could be attributed to the severity of chronic kidney disease and the large sample size/respondents involved in this study [10, 11]. In contrast, a similar study conducted in the United States and North America revealed that the mean +SD of hemoglobin levels were 12.2 g/dl, +1.6 g/dl, and 12.5, +1.5g/dl, which supports this study [12-14].

This study found that CKD stages were statistically significantly associated with low hemoglobin concentrations, which is similar to a North American study that found that CKD stages were statistically significant with anemia. In this study, the presence of anemia was 39(16.4 %), which is lower than studies in North America, which revealed that 55 % of study participants had anemia. [15]. The presence of anemia increases as the stages of CKD rise, the risk of anemia in children with CKD increases as the GFR decreases, anti-hypertensive medications, and increased risk of hospitalization. Another multicentre study in the United States found that the prevalence of anemia was 47.7 %, which is higher than this study. This disparity could be attributed to the large sample size/participation of respondents, the presence of comorbidity disease, and the severity of CKD [12].

The platelet count in this study was significantly higher than in previous studies conducted in the United States, Cameroon, and Libya; these findings revealed that platelet counts were mostly normal [16, 9, 17].The reason for platelets count variation is due to large sample size/respondents involvement, severity, and cause of CKD.

This study found that the leading and common cause of kidney disease in children is congenital abnormalities. On the contrary, the finding in Nigeria revealed primary kidney disease with acute glomerulonephritis, nephrotic syndrome and UTI was an important factor affecting the incidence of kidney dysfunction [19]. This difference could be due to the study being conducted in children hence that they are more susceptible to congenital problems. In addition, detection of AKI only when serum creatinine is elevated or has risen significantly low socioeconomic income, and lifestyle could contribute to this difference. On the contrary, another study conducted in North America revealed that elevated blood pressure was the most common cause of chronic kidney disease than a congenital abnormality. The reason for this variation is due to the absence of anti-hypertensive use, and the shorter duration of kidney

This study found that 5(2.1 %) of CKD patients began renal replacement therapy, which was lower than the study in Sub-Saharan Africa, Cameroon, and Nigeria. This contradicted a descriptive study in Sub-Saharan Africa, which discovered that 17% received renal replacement therapy, and a similar study in Nigeria, which discovered that 22 children required dialysis but only 15 (68.2%) received dialysis [18, 6]. Another similar study in Cameroon found that of 25 patients in renal recovery, 22 were complete, one was partial, and two were dialysis-dependent [9]. The most likely explanation for this finding is that TASH and SPHMMC have one of the few governmental dialysis centres in Ethiopia, which contributes to the higher prevalence of RRT initiation. Another study conducted in Cameroon and Nigeria found that 57.1% and 63.2 % of CKD patients required dialysis [9, 6], respectively, which is higher than the current study. This disparity could be attributed to a lack of adapted dialysis material as well as financial constraints.

Limitation of the study

Due to the corona virus pandemic and a long period of gap during those children with chronic kidney disease on follow-up, the study faced the challenge of obtaining the intended sample size within a given study period because study participants were unwilling to participate.

The study found that the majority of study participants were in stages 1 to 3 based on their eGFR, which is significantly related to RBC, HGB, and HCT count. This study discovered a statistically significant difference in RBCs, Hb, HCT, MCH, MCHC, PLT, WBC, and differential count in children with chronic kidney disease (p<0.05) at SPHMMC and TASH. With p-values of 0.001, 0.007, and 0.001, a positive correlation was found between eGFR and a low range of RBC, HGB, and HCT. The mean WBC count was 8.9± 3.32 (thousand/L) and the mean RBC count was 4.73± 0.86 (million/L), both of which were found to be positively correlated with eGFR with a P-value of 0.001. The mean MPV of children with CKD was 9.03±4.30 (fL), a positively correlated with eGFR with p-value 0.0001 whereas eGFR was negatively correlated with RBC, HGB, and HCT with p-values of 0.010, 0.006, and 0.004, respectively.

