Exploring the quality of life of end-stage kidney disease patients in Khartoum State, Sudan: a multicenter cross-sectional study

TL;DR

This study assesses the quality of life for kidney disease patients in Sudan, finding significant physical and mental health challenges linked to factors like age and comorbidities.

Contribution

The study provides new insights into HRQOL in Sudanese ESKD patients using a multicenter cross-sectional design.

Findings

Physical and mental health scores were significantly impaired in ESKD patients.

Disease burden and work status had the lowest HRQOL scores.

Age and comorbidities like diabetes were significant predictors of HRQOL.

Abstract

Given the rising incidence of end-stage kidney disease (ESKD) in Sudan, assessing health-related quality of life (HRQOL) is critical for evaluating patient outcomes. This study evaluated HRQOL and associated factors in end-stage kidney disease patients in Khartoum State renal centers in Sudan.

This cross-sectional study administered the Kidney Disease Quality of Life Short Form (KDQOL-SF™) to 150 ESKD patients on maintenance dialysis for ≥ one month across 13 renal centers in Khartoum State. Data were analyzed using SPSS Statistics. Independent t-tests, ANOVA, Pearson correlation, and multiple regression analyses were conducted to assess associations. The p-value was set at 0.05 for statistical significance.

The Physical (40.17 ± 9.01) and Mental (47.10 ± 9.86) Component scores significantly affected HRQOL in ESKD patients. The lowest scores were observed for burden of kidney disease (31.25 ± 38) and work status (0.00 ± 50). The SF-12 Physical Component was associated with employment status (p < 0.001) and dialysis accompaniment (p = 0.011). Diabetes comorbidity affected the Mental Component (p = 0.017). Hospitalization frequency showed significant negative correlations with the SF-12 Mental Component (r = -0.249), burden of kidney disease (r = -0.330), effects of kidney disease (r = -0.303), and Kidney Disease Component Summary (r = -0.247). In the multiple regression model for the SF-12 Physical Component, age group was the only significant factor (p = 0.023).

Both physical and mental health domains were significantly impaired in the studied ESKD population. The lowest scores were observed for disease burden and work status. Enhancing healthcare access, addressing comorbidities, and reducing financial strain may improve outcomes. Further longitudinal and case-control studies are warranted to clarify determinants of HRQOL.

The online version contains supplementary material available at 10.1186/s12882-025-04257-2.

Full text

Background

Chronic kidney disease (CKD) is defined as kidney damage or a reduced estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² for ≥ 3 months [1]. It is characterized by a gradual deterioration in kidney function, often necessitating treatments such as dialysis or transplantation [1, 2]. CKD is categorized into five stages on the basis of the glomerular filtration rate (GFR), with the 5th stage representing end-stage kidney disease or kidney failure, where the GFR is less than 15 ml/min [2, 3]. Patients with end-stage kidney disease have higher mortality rates, ranging from 20% to 50% over 24 months, even with timely dialysis [3]. Worldwide, it is estimated that between 4.902 and 9.701 million people suffering from end-stage kidney disease (ESKD) need renal replacement therapy (RRT), with a higher prevalence observed in low- to middle-income nations [4]. The prevalence of the disease is notably high in African nations, affecting approximately 8–16% of the population [5]. The lack of trustworthy health information systems has made it difficult to determine the exact prevalence of ESKD in the area. According to statistics from 13 sub-Saharan nations, the overall prevalence of CKD was estimated to be 13.9% [6]. In Sudan, access to kidney replacement therapy is constrained and not universally available. Governmental and private hemodialysis centers serve 62.6% and 37.4% of the dialysis population, respectively [7]. Patients often incur substantial out-of-pocket expenses, with the median annual direct per capita cost of ESKD reaching 38,600 SDG ($1,723.2) in Khartoum State [8].

Health-related quality of life (HRQOL) is a multifaceted concept that encompasses a patient’s overall assessment of how their health condition and medical interventions influence their physical, mental, and social dimensions of well-being [9]. ESKD patients report poorer HRQOL compared to the general population [5, 10]. Numerous studies have identified various determinants that contribute to and impact health-related quality of life (HRQOL) among individuals suffering from ESKD. Patients with ESKD commonly experience constrained functional status across domains such as physical, role, social, and mental functioning due to the manifestations of the disease and the associated treatment protocols [11, 12]. Multiple comorbidities, lower albumin and haemoglobin levels, and other clinical factors have been consistently linked to a reduced quality of life. Additionally, factors such as age, gender, living situation, and income have been linked to HRQOL [13].

