Study design and patient

This cross-sectional study was conducted after obtaining ethical clearance from the Institutional Ethics Committee of the National Institute of Pharmaceutical Education and Research, Mohali (IEC/78/2023-RI) and the Government Medical College and Hospital, Chandigarh (GMCH/IEC/2024/1137R). This original research was carried out at a Government Medical College and Hospital (GMCH) located in Chandigarh, India, between April to November 2024, focusing on patients with DD-CKD.

A total of 104 DD-CKD patients were included based on predefined inclusion and exclusion criteria. The inclusion criteria required participants to be between 18 and 70 years old, have completed at least three months of maintenance hemodialysis, and have provided written informed consent to participate in the study. Patients typically underwent twice- or thrice-weekly hemodialysis sessions, with each session lasting 4–5 hours.

The risks and benefits of participation were thoroughly explained to each participant before enrolment, ensuring a clear understanding of the study’s purpose, objectives, and procedures. The exclusion criteria prohibited those patients with a history of malignancy, recent or active blood loss, pregnant or breastfeeding women, psychiatric disorders, and sensory impairments (hearing or speech limitations) [20–23] from participating in this study.

Data collection was done using a structured questionnaire. This included personal identifiers such as unit name and Unique Health Identification (UHID) number, along with demographic information, including age, gender, educational level, occupation, marital status, income, social habits, and health status. These factors were meticulously documented to facilitate a robust analysis. The methodology emphasized rigorous participant selection and systematic data collection, enabling a comprehensive understanding of the factors influencing CKD patients in a tertiary care setting.

Data collection tool (KDQOL-SF™)

This study has utilized a standardized self-administered questionnaire to collect data, incorporating the Kidney Disease Quality of Life-Short Form (KDQOL-SF™) survey. This comprehensive tool consists of two distinct sections: one addressing issues specific to ESRD and another based on the SF-36. Participants primarily completed the questionnaire independently, but assistance was offered by researchers for those who required help, ensuring completeness and accuracy.

The ESRD-specific section included various domains: 12 items measuring symptoms and problem (SP) list, 8 items evaluating the effect of kidney disease (EKD), 4 items assessing the burden of kidney disease (BKD), 11 items covering the physical component summary (PCS), and 8 items addressing the mental component summary (MCS). The SF-36 component examined across multiple domains, with 10 items on physical functioning (PF), 4 on role physical (RP), 2 on bodily pain (BP), 5 on general health (GH), 5 on emotional well-being (EWB), 3 on role emotional (RE), 2 on social function (SF), and 4 on energy and fatigue (EF) [24, 25].

Responses were transformed to a standardized 0–100 scale, where higher scores indicated better health outcomes. However, certain pre-coded items were scored inversely, with lower scores representing more favorable health conditions. This standardized approach enabled a robust and detailed analysis of the HRQoL measures among the patients.

Data analysis approach

The data collected was analysed using SPSS software (version 20.0). Descriptive statistics, including mean ± standard deviation, percentages, and frequencies, were applied to describe socio-demographic variables, health-related characteristics, and categorical data. Responses from the KDQOL-SF™ survey were examined to determine the proportion of participants encountering difficulties in specific dimensions. Cronbach’s α coefficient was employed to assess the internal consistency and reliability of the subscales.

For statistical comparisons, student t-tests were used to evaluate differences between two groups, while ANOVA was utilized to examine variations across three or more groups. In addition, statistical models, including simple linear regression, logistic regression, and multiple regression analyses, were used to investigate associations between variables. A significance level of p < 0.05 was set to ensure the statistical validity of the results.

