Time to Death and Its Predictors Among Patients With Chronic Kidney Disease on Hemodialysis at Dialysis Unit in Addis Ababa, Ethiopia: a Retrospective Cohort Study

doi:10.21203/rs.3.rs-3803778/v1

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Research Article

Time to Death and Its Predictors Among Patients With Chronic Kidney Disease on Hemodialysis at Dialysis Unit in Addis Ababa, Ethiopia: a Retrospective Cohort Study

https://doi.org/10.21203/rs.3.rs-3803778/v1

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Background

Over 10% of the world's population suffers from chronic kidney disease, which is the primary cause of death in the twenty-first century and it progresses over time. Furthermore, it places a heavy financial strain on those receiving hemodialysis. The time to death and its predictors among hemodialysis patients in Ethiopia, have not been extensively studied. As a result, improving survival rates and improving the prognosis of hemodialysis patients requires an understanding of time to death and the predictors that influence it.

Methods

Institution-based retrospective cohort study was carried out among 370 chronic kidney disease patients on hemodialysis from January 1st, 2017 to December 30th, 2022. Data were extracted from April 1st -May 20th, 2023, and each variable was coded and entered into Epi Data version 3.1 and then exported into STATA version 15 software for analysis. Kaplan-Meier and the log-rank test was done. Bivariable Cox-proportional regression was done, and a variables whose p-value < 0.25 and fulfilled proportional hazard assumption were entered into multivariable Cox-proportional regression. Finally, a variable whose p-value < 0.05 and Adjusted hazard ratio with its CI was declared statistically significant predictors for time to death.

Result

In this study the overall median survival time was 47 month (95% CI: 36.7, 56), with incidence rate of death 16.8/1000 person-month of observation (95% CI: 13.8–20.3). Age 64 and above (Adjusted Hazard Ration: 2.8; 95% CI: 1.67–4.98), Catheter vascular access (Adjusted Hazard Ration 3.47; 95% CI: 2.03–5.93), Cardiovascular disease (Adjusted Hazard Ration 1.88; 95% CI: 1.15–3.07), blood group B (Adjusted Hazard Ration 2.07; 95% CI: 1.17– 3.69) were significant predictors of time to death among hemodialysis patients.

Conclusion and recommendation:

The incidence rate of death was 16.8/1000 person-months of observation, and the median survival time was 47 months with an Interquartile range of 40. Cardiovascular disease, older adults, central venous catheters, and blood type B were significant predictors of time to death for hemodialysis patients. Therefore, in order to improve the survival of hemodialysis patients, health professionals should pay attention and work on those predictors.

Urology & Nephrology

Hemodialysis

chronic kidney disease

Time to death

cox-regression

Chronic kidney disease (CKD) refers to reduced kidney function that lasts for at least three month and a glomerular filtration rate (GFR) of less than 60 mL/min per 1.73 m², as well as the existence of sign of kidney damage there is also a gradual loss of kidney function, this results in a decreased kidney's ability to process waste and also affects other important functions of the kidney (1, 2)

According to the global burden of illness, chronic renal disease is the 12th most common cause of mortality and is responsible for 1.1 million deaths globally (3). Additionally, it was placed 27th in the list of factors contributing to all deaths globally in 1990 and rose to 18th in 2010 (4). Besides, it is expected to be the fifth-leading cause of death by 2040 around the globe (5).

The burden of chronic kidney disease on the continent of Africa accounted for 15.8% from this 4.6% had moderate or severe declines in renal function (6). In Ethiopia, the pooled prevalence of chronic kidney disease among patients with chronic illnesses is 21.71% particularly due to increased risk factors such as hypertension and diabetes mellitus (7, 8).

In chronic kidney disease, there is a problem with the kidney filtering out and performing its activity, due to this, dialysis can be used, whether in the form of hemodialysis or peritoneal dialysis (9). Approximately 2 million patients worldwide undergo regular renal replacement therapy in the form of dialysis to maintain their lives, with around 80% receiving hemodialysis and 15% receiving peritoneal dialysis (10).

