Abstract

Introduction: Chronic Kidney Disease mortality is still increasing every year. Hemodialysis is the basis for chronic end-stage renal disease management in Benin.

Purpose: To study the survival of hemodialysis patients at the Departmental Teaching Hospital of Borgou and Alibori (CHUD-B/A) from 2016 to 2021.

Methods: We performed a cohort study with retrospective data collection on 104 hemodialysis patients during the study period. Data was recorded on the KoBo Toolbox platform and analyzed with SPSS 26 software. Survival was performed using the Kaplan-Meier method.

Results: The median survival time for hemodialysis patients was 3 months with a mean survival time of 7.4 ± 1.3 months. The overall mortality rate was 78.9%. Predictors of mortality in hemodialysis patients were occupational status (p=0.027), underlying kidney injury (p=0.010), associated cirrhosis (p=0.019), type of vascular access (p=0.006), and duration of hemodialysis sessions (p=0.044).

Conclusion: Survival of hemodialysis patients in CHUD-B/A is low. Efforts to prevent cardiovascular risk factors minimize infectious complications and free dialysis could improve the survival of hemodialysis patients.

Keywords

Hemodialysis; Chronic Kidney Disease; Mortality; Survival; Benin

Introduction

Chronic Kidney Disease (CKD) is the eleventh leading cause of death worldwide, and poses numerous management challenges, particularly in low- and middle-income countries [1]. The incidence of Chronic Kidney Disease (CKD) has been rising steadily for decades. Worldwide, one in ten adults suffers from CKD. It is part of a new disease epidemic that replaced malnutrition and infections as the leading causes of death in the twentieth century [2]. Worldwide, one in ten adults suffers from CKD. Indeed, changes in diet and lifestyles have favored the emergence of pathologies such as diabetes mellitus and hypertension, which in our context are complicated for most CKD patients by the lack of early detection and medical follow-up [3]. In addition, inadequately treated infectious diseases, self-medication favored by the increased availability of street drugs, and urological pathologies such as prostatic hypertrophy and kidney stones are other no less important causes of CKD in our context [4]. The severity of the disease lies mainly in the availability and cost of treatment.

Unlike in high-income countries, inaccessibility to replacement therapy remains the major difficulty in developing countries. Hemodialysis is one of the therapeutic options available to reduce mortality in patients with end-stage CKD. Extrarenal purification by hemodialysis is the mainstay of the management of end-stage chronic kidney disease in Benin.

Hemodialysis is one of the therapeutic options available to reduce mortality in patients with end-stage CKD. Hemodialysis should be a survival option for patients with chronic end-stage renal disease, but several factors compromise this survival. This work was initiated to study the survival of hemodialysis patients at the Departmental Teaching Hospital of Borgou and Alibori (CHUD-B/A) from 2016 to 2021 and study the factors associated with survival.

Methods

Nature, scope and study period

A cohort study with retrospective data collection was carried out in the Hemodialysis Unit of the Nephrology Service of the Departmental Teaching Hospital of Borgou and Alibori, between January 1, 2016, and December 31, 2021.

Sampling, parameters of interest and operational definitions

Sampling was exhaustive, with consecutive recruitment of patients. All patients whose medical records provided clear information on patient identity, general information on admission, clinical examination data, paraclinical data, management modalities and evolution modalities, and who had started their first supportive therapy between January 1, 2016, and December 31, 2021. Patients with incomplete or unusable records were excluded. The dependent variable was the survival of patients with the modality alive/ deceased. Parameters of interest included: demographic data (age, sex, occupation, marital status, level of education, place of residence, socioeconomic level, availability/non-availability of public health insurance), behavioral data (consumption of alcohol, herbal tea, self-medication, and exposure to tobacco), clinical data (medical history and comorbidities), paraclinical and therapeutic data, and developmental modalities (survival on hemodialysis, probable causes of death).

Statistical analysis

Data were collected from the individual medical records of patients diagnosed with CKD and placed on hemodialysis. Once we identified the patient records that met the above criteria, we proceeded with data collection. Quality control of the data processing forms was performed as the data were processed and collected. Data entry was done using the KoBoToolbox platform.