Baseline SF-36 domain analysis in ESRD patients

The baseline characteristics of the SF-36 domains for ESRD patients analysed in Table S1 using binary logistic regression showed statistically insignificant differences in PF between IPD and OPD patients (22.31 ± 11.10 vs. 22.69 ± 14.47, OR = 0.999, p = 0.956). Role physical (RP) was higher in IPD patients (11.54 ± 19.40 vs. 9.07 ± 20.24, OR = 1.009, p = 0.568), while BP was lower (31.15 ± 14.38 vs. 39.36 ± 15.68, OR = 0.957, p = 0.058). GH, EWB, RE, and SF showed statistically insignificant differences between IPD and OPD patients (p > 0.05). For age, ≤ 50 years had significantly better GH (29.74 ± 10.72 vs. 20.65 ± 12.74, OR = 0.927, p < 0.001) and SF (38.57 ± 15.27 vs. 34.42 ± 15.37, OR = 0.962, p = 0.041), while > 50 years of patients had higher EWB (49.04 ± 9.20 vs. 46.29 ± 8.47, OR = 1.076, p = 0.017). Gender comparisons showed males had higher PF (24.07 ± 14.37 vs. 19.71 ± 13.08, OR = 0.971, p = 0.100), while females had marginally better GH (24.59 ± 12.17 vs. 23.28 ± 13.15, OR = 1.015, p = 0.382). Unmarried patients had significantly higher RP (20.00 ± 25.82 vs. 8.24 ± 19.18, OR = 1.034, p = 0.049), with statistically insignificant differences in other domains. Patients without a history of past disease had higher GH (29.76 ± 11.00 vs. 20.37 ± 12.56, OR = 1.085, p < 0.001) and SF (41.21 ± 14.69 vs. 32.83 ± 15.04, OR = 1.068, p = 0.002), but slightly lower EF (39.05 ± 9.63 vs. 41.34 ± 12.32, OR = 0.945, p = 0.030). Fistula recommendations were not significantly associated with most variables, except for SF, which was higher in patients not recommended for fistula (39.88 ± 15.67 vs. 33.06 ± 14.68, OR = 0.963, p = 0.018). Patients from rural or urban settings were found to match across SF-36 domains (p > 0.05).

Table S2 (the multinomial logistic regression analysis) showed statistically insignificant differences across education, occupation, or monthly income categories for any SF-36 domains (p > 0.05). Diagnosis type revealed no major differences, although known cases had slightly lower BP (35.64 ± 14.97) compared to freshly diagnosed patients (40.15 ± 15.94, OR = 0.982, p = 0.259).

Correlation among key variables in ESRD patients

The correlation analysis of key variables in ESRD patients, as presented in Table 3, shows that the PCS showed a weak positive correlation with EKD (r = 0.211, p = 0.032), SP (r = 0.321, p < 0.001), and MCS (r = 0.203, p = 0.038). EKD exhibited a moderate correlation with SP (r = 0.576, p < 0.001) and a weak correlation with MCS (r = 0.202, p = 0.039), highlighting its influence on social and mental health dimensions. SP was weakly correlated with MCS (r = 0.269, p = 0.006), while BKD showed no significant correlations with other variables (p > 0.05). These findings highlight the interconnectedness of physical, social, and mental health aspects in ESRD patients, guiding integrated care strategies.

Correlation among key variables in SF-36 health survey domains

The correlation among SF-36 health survey domains in ESRD patients Table 4 showed that PF was statistically significantly correlated with GH (r = 0.248, p = 0.011) and BP (r = 0.219, p = 0.026), suggesting that better PF is associated with improved GH and BP levels. RP showed a moderate correlation with RE (r = 0.334, p < 0.001), indicating a close relationship between the PF and EWB in daily functioning. Additionally, SF showed weak correlations with EWB (r = 0.228, p = 0.020) and BP (r = 0.207, p = 0.035). These results emphasise the comprehensive nature of physical, emotional, and social health in ESRD management.

ESRD and SF-36 health survey: association of variables

The simple linear regression analysis depicted statistically significant associations between variables Table 5. SP showed a strong relationship (B = 0.263, t = 8.207, p < 0.001), accounting for 39.8% of the variance (R² = 0.398, F = 67.35). Similarly, the EKD demonstrated a significant impact (B = 0.184, t = 5.418, p < 0.001, R² = 0.223, F = 29.35), while the BKD contributed moderately (B = 0.131, t = 3.831, p < 0.001, R² = 0.126, F = 14.67).