Even if dialysis is used as a treatment modality in Ethiopia, access to it is quite limited and unaffordable to low-socioeconomic societies (11). Only a few studies have been done on the survival status and predictors of death among CKD patients receiving hemodialysis, despite the fact that there have been several prevalence studies on CKD that show the expanding burden of the disease (11–15). Study conducted in Amhara found that in a 5-year follow-up, the median survival time was 345 days and the incidence of death was 1.89 per 1000 person-days. Rural residence, hypertension, human immune virus, and use of a central venous catheter were significant predictors that affected the survival of hemodialysis patients. Aside from this, mortality for the first 3 months of starting dialysis was very high (14).

As far as our concerned, there is only a limited amount of information available about time to death and its predictors in our country. Additionally, evidence from previous studies indicated that there is an increasing number of hemodialysis patients. That's why studying about time to death and its predictors is crucial in order to increasing the survival time of hemodialysis patients. Furthermore, most studies are confined to a single dialysis unit, but this study tried to include both public and private dialysis units and also attempted to include some predictors which had not been addressed from previous study. This study aimed to investigate the time to death and its predictors among hemodialysis patients in selected dialysis centers in Addis Ababa, Ethiopia.

The study was conducted in dialysis unit in Addis Ababa, the capital of Ethiopia. The total population Addis Ababa has 3,273,000 from this 1,722,000 female and 1,551,000 male (16) There are 35 hemodialysis units found in Ethiopia from those only eleven units are government-run hospitals the rest are privately owned (17). The study was carried out from April 1st -May 20, 2023 in Addis Abebe, Ethiopia. An institution-based retrospective cohort study design was used.

Those adult patients who had hemodialysis for chronic kidney disease at randomly selected dialysis units in Addis Ababa during the period from January 1st, 2017 to December 30, 2022 and aged 18 and above were included in the study whereas patients who received hemodialysis for acute renal failure and were transferred from other dialysis facilities or whose time from the start of hemodialysis to the end of hemodialysis was shorter than 30 days and lost medical records were excluded from the study. The outcome variable was Time to death whereas the independent variable were Socio-demographic factors, Clinical characteristics of the patient, Biochemical parameters and Treatment-related factors.

Sample size determination and sampling procedures

The minimum required sample size was calculated by using a survival analysis formula. Using STATA™ Version 15 statistical software to calculate the sample size by considering the following assumptions: the level of significance (α) = 5%, Zα /2 (value at 95% confidence interval = 1.96), power of 80%, and hazard ratio (0.5314) and probability of death 0.2536 (13). The calculated sample size was, n = 310, and after adding a 10% for incomplete records the final sample size will be about n = 341 whereas all patients who fulfilled the inclusion criteria was 370 were used, by using census sampling from five hospital who give dialysis service.

Operational definitions

Time to death

Length of time it takes for hemodialysis patients to develop an event of interest measured in days from start of hemodialysis to the date of death.

Event

Death of patients due to chronic kidney disease on hemodialysis.

Censored

Those patients who didn’t develop the outcome of interest at the end of the follow-up period or were alive; loss of follow-up; transfer to another dialysis center; or kidney transplant.

Charlson comorbidity index

An index consist 17 medical condition weighted 0–6 and weighted as mild, moderate and severe based on number of comorbidity where 1–2, 3–4 & ≥5 respectively (18).

Data collection tool and techniques

The data were collected by using a data extraction checklist that was adapted from different studies (14, 15, 19–21).Which consist of socio-demographic-related factors, clinical characteristics of the patient, biochemical parameters, and treatment-related factors. Data were gathered by reviewing patients’ medical records, hemodialysis charts, and registration book. A checklist with an English version was used for data collection to facilitate the extraction of data. Ten BSc nurses (two in every hospital) were involved in the data collection.

Data Quality Control and Management

Data collectors and supervisors received one day of training on the data gathering procedure regarding to objective of the study, and the study population’s eligibility criteria. The investigator and supervisors daily checked the obtained data for consistency, and completeness to minimize the chance of loss of data and to ensure data quality.