At the end of data collection, the data were coded and analyzed using SPSS software version 26 (April 2019) from the International Business Machine Corporation (IBM). The Kolmogorov-Smirnov test was used to verify the normality of the distribution of quantitative variables. They were expressed as the mean with its standard deviation if they followed a normal distribution; otherwise the median followed by the percentile was used. Qualitative variables were expressed as proportions. The chi-square test was used to compare qualitative variables, and the Student or Mann-Whitney test was used to compare quantitative variables, depending on the normality of the distribution. The survival distribution was described by the Kaplan-Meier estimator [5]. In bivariate analysis, the log-rank test was used to compare two survival curves. Variables were then included in the multivariate Cox model [6] if their p-value was less than 0.20 in the bivariate analysis. The association between explanatory variable and survival was measured by the Hazard Ratio (HR). A multivariate analysis was performed to eliminate confounding factors between measures of association using logistic regression with SPSS version 20 software. The significance threshold was 5%.

Ethical and Deontological Considerations

Confidentiality and ethical rules were followed according to the Helsinki protocol.

Results

During this period, 174 hemodialysis patients were identified, of whom 127 met the inclusion criteria, for a participation rate of 72.9%. We excluded 13 patients whose records could not be used. A total of 104 files were eligible for inclusion in the study. These are shown in figure 1.

Figure 1: Flow chart of patients included in the study.

General characteristics of the population

Of the 104 patients included in the study, 70 (67.3%) were male, resulting in a sex ratio of 2.06. The mean age was 42.9 ± 12.6 years [extremes 18 and 72 years]. The predominant socioeconomic category was civil servants (29.8%), and 57.7% of patients lived in urban areas. Uneducated patients were most common (43.8%), 63.5% had a low socioeconomic level, and 76.9% had public health insurance.

Alcohol consumption and tobacco use were found in 16.4% and 9.6%, respectively. Medical history of hypertension and diabetes mellitustype2were found in 78.9% and 13.5% of hemodialysis patients, respectively.

On admission, 51.9% and 45.2% had edema and asthenia, respectively. Among the enrolled patients, 74.0% had edema of the lower limbs. 9.9 mmHg. Blood pressure was elevated in 94 patients (90.4%). Physical examination on admission was unremarkable in 29.6% of patients. Crepitus (18.3%), signs of right heart failure (16.7%) and left heart failure (11.3%) were the most common physical findings.

The mean predialysis uremia was 2.2 ± 1.03 g/dL and the mean serum creatinine was 201.0 ± 86.3 mg/dL. Hyponatremia (55.8%) and hyperkalemia (96.2%) were the most common blood ionogram abnormalities. Hypocalcemia was present in 75.0% of cases. Anemia (hemoglobin less than 10g/L) was present in 98 patients (94.2%). It was normochromic (55.8%), normocytic (40.38%) and microcytic (49.04%). Hyperleukocytosis was observed in 36.54% of the cases.

The main cause of end-stage renal disease in patients was vascular nephropathy (61.5%), followed by chronic tubulointerstitial nephropathy (11.5%). Diabetic nephropathy and other chronic glomerular nephropathies accounted for 9.6% and 11.5% respectively. Other characteristics are described in table 1.

	Frequency	Proportion (%)
Age group (completed years)
[18-33]	23	22.1
[33-48]	46	44.2
[48-63]	27	26
≥ 63	8	7.7
Sex
Male	70	67.3
Female	34	32.7
Educational level
Not in school	45	43.3
Primary	22	21.5
Secondary	20	19.2
University	17	16.4
Socio-professional status
Student	5	4.8
Official	31	29.8
Craftsman/worker	21	20.2
Farmer/breeder	7	6.7
Merchant/Reseller	18	17.3
Housewife	11	10.6
Motorcycle/car/truck driver	11	10.6
Residential area
Urban	60	57.7
Rural	44	42.3
Support mode
State health coverage	80	76.9
By the patient himself	24	23.1
Lifestyle
The practice of herbal medicine	78	75
Practice of self-medication	69	66.4
Alcohol consumption	17	16.4
Exposure to tobacco	10	9.6
None	10	9.6
Personal medical history
High blood pressure	81	77.9
Diabetes mellitus	14	13.5
HIV infection	5	4.8
Preeclampsia (female)	5	4.8
None	14	13.5
Blood pressure
Optimal	3	2.9
Normal	7	6.7
Hypertension	94	90.4
Natremia (mEq/L)
<135	58	55.8
[135-148]	45	43.2
>148	1	1
Serum potassium (mEq/L)
<3.5	6	5.8
[3.5-5.5]	69	66.3
>5.5	29	27.9
Hemoglobin (g/ dL )
<10	98	94.2
≥ 10	6	5.8
Anatomoclinical diagnosis
Chronic vascular nephropathy	64	61.5
Diabetic nephropathy	10	9.6
Other chronic glomerular nephropathy	12	11.6
Chronic tubulointerstitial nephropathy	12	11.6
Hereditary nephropathy (polycystic kidney disease)	2	1.9
Indeterminate	4	3.8