Research on quality of life among ESKD patients in Sudan is limited. A recent study of 168 patients found that transplant recipients had significantly better quality of life than those on dialysis, highlighting the strong impact of treatment type on well-being [14].

Despite Sudan’s growing ESKD burden, gaps persist in understanding HRQOL determinants. This highlights the need for further exploration of patient-centered outcomes in this population. Assessing health-related quality of life (HRQOL) has become crucial and is often necessary for evaluating health outcomes [11]. Evaluating HRQOL in patients with ESKD could provide a valuable way to understand the impact of healthcare interventions in situations where a cure is not feasible. Therefore, this study aimed to assess health-related quality of life and its associated factors in adult patients with end-stage kidney disease who were attending public specialized renal centers in Khartoum State, Sudan.

Methods

Study design and setting

This was a descriptive, cross-sectional study conducted at the health facility level in January 2020. It was carried out in 13 selected dialysis centers in Khartoum State, Sudan. These centers provide dialysis services to registered renal patients and handle emergency cases. Out of 32 renal centers in Khartoum State, only 13 centers were included, as they were the ones where patients attended follow-up visits during the research period. The renal centers included in the study were Alwaledeen, Tropical Disease, Alnaw, Ombada, Chinese Friendship, Military Hospital, Academy, Elshahida Salma, Bashaier, Ibnsina, Elsafia, Renal Transplant Association, and Ahmed Gasim. We used strengthening the reporting of observational studies in epidemiology (STROBE) reporting guidelines for cross-sectional studies to ensure proper reporting for the study [15].

Study participants

The study included adult male and female patients aged ≥ 18 years who were diagnosed with ESKD and had been on maintenance hemodialysis for at least one month. This minimum duration was chosen to ensure that participants had sufficient experience with dialysis to meaningfully assess its impact on their quality of life.

Exclusion criteria were patients with acute or severe health conditions that could impair communication and those who declined participation. These exclusions aimed to ensure valid and complete responses during data collection.

Sample size and sampling technique

The required sample size was calculated using Cochran’s formula for single population proportion [16], with a 95% confidence interval, a proportion of 0.001 (based on the global prevalence of ESKD in 2019) [17], and an accepted sample error of 0.05.

Equation:

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n = sample size.

t = standard error associated with the chosen level of confidence (%95).

p = proportion of the population (prevalence).

q = 1-p.

d = accepted sample error (reduced to 0.005 to increase precision in estimating a rare outcome).

Based on this calculation, the final sample size was 154 patients. Ultimately, data were collected from 150 patients. The sample was distributed proportionally on the basis of the average patient frequency at each facility, with the interval for each center determined as shown in Table S1. Systematic random sampling was used within each center. Random days of the week were selected, and on each selected day, the patient list was used as the sampling frame. Patients were selected at fixed intervals as they arrived until the desired number was reached.

Study tools

A face-to-face interview questionnaire was conducted by trained medical students. Informed consent was obtained immediately before each interview. Patients’ medical records were reviewed to extract clinical and sociodemographic data. The sociodemographic variables included age, gender, residence, education level, and employment status. Medical information covered the impact of kidney failure on daily activities, comorbidities, history and duration of renal impairment, number of dialysis locations attended, frequency of dialysis sessions per week, accompaniment during dialysis visits, presence and type of health insurance, and history of hospitalization. We used the standardized Kidney Disease Quality of Life Short Form (KDQOL-SF™), version 1.3, to assess the quality of life in these patients [18, 19]. This tool is specifically designed for individuals with kidney disease and those undergoing dialysis. It includes 43 items targeting kidney disease and 36 items that form a generic core, along with an overall health rating item. The questionnaire consists of 80 items divided into 19 dimensions. The disease-specific Component of KDQOL-SF™ 1.3 comprises 43 items across 11 domains, including the symptom/problem list (12 items), effects of kidney disease (8 items), burden of kidney disease (4 items), cognitive function (3 items), quality of social interaction (3 items), sexual function (2 items), sleep (4 items), social support (2 items), work status (2 items), patient satisfaction (1 item), and dialysis staff encouragement (2 items).