Data processing and analysis

After the data was collected, each variable was coded and entered into Epi Data version 3.1 and then exported into STATA version 15 software for analysis. In order to handle missing values for the variables, the imputation method was used. Kaplan-Meier and the log-rank test were done. Bivariable Cox-proportional regression was done, and a variable whose p-value < 0.25 entered into multivariable Cox-proportional regression analysis. In order to check the proportional hazard assumption Shenfield residual test was used, which had a P-value > 0.05 was considered as fulfill the assumptions. And multicollinearity was done by using variance inflation factor a mean VIF was 1.18. Finally, a variable whose p-value < 0.05 in multivariable Cox-proportional regression analysis was considered a statistically significant predictors for time to death among patients on hemodialysis. Overall model fitness was also checked by the Cox-Snell residual. Finally, the result was presented through tables, text, and graphs.

Socio demographical characteristics of study participant

A total of 370 hemodialysis patient records were included in the study, with the mean age of the participants 46.38 ± 15.72 years. Two hundred thirty three (62.97%) participants were males, and 137(37.03%) were females and majority of patients were residents of Addis Ababa which is 319 (86.45%).

Table 1

Socio demographical characteristics of study participant of chronic kidney diseases patients on hemodialysis at selected hospitals giving dialysis Service, Addis Ababa, Ethiopia, 2023.
Variables & There Categories	Death (n = 105) Frequency (%)	Censored (n = 265) Frequency (%)	Total (n = 370) Frequency (%)
Sex
Male	72 (68.6)	161 (60.75)	233 (62.97)
Female	33 (31.4)	104 (39.24)	137(37.03)
Age (in years)
18–47	36 (18.4)	160 (81.6)	196(52.9)
48–63	39 (34.5)	74 (67.9)	113 (30.5)
≥64	30(49.2)	31 (47)	61 (16.5)
Residence
Addis Ababa	93 (29.2)	226 (70.8)	319 (86.45)
Outside Addis Ababa	12 (24)	38 (76)	50 (13.55)

Clinical characteristics of the study participant

From a total of 370 patients, 313 (84.6%) had comorbidities, and from the comorbidities, the leading one was hypertension 208 (56.2%). The most common potential causes of chronic kidney disease were hypertension 113 (30.54%), diabetes mellitus 108 (29.19%), and both hypertension and diabetes mellitus 64 (17.3%), whereas 48 (12.97%) of patients had chronic kidney disease for which the etiology was unknown.

Table 2

Clinical characteristics of the study participant of chronic kidney diseases patients on hemodialysis at selected hospitals giving dialysis Service, Addis Ababa, Ethiopia, 2023.
Variables & There Categories	Death (105) frequency (%)	Censored (n = 265) frequency (%)	Total (n = 370) frequency (%)
Comorbidity
Yes	93 (29.7)	220 (70.3)	313 (84.6)
No	12 (21.1)	45 (78.9)	57 (15.4)
DM
Yes	58 (29.4)	139 (70.6)	197 (53.2)
No	47 (27.2)	126 (72.8)	173 (46.8)
HTN
Yes	67(32.2)	141(67.8)	208 (56.2)
No	38 (23.4)	124 (76.54)	162 (43.8)
CVD
Yes	25 (49.1)	26 (50.9)	51 (13.8)
No	80 (25.1)	239 (74.9)	319 (86.2)
HIV
Yes	12 (44.4)	15 (55.6)	27 (7.3)
No	93 (27.1)	250 (72.9)	343 (92.7)
Potential cause of CKD
DM	29 (26.9)	79 (73.1)	108 (29.19)
HTN	30 (26.5)	83 (73.5)	113 (30.54)
DM + HTN	23 (35.9)	41 (64.1)	64 (17.3)
Tubular interstitial nephritis	3 (30)	7 (70)	10 (2.7)
Cystic disease	4 (28.6)	10 (71.4)	14 (3.78)
Glomerulonephritis	5 (38.5)	8 (61.5)	13 (3.52)
Unknown	11 (22.9)	37 (77.1%)	48 (12.97)
Family History of CKD
Yes	22 (36.7)	38 (63.3)	60 (16.2)
No	83 (26.8)	227 (73.2)	310 (83.8)
Charlson comorbidity index (CCI)
1–2	23 (23)	77 (77)	100 (27.03)
3–4	46 (24.6)	141(75.4)	187 (50.54)
≥5	36 (43.4)	47 (56.6)	83 (22.43)
DM = Diabetes mellitus HTN = Hypertension CVD = Cardiovascular disease

Treatment related and biochemical parameters of study participant

The majority of the patients used arteriovenous fistulas for vascular access, which is 224 (60.54%), and most of the patients hemoglobin levels were < 11 mg/dl 215 (58.1%). About 123 (33.24%) patients undergo hemodialysis three times a weeks, whereas 137 (37.03%) and 110 (29.73%) used twice and once per week, respectively. Regarding blood groups, 124 (33.51%) were blood groups O, whereas, 82 (22.16%), 110 (29.73%), and 54 (14.6%) were blood groups A, B and AB, respectively.