Table 1: Description of socio-demographic, clinical, and paraclinical characteristics of hemodialysis patients at CHUD-B/A between 2016 and 2021 (N=104).

Characteristics of hemodialysis treatment

In our series, 83 patients (79.8%) had no clinical nephrology followup before initiation of renal replacement therapy. Hemodialysis was indicated in 49.6% of patients for poorly tolerated hyperemia and 40.5% for water-salt overload.

Central venous catheterization was the vascular access of choice in 88.5% of patients compared to 11.5% for arteriovenous fistula (Table 2).

	Frequency	Proportion (%)
Patients not followed in clinical nephrology	83	79.8
Indication
Poorlytolerated hyperuremia	52	49.6
Hydrosodic overload	42	40.5
Central venous catheterization	92	88.5
Arteriovenous fistula	12	11.5

Table 2: Characteristics of hemodialysis patients at CHUD-B/A between 2016 and 2021.

Hemodialysis survival and mortality rates

Of the 104 patients enrolled, 82 died during the study period, resulting in a cumulative death rate of 78.9%. Using the non-parametric Kaplan-Meier method, the median survival time was estimated to be 7.4 ± 1.3 months, with a 95% Confidence Interval (CI) ranging from 4.79 to 10.05 months. The median time that half of the hemodialysis patients were still alive was 3 months (95% CI = [0.59-1.83]). Figure 2 shows the Kaplan-Meier curve for the overall survival of hemodialysis patients with CHUD-B/A between 2016 and 2021.

Figure 2: Kaplan-Meier curve showing overall survival of hemodialysis patients at CHUD-B/A between 2016 and 2021 N=104.

The probability of survival at six months, one year, and two years was 17.2%, 12.5%, and 5.5%, respectively. The following causes of death were identified: decompensation of anemia (15.9%), complications of diabetes mellitus (14.6%), sepsis (14.6%), heart failure (10.9%), acute pulmonary edema (8.5%), hyperkalemia (3.7%), stroke (1.2%), sudden death (1.2%), and unknown causes (29.3%).

Analysis of factors influencing survival in hemodialysis patients

Analysis bivariate: In bivariate analysis, higher education was correlated with better survival (p=0.022). The presence of diabetes mellitus (p=0.016) or cirrhosis (p=0.019) as comorbidities, the use of a central venous catheter (p<0.001), and a hemodialysis session duration of less than 4 hours (p<0.001) were associated with poor survival. Table 3 summarizes the factors influencing hemodialysis survival in the bivariate analysis.

	Average [95% CI]	Log Rank test
		Chi2	p-value
Education level		9.61	0.022
Out of school	4.4 [1.44-7.38]
Primary	5.8 [2.15-9.51]
Secondary	10.3 [5.97-14.66]
University	11.9 [3.24-20.68]
Diabetes mellitus		5.85	0.016
No	8.4 [5.31-11.47]
Yes	2.5 [1.40-3.60]
Cirrhosis		5.50	0.019
No	7.5 [4.85-10.14]
Yes	0.00
Type of vascular approach		13.70	<0.001
Central venous catheterization	4.5 [3.28-5.80]
Arteriovenous fistula	23.6 [11.84-35.33]
Average session length		15.80	<0.001
3	1.2 [0.27-2.067]
4	7.8 [2.28-22.13]
5	12.2[2.28-22.125]

Table 3: Factors influencing hemodialysis survival in bivariate analysis.