The SF-36 Component contains 36 items that assess eight domains of functioning and well-being on a 100-point scale. These domains include physical function (10 items), role limitations due to physical problems (4 items), role limitations due to emotional problems (3 items), pain (2 items), general health perceptions (5 items), social function (2 items), emotional well-being (5 items), and energy/fatigue (4 items). The final item, an overall health rating, asks respondents to rate their health on a 0–10 scale. Results from the SF-36 are further summarized into a Physical Component Summary (PCS) score and a Mental Component Summary (MCS) score. PCS aggregates items from physical function, role physical, pain, and general health, while MCS aggregates items from role emotional, emotional well-being, energy, and social function [18]. According to Mapes et al. [20], the kidney disease-targeted scale items are also summarized into a Kidney Disease Component Summary (KDCS) score on a 100-point scale.

The standard KDQOL-SF™ 1.3 scoring program, based on a Microsoft Excel 97 spreadsheet, includes detailed computation methods. Scores for each dimension range from 0 to 100, with higher scores indicating better health-related quality of life (HRQOL). The health change (question 2) of the SF-36 and the 0–10 overall health rating (question 22) are scored as single items [18]. The KDQOL-SF™ 1.3 questionnaire was administered in a single session, typically taking around 16 min, with the entire questionnaire, including this section, requiring approximately 20 min. To minimize patient burden, interviews were conducted during dialysis sessions or at times convenient for the participants.

Data analysis

The Statistical Package for the Social Sciences (SPSS) version 26 was used for data entry and statistical analysis. Frequency tables and percentages were used to present the categorical variables. Means, standard deviations, medians, and interquartile ranges were used to describe the continuous data. Normality tests were applied to all scales. All scales exhibited non-normal distributions, except for the SF-12 Physical Health and SF-12 Mental Health composite scores, which demonstrated normal distribution. Independent t tests and analysis of variance (ANOVA) were used to determine the differences across socio-demographic and clinical data regarding the SF-12 scales, as they were normally distributed. Mann-Witney t test and Kruskall Walis test were used for Kidney Component Scale as it was non normally distributed. Pearson’s and Sperman’s correlations were used to assess the association between total cost, hospitalization time, and duration of hospitalization with quality of life scales. Multiple linear regressions were used to find associations between the study variables. The used level of significance was 0.05.

Results

Sociodemographic, clinical, and dialysis-related characteristics

A total of 150 patients participated in the study, with a response rate of 97%. The median age was 48 years, and the most common age group was 51–60 years, representing one-quarter of the participants. The majority—almost two-thirds—were male. Most of them (90.7%) lived in urban areas. Almost 36% were working at the time, even though nearly two-thirds of the sample reported having stopped their daily activities. The majority (70.7%) had hypertension as comorbidity with kidney failure. Nearly one-third of the patients (34.2%) had visited more than one dialysis center. Almost 60% of the participants went to dialysis accompanied. The majority of the sample (85.3%) had health insurance (Table 1).

Table 1. Sociodemographic, clinical, and dialysis-related characteristics of the participants (N = 150)VariablesFrequencyPercentage (%) Age: 21–303020.0%31–402214.7%41–503020.0%51–603926.0%61–702617.3%> 7032.0%Gender:Male9261.5%Female5838.5%Current place of residence:Urban13690.7%Urban slum96.0%Rural53.3%Highest level of education:Not attended/illiterate117.5%Primary3624.5%Secondary6040.8%Graduate2919.7%Postgraduate106.8%Other types of education10.7%Current employment status:Currently working5436.5%Not working currently9463.5% Have you stopped your daily life activities due to kidney failure? Yes8367.5%No4032.5%Presence of comorbidities*:Hypertension10670.7%Diabetes2315.3%Autoimmune disease21.3%Other1812.0%Do you have any history of renal impairment:Yes7248.0%No7852.0%Duration of kidney failure:Less than 1 year96.0%1–3 years7751.3%4–6 years3221.3%7–10 years2114.0%More than 10 years117.3%Number of places of dialysis:One place9865.8%Two places2315.4%Three places149.4%More than three places149.4%Times per week for dialysis:1 time10.7%2 times13086.7%3 times1912.7% Go to dialysis accompanied Yes8959.7%No6040.3% Presence of health insurance Yes12885.3%No2214.7%Type of insurance:Social health insurance11892.2%Private insurance97.0%Other10.8%Have you been hospitalized before or after the beginning of dialysis:Yes9160.7%No5939.3%*Participants were allowed to select more than one choice