Table 3

Treatment related and biochemical parameters of chronic kidney diseases patients on hemodialysis at selected hospitals giving dialysis Service, Addis Ababa, Ethiopia, 2023.
Variables	Categories	Death (n = 105) frequency (%)	Censored (n = 265) frequency (%)	Total (n = 370) frequency %
Infection	Yes	47 (44.8)	58 (55.2)	105 (28.4)
Infection	No	58 (21.9)	207 (78.1)	265 (71.6)
Vascular access	AVF	56 (25)	168 (75)	224 (60.54)
	AVG	21 (33.3)	42 (66.7)	63 (17.03)
	JVC	28 (33.7)	55 (66.3)	83 (22.43)
Dialysis duration per/hr.	3hr	63 (31.1)	140 (68.9)	203 (54.9)
	3.5 hr.	36 (26.1)	102 (73.9)	138 (37.3)
	4 hr.	6 (20.7)	23 (79.3)	29 (7.8)
Dialysis frequency per/wk.	1times	23 (20.9)	87 (79.1)	110 (29.73)
	2times	41 (29.9)	96 (70.1)	137 (37.03)
	3 times	41 (33.3)	82 (66.7)	123 (33.24)
Blood transfusion	Yes	21 (21.2)	78 (78.8)	99 (26.8)
Blood transfusion	No	84 (31)	187 (69)	271 (73.2)
Erythropoietin treatment	Yes	36 (27.3)	96 (72.7)	132 (35.7)
Erythropoietin treatment	No	69 (29)	169 (71)	238 (64.3)
Blood type	A B AB O	28 (34.1) 47(42.7) 15 (27.8) 19 (15.3)	54 (65.9) 67 (60.9) 39 (72.2) 105 (84.7)	82 (22.16) 110 (29.73) 54 (14.6) 124 (33.51)
Adequacy of dialysis	< 1.2	18 (45)	22 (55)	40 (10.8)
Adequacy of dialysis	≥ 1.2	87 (26.4)	243 (73.6)	330 (89.2)
Health facility	Private	79 (27.2)	211(72.8)	290 (78.4)
Health facility	Public	26 (32.5)	54(67.5)	80 (21.6)
Hemoglobin g/dl	< 11	68(31.6)	147 (68.4)	215 (58.1)
	11-12.5	32(25.8)	92 (74.2)	124 (33.5)
	> 12.5	5(16.1)	26 (83.9)	31 (8.4)
Creatinine mg/dl	< 8	47 (26.1)	133 (73.9)	180 (48.65)
Creatinine mg/dl	≥ 8	58 (30.5)	132 (69.5)	190 (51.35)
AVF = Arteriovenous Fistula AVG = Arteriovenous Grafts JVC = Jugular venous catheter

Time to death of chronic kidney disease patients on hemodialysis

Of the total of 370 patients who were on HD for at least one month, were followed for a total of 60 months, with a minimum and maximum follow-up time of 1 and 59 month, respectively. From the total of 370 eligible patients, 265 (71.6%) were censored and 105 (28.4%) (95% CI: 23.8–33.3) died. The study participants were followed for a total of 6245 person-month, with an incidence rate of death 16.8/1000 person-month of observation (95% CI: 13.8–20.3).The median time to survival was 47 months (95% CI: 36.7, 56) (Fig. 1). The probability of survival at 3 month, 1st, 3rd, and end of follow-up years was 92.76%, 80.1%, 58.14%, and 24.95%, respectively. The Kaplan-Meier curve with log-rank of p-value shows that differences in survival between the categories of variables: vascular access (Fig. 2) and cardiovascular disease (Fig. 3).

There is survival difference b/n a vascular access with log rank test at p value of 0.0000 and chi² of 41.89.