Patient survival by subgroup: In two groups, the first of which included patients with a survival time of less than six months, the influencing factors were heart failure (p=0.049), high blood pressure (p=0.006) and duration of hemodialysis session (p=0.038). Survival beyond 6 months was related to occupational status (p=0.043), herbal medicine practice (p=0.036), diabetes mellitus (p=0.026), central catheter port (p=<0.001), and microcytosis (p=<0.008). Table 4 summarizes the factors influencing hemodialysis survival in bivariate analysis by subgroup.

	<6 months		≥ 6 months
Professional status	Average p-value		Average p-value
Pupil/student	1.7		13
Civil servant	2.4		20.7
Craftsman/worker	2.1		13
Farmer/breeder	1.5		0
Retailer	2.5		7.3
Housewife	2.0		50.0
Motorcycle/car/bulk carrier driver	2.9		6
Herbal medicine practice		0.600		0.036
No	2.5		27.8
Yes	2.1		14.7
Diabetes mellitus		0.332		0.026
No	2.3		18.6
Yes	1.9		6.0
Heart failure		0.049		0.329
No	2.4		18.3
Yes	1.0		8.0
Systolic blood pressure (mmHg)		0.006		0.357
< 120	-		25.3
[120-140]	0.5		8.0
≥ 140	2.4		16.9
Kalemia (mEq/L)		0.040		-
<3.5	0.5		-
>5.5	2.3		17.7
VGM (fL)		0.765		0.008
<80	2.4		15.3
[80-90]	2.0		25.4
>90	2.3		6.0
Type of vascular approach		0.386		<0.001
Central venous catheterization	2.2		10.4
Arteriovenous fistula	3.4		38
Session duration (hours)		0.038		0.988
3	1.2		-
4	2.4		17.6
5	3.0		18.3

Table 4: Factors influencing hemodialysis survival in bivariate analysis by subgroup.

Multivariate analysis: In the multivariate analysis, the variables that significantly influenced hemodialysis patient’s survival were professional status (p=0.027), anatomoclinical lesions (p=0.010), liver cirrhosis (p=0.019), central catheter use (p=0.006) and duration of hemodialysis sessions (p=0.044). Table 5 summarizes the factors influencing hemodialysis survival in multivariate analysis.

	HR (95% CI)	p-value
Socio-professional status		0.027
Civil servant	1
Pupil/student	1.77 [0.59-5.33]
Craftsman/worker	1.92 [0.97-3.84]
Farmer/breeder	7.50 [2.40-23.35]
Retailer	1.87 [0.91-3.81]
Housewife	2.63 [1.13-6.12]
Motorcycle/car/bulk carrier driver	2.17 [0.94-5.02]
Anatomoclinical diagnosis		0.010
Chronic vascular nephropathy	1
Diabetic nephropathy	3.31 [1.52-7.17]
Other glomerular nephropathies	1.31 [0.60-2.86]
Chronic tubulointerstitial nephropathy	0.47 [0.20-1.12]
Hereditary nephropathy (polycystic kidney disease)	0.46 [0.10-2.13]
Undetermined	0.62[0.13-2.91]
Associated cirrhosis
No	1	0.019
Yes	13.96 [1.56-125.24]
Type of vascular approach
Central venous catheterization	3.51 [1.45-8.54]	0.006
Arteriovenous fistula	1
Duration of hemodialysis sessions	0.44 [0.20-0.90]	0.044

Table 5: Factors influencing hemodialysis survival in multivariate analysis.

Discussion

We conducted this study to characterize hemodialysis patients at CHUD-B/A between 2016 and 2021 in terms of sociodemographic, clinical, and paraclinical aspects. It is the first to be carried out at CHUD-B/A, and as such provides baseline data for further, more indepth studies.

Among the 104 patients analyzed, the majority were male (67.3%) with a mean age of 43 years. A significant proportion (43.3%) had no formal education, and 63.5% belonged to a low socioeconomic status. Hypertension was the most prevalent comorbidity (77.9%), while chronic vascular nephropathy was suspected in 61.5% of cases. Additionally, anemia was present in 94.2% of patients, and paraclinical abnormalities such as hyponatremia (55.8%) and hyperkalemia (27.9%) were observed.