Physical and mental functioning

A univariate analysis of sociodemographic, clinical, and dialysis characteristics in relation to the Physical Component scale was conducted. Patients who were working at the time reported significantly lower physical functioning (mean = 44) compared to those in the non-working group (p-value < 0.001). Additionally, patients who went to dialysis accompanied had significantly lower physical functioning (mean = 39) compared to those who had no one (p-value = 0.011) (Table 2). Regarding the Mental Component scale, patients with diabetes had significantly better mental functioning than those without diabetes (mean = 51 vs. 46; p-value = 0.017) (Table 3).

Table 2. Univariate analysis of sociodemographic, clinical, and dialysis-related characteristics of the participants and the SF-12 physical component (N = 150)VariablesSF-12 Physical ComponentMeanStandard deviationP-valueAge groups21–304380.06231–4044841–5039951–6039961–703710> 70353GenderMale4180.201Female3910ResidencyUrban4090.451Urban slum389Rural458Educational levelNot attended/illiterate3970.270Primary389Secondary4210Graduate418Postgraduate3810Other type of education33Are you currently working?Currently working4480.000Not working currently389Have you stopped working due to kidney failure?Yes4090.642No4111Presence of comorbidities^a^Hypertension3990.097Diabetes3880.268Autoimmune disease3160.161Other diseases3990.731Do you have any history of renal impairment?Yes3990.208No419Duration of kidney failure in yearsLess than 1 year4470.2781–33994–643107–10409More than 10 years377How many places to receive dialysis?One place3990.565Two places419Three places438More than three places408Times per week to receive dialysis1 time320.6082 times4093 times4112Go to dialysis accompaniedYes3990.011No438Presence of health insuranceYes4090.421No399Type of insuranceSocial health insurance4190.259Private insurance376OtherHave you been hospitalized before or after the beginning of dialysis?Yes3990.104No429^a^ Participants were allowed to select more than one choice, which is why the analysis was performed as 0/1 for each choicep-value < 0.05p-value < 0.001

Table 3. Univariate analysis of sociodemographic, clinical, and dialysis-related characteristics of the participants and the SF-12 mental component (N = 150)VariablesSF-12 Mental ComponentMeanStandard Deviationp-valueAge groups21–3045100.34131–4046841–5049951–60471261–70477> 70524GenderMale47100.600Female4810ResidencyUrban46100.068Urban slum538Rural5411Educational levelNot attended/illiterate5050.391Primary4711Secondary4610Graduate5010Postgraduate437Other type of education55Are you currently working?Currently working48100.684Not working currently4710Have you stopped working due to kidney failure?Yes46100.225No4910Presence of comorbidities^a^Hypertension47100.821Diabetes5170.017Autoimmune disease4210.481Other diseases4960.240Do you have any history of renal impairment?Yes46100.178No489Duration of kidney failure in yearsLess than 1 year42120.6821–34794–648107–104512More than 10 years489How many places to receive dialysis?One place47110.818Two places478Three places478More than three places508Times per week to receive dialysis1 time440.8372 times47103 times488Go to dialysis accompaniedYes47110.715No479Presence of health insuranceYes47100.755No4610Type of insuranceSocial health insurance47100.824Private insurance467OtherHave you been hospitalized before or after the beginning of dialysis?Yes47100.445No4810^a^Participants were allowed to select more than one choice, which is why the analysis was performed as 0/1 for each choicep-value < 0.05

Kidney disease component summary (KDCS)

A univariate analysis was performed regarding patient characteristics against KDCS. Compared with people living in urban areas, those living in rural areas reported a significantly higher quality of life using the KDCS (p-value = 0.037). Additionally, patients who had stopped working experienced a significantly better quality of life than those who were working at the time of the study (p-value = 0.011). Patients who did not have hypertension had a significantly better quality of life (median = 67.61 vs. 58.3) than those who did (p-value = 0.014). Having health insurance was also significantly associated with a better quality of life (p-value = 0.001), whereas going to dialysis accompanied was significantly associated with a poorer quality of life (p-value = 0.002) (Table 4).