It indicated that there is a difference of survival between those who had cardiovascular disease than of without cardiovascular disease at p-value of 0.0000.

Table 4

Log-rank test of survival time difference among patients disease of chronic kidney diseases patients on hemodialysis at selected hospitals giving dialysis Service, Addis Ababa, Ethiopia, 2023.
Variables	Category	Median survival time	X²	P-value
Infection	yes no	27 56	14.64	0.0001
Blood type	A B AB O	30 30 42 56	19.73	0.0002
Adequacy	< 1.2 ≥ 1.2	21 47	14.03	0.0002
Sex	Male Female	42 47	3.68	0.05
Hypertension	yes no	37 54	4.65	0.03
Existence of comorbidity	yes no	42 54	1.59	0.2
Hemoglobin	< 11 11-12.5 ≥ 12.5	42 42 47	5.14	0.07
Health facilities	private public	47 37	1.3	0.25
Residence	Addis Ababa Outside Addis Ababa	47 27	0.96	0.33

Table 5

Proportional hazard assumption of the variables included in multivariable analysis.
Variables	Rho	chi²	Df	Prob > chi²
Hemoglobin
< 11mg/dl			1
11-12.5 mg/dl	0.04982	0.29	1	0.5899
≥ 12.5 mg/dl	0.00767	0.01	1	0.9371
CCI
1–2			1
3–4	0.02162	0.05	1	0.8248
≥ 5	0.11264	1.27	1	0.2602
Adequacy of dialysis
< 1.2			1
≥ 1.2	0.12519	2.01	1	0.1568
Blood type
A	-0.05977	0.36	1	0.5488
B	-0.07341	0.60	1	0.4381
AB	0.02089	0.05	1	0.8313
O			1
Age
18–47			1
48–63	0.06856	0.65	1	0.4200
≥ 63	0.04945	0.33	1	0.5664
Hypertension
Yes			1
No	0.09051	0.88	1	0.3477
Cardiovascular disease
Yes	0.10361	1.32	1	0.2513
No			1
Existence of comorbidity
Yes	-0.09309	0.84	1	0.3595
No			1
Vascular access
AVF			1
AVG	0.13259	2.12	1	0.1457
CVC	0.00497	0.00	1	0.9560
Infection
Yes	0.11485	1.70	1	0.1919
No			1
Sex
Male			1
Female	0.03368	0.14	1	0.7079
Global test	11.85		17	0.8092

Predicators of time to death among chronic kidney disease on hemodialysis

At a p-value of < 0.25 in bivariable Cox regression analysis, eleven variables were selected, those were sex, age, infection, hypertension, vascular access, CVD, blood type, hemoglobin level, adequacy of dialysis, existence of comorbidity, and the Charlson comorbidity index. The global goodness-of-fit test of Shenfield residuals was used to verify the proportional hazard assumption. Which had a global test p-value of 0.8092 > 0.05 which satisfy PH assumption.

Those eleven variables from bivariable cox regression entered to multivariable cox regression. Among the eleven variables that were entered into cox multivariable cox regression analysis, four variables became statistically significant at a p value of < 0.05, those are central venous catheter, older age, CVD, and blood type B.

Those patients who used central venous catheter for vascular access were 3.47 times risk of death than those with AVF. Those patients who were aged 64 and older were 2.8 times more likely to die than those in the age group of 18–47 years. The risk of death in those hemodialysis patients whose blood group B has 2.07 times than blood type O Those patients with cardiovascular disease were 1.88 times more likely to die than those patients without cardiovascular disease.