Regarding patient survival, the mean duration was 7.4 months, with a high mortality rate (78.9%). In bivariate analysis, higher educational attainment was associated with improved survival (p=0.022), whereas patients with no formal education had a lower mean survival of 4.4 months. The presence of diabetes mellitus (p=0.016) or cirrhosis (p=0.019) was correlated with reduced survival. The use of a central venous catheter was linked to poor survival, whereas patients with an arteriovenous fistula had significantly better survival outcomes (p<0.001).

Multivariate analysis identified several factors associated with reduced survival, including occupational status (p=0.027), suspected anatomical-clinical lesions (p=0.010), cirrhosis (p=0.019), type of vascular access (p=0.006), and dialysis session duration (p=0.044). These findings underscore the necessity of tailored patient management strategies, emphasizing optimized dialysis modalities and comprehensive consideration of patient comorbidities.

Comparison of the results obtained with those of other authors, followed by comments

In our study, the mean age of the patients was 42.99 ± 12.56 years, with extremes ranging from 18 to 72 years. Our population is relatively young; a similar mean was found in Ethiopia, where Workie, et al. reported a mean age of 45 years [5]. Vigan, et al. found a mean age of 48.49 ± 13.82 years in 2021 at the CNHU in Cotonou, with extremes ranging from 18 to 90 years [6]. Fouda, et al. reported a mean age of 47.97 ± 13.19 years in 2017 in Cameroon [7]. Ferreira et al. in Brazil in 2020 gave a mean age of 64.02 ± 15.21 years [8] and Jardine, et al. 52.5 years in South Africa [9]. Chronic end-stage renal disease is occurring in increasingly younger populations in Sub-Saharan Africa, possibly due to the changing lifestyles of our populations. Other factors, such as the presence of the apolipoprotein L1 gene in black subjects, have been reported in scientific literature [10].

The sociodemographic data of the hemodialysis patients in our study show a male predominance (67.31%) with a sex ratio of 2.06. This male predominance has been reported by several authors. Annamalai, et al. found a male/female sex ratio of 3:1 in India [11], Vigan, et al. reported a male proportion of 61.50% with a sex ratio of 1.60 [6] and Ferreira, et al. 52.7% [10]. This justifies the fact that male sex is a factor in the initiation and progression of chronic renal failure.

Survival of hemodialysis patients

The median time that 50% of hemodialysis patients were still alive was 3 months, and the mean survival time was estimated at 7.42 ± 1.34 months, with a 95% Confidence Interval (CI) ranging from 4.79 to 10.05 months. The probability of survival at six months, one year and two years for hemodialysis patients was 17.16%, 12.48% and 5.46% respectively in this study. In a multicenter study conducted in Cameroon in 2017, Fouda, et al. reported similar values, with a median survival of 3 months and a mean survival time of 8 months [7]. In Nigeria, Abene, et al. reported a median survival time of 3 months [12]. These results can be explained by the harsh dialysis conditions in our developing countries. The poor survival in our series is the result of a combination of multiple factors, including late referral of patients, the high cost of dialysis treatment, shortage of dialysis equipment, comorbidities and the absence of a national screening and management program for end-stage renal disease. Moreover, the average number of hours spent on dialysis per week is 8 hours, or two 4-hour sessions for regular patients, which does not correspond to the recommended rhythm of 3 weekly sessions of 3-4 hours each [13]. According to a study by Workie et al. in Ethiopia, the median survival time is 345 days, or about 11 months, and the probability of patients being alive at three months and one year is 85.46% and 49.58%, respectively [5]. Vigan, et al. found that the median survival time for 50% of hemodialysis patients in Cotonou was 3 years [6]. These observed differences could be explained by the better-equipped technical facilities and the larger sample size. Ferreira, et al. in Brazil and Nguyen and Fukuuchi in Vietnam found mean survival times of 6.79 ± 0.37 years [8] and 5.27 ± 0.31 years [14], respectively. In South Africa, Jardine, et al. found a one-year survival probability of 90.4% [9] for hemodialysis patients in 2020, like those reported in the USA (81.5%) [15] and France (83%) [16]. This is justified by the existence of a much better-equipped technical platform and good medical care in these developed countries. Mortality among hemodialysis patients.