Table 4. Univariate analysis of sociodemographic, clinical, and dialysis-related characteristics of the participants and kidney disease component summary (N = 150)VariablesKidney Disease Component SummaryMedianInterquartile range (IQR)p-valueAge groups21–3056.7220.740.65731–4062.8916.2541–5062.6122.3651–6059.3018.0961–7060.5418.12> 7053.2420.38GenderMale62.9619.500.476Female58.8117.99ResidencyUrban58.7417.590.037Urban slum65.1811.86Rural72.292.84Educational levelNot attended/illiterate60.0218.690.092Primary55.8916.22Secondary63.0316.21Graduate59.3018.69Postgraduate66.5734.22Other type of education71.650.00Are you currently working?Currently working61.6920.800.505Not working currently59.4515.95Have you stopped working due to kidney failure?Yes58.3018.220.011No66.3220.15Presence of comorbidities^a^Hypertension58.3017.480.014Diabetes60.0220.880.425Autoimmune disease53.755.080.366Other diseases59.3018.300.692Do you have any history of renal impairment?Yes59.0917.350.674No60.4819.33Duration of kidney failure in yearsLess than 1 year54.6225.170.1011–357.8917.944–666.2715.297–1059.6017.02More than 10 years58.8821.01How many places to receive dialysis?One place61.7419.110.387Two places55.5617.99Three places56.3123.01More than three places62.897.57Times per week to receive dialysis1 time67.610.000.8132 times59.4918.143 times59.8923.56Go to dialysis accompaniedYes56.8817.990.002No66.1516.47Presence of health insuranceYes62.1318.410.001No53.7915.47Type of insuranceSocial health insurance61.6918.020.855Private insurance66.9324.11Other67.900.00Have you been hospitalized before or after the beginning of dialysisYes61.3518.610.971No59.3018.17^a^Participants were allowed to select more than one choice, that is why the analysis was performed as 0/1 for each choicesignificant p-value < 0.05

Factors that correlate with the KDQOL components

A correlation matrix was conducted to examine the relationships between the cost of illness, the number of hospitalizations, and the duration of hospitalization against the scales of quality of life. The total cost of dialysis was significantly positively correlated with the burden of kidney disease (r = 0.203, p < 0.05). There was a significant, weak negative correlation between the number of hospitalizations and the following: SF-12 Mental Component (r = -0.249), burden of kidney disease (r = -0.330), effects of kidney disease (r = -0.303), and Kidney Disease Component Summary (r = -0.247; all p < 0.05) (Table S2).

Kidney disease quality of life scale descriptive statistics

A 36-item KDQOL (KDQOL-36) was used to generate summary scores for the Physical Component Summary (PCS) and Mental Component Summary (MCS), with means ± SDs of 40.17 ± 9.01 and 47.10 ± 9.86, respectively. Additionally, the overall health scale score had a median of 60 and IQR of 30, while the patient satisfaction scale score was 66.67 (IQR = 17) (Table 5).

Table 5. Descriptive statistics of the kidney disease quality of life scale domain for the participants (N = 150)Scale (number of items in scale)MedianIQRSymptom/problem list (12)70.4528Effects of kidney disease (8)71.6531Burden of kidney disease (4)31.2538Work status (2)0.0050Cognitive function (3)86.6733Quality of social interaction (3)93.3320Sexual function (2)100.0025Sleep (4)72.5038Social support (2)100.0033Dialysis staff encouragement (2)100.0025Overall health (1)70.0030Patient satisfaction (1)66.6717Physical functioning 10)55.0045Role limitations–physical (4)50.00100Pain (2)66.2565General health (5)60.0030Emotional well-being (5)72.0032Role limitations–emotional (3)100.00100Social function (2)50.0063Energy/fatigue (4)50.0030Kidney Disease Component Summary59.5259.52 Mean

SD SF-12 Physical Health Component40.179.01SF-12 Mental Health Component47.109.86IQR = Interquartile rangeSD = Standard deviation

Regression model for the SF-12 Physical Component

The general multiple regression model for the SF-12 Physical Component included factors with a p-value of < 0.25. It included age groups, gender, employment status, presence of hypertension, autoimmune disease, history of renal impairment, dialysis accompaniment, and history of hospitalization. The model was statistically significant (p = 0.004), with an R-squared value of 0.170. Among these variables, only age groups were significantly associated with the SF-12 Physical Component (p = 0.023) (Table S3).