Table 6

Cox proportional hazard model analysis for predictors of time to death among chronic kidney diseases patients on hemodialysis at selected hospitals giving dialysis service, Addis Ababa, Ethiopia, 2023.
Variables	categories	Death (n = 105)	Censored (n = 265)	CHR (95%CI)	AHR (95%CI)	P-VALUE
Age (years)	18–47	36	160	1	1
	48–63	39	74	2.03(1.28–3.2)*	1.5(0.91–2.48)	0.11
	≥ 64	30	31	4.9(2.98–8.06)*	2.8(1.7–4.98)***	0.001
Sex	Male	72	161	1	1
Sex	Female	33	104	0.67(0.44–1.01)*	0.90(0.57–1.42)	0.66
Infection	Yes	47	58	2.08 (1.4–3.1)*	1.44(0.94–2.21)	0.09
Infection	No	58	207	1	1
Vascular access	AVF	56	168	1	1
	AVG	21	42	1.4 (0.89–2.47)*	1.47 (0.87–2.48)	0.14
	CVC	28	55	4.5 (2.8–7.4)*	3.47(2.1–5.9)***	< 0.001
Existence of comorbidity	Yes	93	220	1.46(0.80–2.68)*	1.16 (0.52–2.57)	0.70
Existence of comorbidity	No	112	45	1	1
Charlson comorbidity index	1–2	23	77	1	1
	3–4	46	141	1.09(0.66–1.81)	0.93(0.50–1.3)	0.82
	≥ 5	36	47	2.02(1.19–3.41)*	1.52 (0.82–2.82)	0.17
Cardiovascular disease	Yes	25	26	2.64(1.68–4.15)*	1.88(1.15–3.07)*	0.01
Cardiovascular disease	No	80	239	1	1
Hypertension	Yes	67	141	1.54(1.03–2.30)*	1.29 (0.80–2.03)	0.28
Hypertension	No	38	124	1	1
Blood type	A	28	54	2.3 (1.28–4.12)*	1.68 (0.91–3.07)	0.09
	B	47	67	3.15(1.83–5.41)*	2.07(1.17–3.69)**	0.01
	AB	15	39	1.72(0.87–3.39)*	1.98 (0.98-4)	0.05
	O	19	105	1	1
Adequacy of dialysis	< 1.2	18	22	1	1
Adequacy of dialysis	≥ 1.2	87	243	0.39(0.23–0.65)*	0.58 (0.33–1.03)	0.06
Hemoglobin	< 11	68	147	1	1
	11-12.5	32	92	0.78(0.51–1.2)	0.99 (0.63–1.56)	0.98
	≥ 12.5	5	26	0.38(0.15–0.95)*	0.4 (0.16–1.19)	0.10
Note: (CHR = Crude hazard ratio, AHR = Adjusted hazard ratio, CI = Confidence interval, ¹Reference) Predictors at P-value < 0.05; Predictors at P-value < 0.01; *Predictors at P-value < 0.001. For Bivariable predicators at p < 0.25

It indicated that the residuals followed an exponential distribution along the 45-degree slope of the fitted line.

The objective of this study was to assess time to death and its predictors among patients with chronic kidney disease on hemodialysis at selected hospitals giving dialysis service in Addis Ababa, Ethiopia, 2023. The incidence rate of death in this study was 16.8 per1000 person-month. which is consistent with study conducted in Tigray 1.4 per 1000 person year (15). However lower than study conducted in Amhara 1.89 per 1000 person-days (14). Which might be due to a variation in study setting and sociodemographic characteristics of the patients.

The median survival time in this study was 47 months, which is higher than the study conducted in Ayder Hospital in Tigray region, which was 378 days (15).This might be a result of the fact that the majority of the patients in Tigray were older, and it was carried out over a three-year period in a single public dialysis facility.

Those patients who were aged ≥ 64 years were 2.8 times more likely to die than of young adults aged 18–47 years. This finding is supported by other previous studies conducted in Vietnam (22), Iran (23), and Adama (12). This might be due to the fact that older age is associated with lower levels of physical functioning (24); as a result, it increase the risk of different comorbidities and makes them more susceptible to hemodialysis instability. However, there was no significant association between age and mortality of hemodialysis patients in study conducted in Tigray (15). Thus, the difference may be due to disparities among the study setting and study period.

Patients using central venous catheters have a 3.47 times higher risk of death than patients using an arteriovenous fistula as their vascular access. The result was supported by studies done in Taiwan (21), Palestine (25), Japan (26) and Amhara (14). This could be because the central venous catheter had a high risk of infection, thrombosis, and mechanical stress, which caused vein stenosis, which lowers blood flow and lowers the required dose of dialysis and, as a result decreased patient survival (27). However this finding not supported by a study conducted in china (20). This difference could be due to low usage of central veins catheters in a study conducted in China.