PCS (B = 0.192, t = 3.593, p < 0.001, R² = 0.112, F = 12.90) and MCS (B = 0.355, t = 5.726, p < 0.001, R² = 0.243, F = 32.79) were both significant, with MCS showing a stronger association. PF (B = 0.252, t = 7.380, p < 0.001, R² = 0.348, F = 54.46) and RP (B = 0.142, t = 5.419, p < 0.001, R² = 0.224, F = 29.36) also exhibited substantial impacts.

Other key variables such as RE (B = 0.173, t = 5.639, p < 0.001, R² = 0.238, F = 31.79), BP (B = 0.167, t = 4.912, p < 0.001, R² = 0.191, F = 24.13), and GH (B = 0.216, t = 5.250, p < 0.001, R² = 0.213, F = 27.56) were statistically significantly associated with HRQoL. SF (B = 0.102, t = 2.734, p = 0.007, R² = 0.068, F = 17.47) and EF (B = 0.175, t = 3.582, p < 0.001, R² = 0.112, F = 12.83) also showed notable contributions. However, EWB (B = 0.126, t = 1.947, p = 0.054) was statistically insignificant. These findings confirm that physical, mental, and social health factors significantly influence HRQoL in ESRD patients, emphasising the need for comprehensive targeted interventions to improve the HRQoL.

The HRQoL data was analysed with respect to comorbidity and social habits.

Comorbidity patterns in DD-CKD patients

The comorbidity patterns of DD-CKD patients in Table 6 were categorized by age and gender. In males, HTN was significantly higher in male patients > 50 years (74.6%) compared to ≤ 50 years (25.4%, p = 0.037) as was polycystic kidney disease (PKD) (76.5% vs. 23.5%, p = 0.028) and pyelonephritis (76.0% vs. 24.0%, p = 0.029). In females, HTN was evenly distributed across age groups (50.0%, p = 0.034), while diabetes showed a significant age-related difference (50.0% in ≤ 50 years vs. 50.0% in > 50 years females, p < 0.001). Glomerulonephritis was present among the female patients (50.0%, p < 0.001). These results indicate a higher prevalence of certain comorbidities, particularly HTN and PKD, in > 50 years males.

Risk factors distribution by age and gender

In males, smoking was more prevalent in > 50 years (80.6%) than ≤ 50 years (19.4%, p = 0.146), while alcohol consumption was significantly greater in > 50 years males (75.0% vs. 25.0%, p = 0.013). Tobacco use was also elevated in > 50 years males (78.9% vs. 21.1%, p = 0.065), and other risk behaviors (poor dietary habits, physical inactivity, inadequate hydration, drug use, and non-adherence to medication) were more common (72.4% vs. 27.6%, p = 0.036). Among females, smoking and alcohol use were higher in > 50 years (71.4% vs. 28.6%, p = 0.338; 73.3% vs. 26.7%, p = 0.383, respectively), with similar trends in tobacco use (66.7% vs. 33.3%, p = 0.152) and other risk behaviors (61.5% vs. 38.5%, p = 0.179) (Table 7).

Table S3 illustrates a statistically significant association (p < 0.05) in the distribution of past disease history across diagnostic categories. Among freshly diagnosed cases, 44.8% (N = 30) reported a history of past diseases, whereas 97.3% (N = 36) did not; this comprised 63.5% (N = 66) of the total sample studied. In the ‘known’ case group, 52.2% (N = 35) reported a past disease history, with none reporting an absence, accounting for 33.7% (N = 35) of the total. Referred cases represented 2.9% (N = 3) of the total patient set, with 3.0% (N = 2) having a history of past diseases and 2.7% (N = 1) without.

Distribution of social habits

The findings indicate that the most common singular social habit among the patients in the study is smoking, observed in 48.07% (N = 50) of patients. This was followed by alcohol use, reported in 41.34% (N = 43) of patients. The least prevalent habit is tobacco use, noted in 24.03% (N = 25) of patients. Additionally, 40.38% (N = 42) of patients fall into the “Others” category, which encompasses various distinct habits. This distribution highlights smoking as the most pervasive habit among DD-CKD patients, with alcohol being the second most prevalent.