AKI Acute kidney injury

BMI Body mass index

CBC Complete blood count

CKD Chronic kidney disease

CRF Chronic renal failure

EDTA Ethylene diamine tetra acetic acid

EPO Erythropoietin

ESR Erythrocyte sedimentation rates

ESRD End-stage renal disease

GFR Glomerular filtration rare

eGFR Estimated glomerular filtration rate

HCT Hematocrit

HGB Hemoglobin

LYM Lymphocyte Count

MCH Mean corpuscular hemoglobin

MCHC Mean corpuscular hemoglobin concentration

MCV Mean corpuscular volume

MPV Mean platelet volume

MUAC Mid upper arm circumference

NEU Neutrophil

PCC Pearson correlation coefficient

PDW Platelets distribution width

P-LCR Platelet larger cell ratio

PLT Platelets

RBC Red blood cell

RDW Red cell distribution width

RDW-SD Red cell distribution width standard deviation

SD Standard deviation

SPHMMC St. Paul’s hospital millennium medical college

SPSS Statistical Package for the Social Science

TASH Tikur Anbessa specialized hospital

UTI Urinary tract infection

WBC White blood cells

WHO World health organization

Acknowledgment

First and foremost, we would like to thank the Almighty God for his endless support in all aspects. We would like to acknowledge Addis Ababa University for giving this opportunity to do this study. We would also to express our appreciation to the Addis Ababa Health Bureau, health offices, study participants, and data collectors. We'd like to thank family and friends for encouraging and supporting.

Ethics approval and consent to participate

First, Department of Medical Laboratory Science, Tikur Anbessa specialized hospital (TASH), and St. Paul Hospital Millennium Medical College (SPHMMC) all approved the proposal ethically. In addition, the Institutional Review Board of College of Health Science, Addis Ababa University was granted ethical clearance for the study. Following explanations of the significance of the study, parents or guardian of children's were provided informed consent. They had the option of not participating in the study, not answering any questions, or withdrawing at any time. They were also informed that the information they provided would be kept strictly confidential. Finally, all methods were performed in accordance with the relevant guidelines and regulations (eg. Declaration of Helsinki).

Declaration of Competing Interest

We, the authors declare that there is no conflict of interest.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due [we do not have consent from all patients and ethical approval committees to publish this data] but are available from the corresponding author on reasonable request.

Funding

Not applicable

Authors’ contributions

MS, FU, RA and BA conducted the literature search, collected and analyzed the data, and wrote the manuscript. MS and FU contributed to the conception and design of the study, the interpretation of data, and critically reviewed the manuscript. MS participated in the writing, analysis and data interpretation, in addition to providing critical input to the research. FU conceptualized and designed the study, supervised the survey team and data analysis, participated in data analysis, contributed to the discussion, reviewed and edited the manuscript, and provided critical data interpretation. All authors read and accepted the final manuscript.

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Table 1: Socio demographic characteristics of children with chronic kidney disease on follow up in St. Paul millennium medical college and Tikur Anbessa Hospital in Addis Ababa (n=238)

Socio-demographic character		Variable		Frequency	Percent
Sex		Male		142	59.7%
		Female		96	40.3%
Age		1-3 years		33	13.9%
		3-5years		29	12.2%
		> 5 years		176	73.9%
Address		Addis Ababa		158	66.4%
		Out of Addis Ababa		80	33.6%
		Primary		152	63.9%
		Secondary		4	1.7%
Duration of stay with chronic kidney disease		<1 year		63	26.5%
		1 – 3 years		77	32.4%
		3-5 years		50	21%
		5-10 years		48	20.2%
	Weight		Height	MAUC		BMI
Mean	24.25		1.24	16.43		15.23
Median	24.00		1.27	16.00		14.70
Std. Deviation	10.978		.257	4.187		2.940

Table 2: Hematologic profile of children with chronic kidney disease on follow up in St. Paul Millennium Medical College and TikurAnbessa Hospital in Addis Ababa (n=238)