Regression model for Kidney Disease Component Summary

This analysis included factors with a p-value of < 0.25. The model included residency, educational level, stopping work due to renal failure, presence of hypertension, duration of renal failure, dialysis accompaniment, health insurance status, and frequency of hospitalization. The model was statistically significant (p = 0.048), with an R² value of 0.195. However, none of the included variables were significantly associated with the Kidney Disease Component Summary (Table S4).

Discussion

Patients with end-stage kidney disease (ESKD) frequently experience significantly reduced quality of life (QOL) due to high mortality risk and adverse clinical outcomes [21–23]. This study aimed to identify modifiable factors influencing QOL to guide interventions for ESKD patients. Therefore, we used the Kidney Disease and Quality of Life-Short Form (KDQOL-SF™) to assess the quality of life for end-stage kidney disease patients in Khartoum State, Sudan, and linked with various sociodemographic and clinical characteristics in 150 participants.

While our findings on the impaired physical and mental health components align with global trends [24, 25], they provide the first evidence of this burden in Sudan’s unique healthcare context. More than half of our participants reported a duration of kidney failure of ≤ three years. A study conducted in Pakistan also reported a significant proportion of participants having a duration of kidney failure within a similar range of 2–4 years [24], indicating a comparable duration of kidney failure across these populations.

Hypertension and diabetes were the leading comorbidities, mirroring Kenyan data where hypertension was not only the most prevalent comorbidity but also the most prevalent underlying cause of kidney failure [25]. In contrast, a recently conducted study in Pakistan reported that diabetes was the comorbidity most prevalently associated with stage 5 CKD [24]. Comorbidities are known to impact ESKD patients’ survival and hospitalization rates [26]. Notably, a study concerning symptom management in CKD patients reported that patients on dialysis had a variety of symptoms that may be associated with their underlying ESKD, as well as various comorbidities, such as diabetes, hypertension, social situations, financial stresses, and/or multiple medications [27]. Over 85% of participants had health insurance, similar to rates reported in India [28]. Health insurance is intended to increase the accessibility and affordability of healthcare services. This approach becomes particularly crucial amidst the rising costs of health services. Therefore, a lack of insurance increases the economic burden on the patients and their families [29]. In this context, a study conducted in America revealed that people with CKD who do not have enough insurance pay a large amount out of pocket since they need continuous medical care throughout the year [30].

In terms of the burden of kidney disease, the present study revealed a relatively high burden compared to another study conducted in southern Kerala, India, which reported a lower burden on the kidney disease subscale [28]. These variations in the magnitude of the kidney disease burden may be attributed to the discrepancy in socioeconomic status between the different study populations, as there is an established association between low income and the progression of chronic kidney disease [31]. However, generally, these low scores may be attributed to factors such as adverse drug reactions, hospital stays, disrupted social life, and restricted diets that may increase the disease burden.

Cognitive function scores were higher in our study than in Indian study [28], suggesting preserved cognition despite ESKD, but a selection bias could not be excluded.

The Physical Component Summary (PCS) score in our study was low, aligning with findings from Kenya and India, which also demonstrated decreased physical functioning among patients with kidney disease [25, 28]. Low PCS scores likely reflect chronic symptoms, physical activity limitations, pain or discomfort, and diminished overall well-being. In the present study, PCS was significantly associated with employment status but not with education level. This finding contrasts with a study conducted in India, which found that higher education was associated with better PCS scores [28]. It also opposes the results of another Indian study, which identified significant associations between unemployment, illiteracy, and lower PCS scores [32]. Furthermore, the latter study highlighted unemployment and age as key predictors of PCS [32].

On the other hand, the Mental Component Summary (MCS) score was higher than the Physical Component Summary (PCS) score in both the present study and the Indian study [10]. This may be attributed to patients’ psychological adjustment to physical challenges. This moderate level of mental well-being among ESKD patients is consistent with findings from a study conducted in Kenya [25].

Diabetes was associated with higher Mental Component Summary (MCS) scores. However, we did not find any other significant factors associated with MCS scores.