Hemodialysis patients with blood group B had twofold higher risk of death than those with blood group O. The result of this study is supported by a study conducted in china (28).This might be due to the fact that non-O blood groups had a great probability of thrombosis due to increased amounts of von Willebrand factor (VWF) and factor VIII (FVIII). As a result they are more susceptible to thromboembolic disorders, which affect patency and longevity of the vascular system (29). However there was no significant association in study conducted in Iran (23).

Patients with cardiovascular disease had increased risk of death by 88% compared to those without cardiovascular. This finding is in line with the studies carried out in Casablanca (30) and China (31). This may be due to the high risk of fluid overload due to loss of renal function as well as due to dialysis treatment, which increases the risk of the pathophysiological processes of hypertension and left ventricular hypertrophy it worsens the progression of CVD (32).

In this study, there is no survival difference between public and private dialysis units; thus, this study is inconsistent with a study conducted in Romania in which those private dialysis units had a better outcome than governmental (non-profit) hemodialysis (33) .This difference might be due to the fact that dialysis treatment protocols and practices may be standardized across public and private units; that’s why they provide consistent care regardless of the type of facility.

Strengths of the study and Limitations of the study

This study collected a sample from multiple dialysis units and collaborated with both private and public dialysis units to make the study more generalizable. Additionally, it attempted to incorporate some predictors that were not addressed in previous studies. Furthermore, due to the secondary sources used to gather the data, some important variables such as albumin level, calcium level, and potassium level were overlooked. This could be one of the limitations of the study.

Overall, this study showed that the incidence rate of death was 16.8 per 1000 people per month, with a median survival time of 47 months. When we compare it to the previous study, it is quite good, but it needs further work in order to decrease the incidence rate of death and increase the median survival time of the patients until they get a transplant. When we come to the predictors of time to death, cardiovascular disease, older adults (aged ≥ 64), catheter vascular access, and blood type B were significant predictors that affected the survival of hemodialysis patients. Therefore, it’s better to intervene for those predictors that affect patient survival time and create interventions accordingly in order to increase survival time for hemodialysis patients.

Acknowledgment

First and foremost, my deepest gratitude goes to St. Paul’s Hospital, Addis Hiwot General Hospital, EthioTebib General Hospital, Zewditu Memorial Hospital, and Bethzatha General Hospital dialysis unit health professionals for providing the information I needed. I also want to thank the data collectors for their cooperation and commitment throughout the data collection period, and I would also like to thank Assosa University for financially sponsoring the study.

Funding

The main source of funds for the study is Assosa University

Contributions

Azeb Haile was involved in conceptualization, design, analysis, interpretation, and report and manuscript writing; Adamu Birhanu and Tufa Kolola were involved in the design, analysis, interpretation, report writing, and review of design, interpretation, and report writing; Diriba Etana and Lenja Hamza contributed to the design and review of design, analysis, interpretation, and report writing. All authors read and approved the final manuscript.

Ethical and Legal Consideration

An ethical clearance letter was obtained from the Ethical Review Board (ERB) of Ambo University, College of Health Sciences, and Referral Hospital, Ref. No.: AU/PGC/620/2023. Official letters were submitted to those selected hospitals, and ethical clearance letters were also obtained from the concerned bodies of those hospitals and submitted to the respective dialysis units. Additionally, to maintain the confidentiality of the study participant, their name and any personal identifier were not included in the collected data; instead, codes were used. The confidentiality of the information was also assured and maintained by preventing the disclosure of the information to third parties.

Consent for publication

Not applicable

Competing interest

The authors declare that they have no competing interests.

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The authors declare no competing interests.

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Time to Death and Its Predictors Among Patients With Chronic Kidney Disease on Hemodialysis at Dialysis Unit in Addis Ababa, Ethiopia: a Retrospective Cohort Study

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Abstract

Background

Methods

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Introduction

Methods and Materials

Sample size determination and sampling procedures

Operational definitions

Data collection tool and techniques

Data Quality Control and Management

Data processing and analysis

Result

Socio demographical characteristics of study participant

Clinical characteristics of the study participant

Treatment related and biochemical parameters of study participant

Time to death of chronic kidney disease patients on hemodialysis

Predicators of time to death among chronic kidney disease on hemodialysis

Discussion

Strengths of the study and Limitations of the study

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