Hematologic parameter	Mean	Std. deviation
WBC(thousand/ µL)	8.9264	3.32235
RBC(million/ µL)	4.7305	.86673
HGB(g/dL)	12.8174	2.76429
HCT (%)	38.2839	7.53023
MCV(fL)	80.3181	7.89342
MCH(pg)	27.6937	6.01287
MCHC( g/dL)	32.9298	3.77578
PLT (thousand/ µL)	349.3445	130.17216
MPV(fL)	9.0301	4.30656
MONO (%)	8.3406	6.41159
EOS(%)	2.0072	2.41994
NEUT(%)	50.7218	18.85463
LYM(%)	37.1884	15.78053
EOS(thousand/µL)	.3057	.51134
NEUT ( thousand/µL)	4.5964	8.30979
LYMPH( thousand/ µL)	2.7887	1.62244
	Mean	Std. deviation
BASO (%)	.5000	.87333
MONO(thousand/µL)	.5000	3.02753
BASO(thousand/µL)	.0300	.24169
RDWSD	42.0000	10.03685

Table 3: Bivariate Correlational analysis of Growth indicators and eGFR with other health conditions of children with chronic kidney disease on follow up in St.Paul millennium medical college and TikurAnbessa Hospital in Addis Ababa (n=238))

	eGFR
	Pearson Correlation coefficient	P-value
RBC	.144	.000
HGB	.396	.004
HCT	.218	.001
PLT	-.082	.228
MPV	.153	.0001
	MPV
	Pearson Correlation coefficient	P-value
eGFR	.153	.0001
RBC	-.226	.010
HGB	-.181	.006
HCT	-.218	.004

Table 4: Description of health condition and co-morbidities of children with chronic kidney disease on follow up in St.Paul millennium medical college and TikurAnbessa Hospital in Addis Ababa (n=238))

Variable		Frequency	Percent
Egfr	Stage 1(>90ml/min)	81	34%
Egfr	Stage 2(60-89ml/min)	58	24.4%
	Stage 3(30-59ml/min)	63	26.5%
	Stage 4(15-29ml/min)	9	3.8%
	Stage 5(15ml/min)	9	3.8%
Cause of Kidney disease	Congenital renal abnormalities	128	53.8%
	Hypertension	14	5.9%
	UTI	30	12.6%
	Kidney stones	2	0.8%
	UTI and kidney stones	8	3.4%
	Hypertension and UTI	6	2.5%
	Congenital abnormalities and UTI	24	10.1%
	Congenital abnormalities and HTN	5	2.1%
List of other co-morbidity	CHD(Congenital heart defect)	9	3.8%
List of other co-morbidity	Neurogenic bladder	3	1.3%
	Hydro ureter	2	0.8%
	HTN	2	1.3%
Hypertension	Yes	74	31.1%
Hypertension	No	164	68.9%
Anemia	Yes	39	16.4%
Anemia	NO	198	83.2%
Dialysis	Yes	5	2.1%
	No	233	97.9%

Table 5: Growth indicators based on WHO growth chart of children with chronic kidney disease on follow up in St. Paul millennium medical college and TikurAnbessaHospital in Addis Ababa (n=238)

BMI For Age (5 – 19 years ) (n =176)			MUAC for age for less than 5 years of age (n = 52)
	Freq.	Perc.	Freq.	Perc.
<- 3 SD	19	10.7%	2	3.84%
<- 2 SD	32	18.1%	19	36.5%
< - 1 SD	49	27.8%	11	21.1%
0 SD	47	26.7%	7	13.4%
>1 SD	17	9.65%	9	17.3%
>2 SD	11	6.25%	12	22.9%

Table 6: Bivariate Correlational analysis of Growth indicators and eGFR with other health conditions of children with chronic kidney disease on follow up in St.Paul millennium medical college and TikurAnbessa Hospital in Addis Ababa (n=238)

Variable	Growth indicators
	BMI			MUAC
	P.C.C	P-value		P.C.C	P-value
Longer stay With CKD				.251	.000
Hypertension				.327	.001
Anemia	.169	.009
Using medication for Anemia	.145	.026
Low platelet count				.161	.013
RDW				.149	.022
	eGFR
	P.C.C		P- Value
Hypertension	.197		.003
Anemia	.211		.002
Using medication for anemia	.208		.002
Low range of RBC	.226		.001
Low range of HGB	.181		.007
Low range of HCT	.218		.001

No competing interests reported.

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published 30 Jan, 2024

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Assessment of the hematological profile of children with chronic kidney disease on follow-up at St. Paul’s Hospital Millennium Medical College and Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia

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