Age has also been reported as a significant factor influencing MCS scores [28, 33]. For instance, research conducted in the United Kingdom found that older patients had higher MCS scores compared to younger patients, suggesting that older individuals may cope better emotionally with their kidney disease [33]. Conversely, another study from India [28] reported that younger patients had better MCS scores than older patients. Similarly, in Kenya [25], although the differences in MCS scores across age groups were not statistically significant, younger patients tended to have higher MCS scores than older ones. The older patients in the Kenyan study were more prone to emotional distress due to the heavy financial burden of dialysis, especially since many were unemployed. However, several other studies have shown that younger patients generally experience a better quality of life [34, 35].

We found no significant connection between having health insurance and the Physical or Mental Component Summary (PCS or MCS) scores. However, research from Nepal [36] did identify a strong link between having health insurance and both PCS and MCS scores. This suggests that health insurance may not cover the full range of needed services or address systemic healthcare challenges, limiting its effect on physical and mental health outcomes. Additionally, patients may face other barriers, such as inadequate healthcare infrastructure or financial constraints, which could undermine the potential benefits of insurance on their overall health. Moreover, factors such as cultural background, economic status, emotional well-being, access to medical care, and spiritual beliefs can significantly shape how individuals perceive their health and illness, potentially overshadowing the benefits of insurance.

We also observed that the Kidney Disease Component Summary (KDCS) score was notably higher than both the PCS and MCS scores. This suggests that patients generally experience fewer issues specifically related to kidney disease, perceive a lower burden from the disease, and thus report a better quality of life in this area.

Univariate analysis linked residence, working cessation due to the disease, high blood pressure, health insurance, and dialysis accompaniment to higher KDCS scores, but multivariate analysis showed no significant associations. In our study, factors such as place of residence, employment status, comorbidities, presence of companions for dialysis, and health insurance were significantly related to quality of life outcomes. This differs from findings in India [28] and Nepal [36], where older age, female gender, and lower education levels were associated with poorer quality of life scores. Additionally, we observed that the total cost of dialysis was positively correlated with the burden of kidney disease score, while the number of hospitalizations was negatively correlated with this score. This aligns with research from Japan, which found a strong association between kidney disease quality of life (KDQOL) and hospitalization in patients with end-stage kidney disease [10]. Moreover, several studies have shown that health-related quality of life (HRQOL) is a predictor of both mortality and hospitalization in dialysis patients [36, 37]. These findings suggest that the impact of factors such as healthcare costs and hospitalization on quality of life may differ across settings and highlight the importance of considering various contextual factors when evaluating quality of life outcomes.

This study is one of the first to assess health-related quality of life (HRQOL) in patients with end-stage kidney disease (ESKD) in Sudan using the validated KDQOL-SF™ 1.3 instrument. The inclusion of 13 renal centers across Khartoum State improves the representativeness of the findings. Data were collected through face-to-face interviews by trained medical students, enhancing the accuracy and completeness of responses.

However, several limitations should be considered. The cross-sectional design restricts the ability to infer causality between HRQOL and associated factors. The study did not explore the association between HRQOL and biochemical or laboratory parameters, which are important clinical indicators in ESKD. Cultural differences might influence how patients perceive and report quality of life, potentially affecting score interpretation.

Conclusion

This study provides valuable insights into the quality of life (QOL) of patients with end-stage kidney disease (ESKD) in Khartoum State, Sudan. Both physical and mental health domains were significantly impaired in the studied ESKD population, aligning with global trends in dialysis care. The lowest scores were observed for disease burden and work status.

To improve the quality of life for ESKD patients in Sudan, it is recommended to enhance patient access to specialized healthcare services, strengthen health insurance coverage, and address the burden of comorbidities such as hypertension and diabetes. Promoting employment opportunities for patients, especially those with ESKD, can help alleviate the physical and mental impact of the disease. Additionally, increasing awareness about the importance of social support and dialysis accompaniment may improve patients’ overall well-being. It is also essential to reduce the financial burden associated with dialysis treatments through policy interventions and subsidies. Further research is needed to explore the unique socio-cultural factors affecting patients’ quality of life. Future studies should include matched controls and biochemical data to clarify causal relationships. More research on the disease burden is also essential to guide effective, tailored healthcare strategies.

Electronic supplementary material

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Supplementary Material 1