During the study period, the overall mortality rate was 78.85%. The probable cause of death was unknown in 24.39% of cases, decompensation of anemia in 15.85%, infectious complications and complications of diabetes mellitus in 14.63%. Fouda, et al. observed a mortality rate of 57.58% among hemodialysis patients in Cameroon [7], with hyperuricemia and catheter sepsis as the main probable causes of death. In China, Yao, et al. observed a mortality of 18.7% in hemodialysis patients and 11.3% in peritoneal dialysis patients [17]. The 2017 annual report of the information network on renal epidemiology in France reported a mortality rate of 16% in hemodialysis patients and 10% in transplant patients [18]. The 2020 USRDS (United States Renal Data System) registry established an annual mortality of 185.4 per 1,000 hemodialysis patients, 137.1 per 1,000 peritoneal dialysis patients and 32.8 per 1,000 transplant patients in 2017 [15]. The mortality rate for hemodialysis patients was higher in Africa than in developed countries. This relatively high rate could be explained by the fact that many patients began their treatment with uremic complications or significant hydro-sodium overload. In addition, some patients were unable to have regular hemodialysis sessions due to the high cost of the sessions [19].

Factors associated with survival in hemodialysis

In multivariate analysis, patient survival was associated with occupational status (p=0.027), anatomoclinical lesions (p=0.010), cirrhosis (p=0.019), central catheter use (p=0.006), and duration of hemodialysis sessions (p=0.044).

Ferreira, et al. estimated that the variables that significantly influence patient survival are the renal lesions responsible for CKD, ferritinemia, serum iron, albuminemia, Providencia, calcemia and phosphoremia. In contrast to our study, they found that patients with hypertensive nephropathy had poor survival compared with those with diabetic nephropathy [8]. Like our study, Yao, et al. found that patients with diabetic nephropathy were more likely to die [17]. Diabetes is the leading cause of CKD worldwide [20], so diabetic patients are easily affected by metabolic disorders, and most commonly used oral antidiabetic drugs are contraindicated in renal failure. This may explain the poor survival of patients with diabetic nephropathy.

The presence of associated liver cirrhosis was found to be a predictive factor for death in our study. A similar finding was reported by Castel, et al. who described a 3-fold increased risk of death in cirrhotic patients undergoing hemodialysis [21]. Hemodialysis in cirrhotic patients is complicated by the increased risk of coagulopathy and interdialytic hypotension [22]. Peripheral vascular resistance is reduced in cirrhotic patients, resulting in chronic hypotension. This hypotension is exacerbated by the sudden decrease in intravascular volume caused by ultrafiltration [23].

In our study, occupational status influenced survival in hemodialysis. Being a civil servant reduced the risk of death by 53.6% while being a farmer or livestock farmer was 5.5 times less likely to survive. This could be explained by the fact that civil servants, generally government employees or retired civil servants, benefit from government funding for their hemodialysis sessions. On the other hand, farmers, who can be considered a lower socio-professional class, do not benefit from this coverage. The result is poor compliance and a high risk of death.

Study Limits

As our study is retrospective, missing and incomplete data are common in this design. We have the possibility of residual confounding by unmeasured variables such as dialysis dose, which reflects the quality of dialysis membrane purification.

Conclusion

Mortality is significantly high, and median survival is relatively short in patients with chronic kidney failure on hemodialysis. Half of all patients die within the first three months of hemodialysis. Median survival is about seven and a half months. It is therefore important to address these factors to improve the quality of hemodialysis care.

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Article Information

Article Type: RESEARCH ARTICLE

Citation: Ahoui S, Godonou J, Melikan AM, Vinassé A, Alavo EJG, et al. (2025) Survival and Predictors of Mortality in Hemodialysis Patients at the Departmental Teaching Hospital of Borgou and Alibori (Benin). Int J Nephrol Kidney Fail 11(2): dx.doi.org/10.16966/2380-5498.254

Copyright: ©2025 Séraphin A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication history: 

Received date: 02 Apr, 2025

Accepted date: 14 May, 2025

Published date: 22 May, 2025