Figure 2 is a detailed analysis of the frequency distribution of various social habits and their combinations among the patients in the study. The most prevalent combination is smoking and alcohol consumption (S+A), reported in 33.65% (N = 35) of patients, followed by smoking with other habits (S+O), observed in 28.84% (N = 30) of patients. Moderate frequencies were recorded for smoking and tobacco use (S+TU), found in 17.30% (N = 18) of patients, and alcohol combined with other habits (A+O), observed in 22.11% (N = 23) patients. More complex combinations, such as smoking, alcohol, and tobacco use (S+A+TU), were reported in 9.61% (N = 10) of patients, and all four habits combined (S+A+TU+O), found in 7.69% (N = 8) of patients, were less frequent.

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Figure 2. 

Frequency distribution of social habits among patients. Smoker (S), tobacco user (TU), alcohol consumer (A), and others (O). Other include poor dietary habits, physical inactivity, inadequate hydration, drug use, and non-adherence to medication

Comorbidities distribution in DD-CKD patients

The distribution of comorbidities in the study (Figure 3) shows significant trends. HTN was the most prevalent comorbidity, affecting 97.11% (N =101) of patients, highlighting its dominance as a comorbid condition in the patient. Diabetes follows, impacting 75.96% (N = 79) of patients, reflecting its substantial role as a comorbidity. Other noteworthy conditions include pyelonephritis, present in 40.38% (N = 42) patients, PKD in 26.92% (N = 28) patients, and glomerulonephritis in 25.96% (N = 27) patients. Additionally, 66.34% (N = 69) of patients fall under the category of “Others”. These findings underscore the prevalent and diverse health challenges to improve patient outcomes.

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Figure 3. 

Distribution of underlying causes among patients. PKD: polycystic kidney disease; HTN: hypertension

Conclusions

The key findings of the study are as follows: first, financial capability positively influences physical health outcomes. Second, unmarried patients experience a higher BKD, likely due to reduced social and emotional support. Third, gender and age-specific patterns, such as a higher prevalence of HTN and alcohol use in older males and diabetes in older females, emphasize the need for tailored interventions targeting these subgroups. Fourth, behavioral risk factors, including smoking and alcohol consumption, were found to exacerbate the challenges faced by these patients, further underscoring the importance of comprehensive lifestyle management programs. The use of the KDQOL-SF™ questionnaire provided a detailed assessment of HRQoL, though variability in reliability across its domains suggests the need for a multifaceted approach to evaluating and addressing the concerns in DD-CKD patients.

Limitations

While this study provides valuable insights into HRQoL among dialysis patients, certain limitations must be considered when interpreting the findings. The cross-sectional design enables the identification of associations but does not establish causality, highlighting the need for longitudinal studies to confirm these relationships over time. Additionally, as this study was conducted in a single-center setting, the findings primarily reflect the characteristics of the studied patients. Conducting multi-center studies would enhance the generalizability of these results.

Furthermore, the reliance on self-reported measures introduces the possibility of response bias. However, standardized assessment tools were employed to minimize subjectivity and improve data reliability. Despite these considerations, the study’s robust methodology and adequate sample size provide a strong foundation for understanding gender- and age-specific disparities in HRQoL. Patients on peritoneal dialysis were excluded from this study; therefore, the findings are specific to hemodialysis patients and may not be generalizable to the peritoneal dialysis population. This study may be prone to recall bias due to the self-reported nature of data collection, which should be considered a methodological limitation. To address these limitations, future research incorporating longitudinal follow-ups, multi-center cohorts, and larger, more diverse populations will be crucial in validating and expanding upon these findings.

Strengths

A notable strength of this study is its adequate sample size, which enhances the reliability and robustness of the results. The focus on socio-demographic variables offers a detailed understanding of the factors influencing HRQoL in DD-CKD patients. This cross-sectional analysis holds significance in its ability to inform strategies for improving HRQoL through tailored, patient-centered interventions. Prior studies have underscored the impact of clinical and treatment-related factors on HRQoL outcomes. For instance, the HEMO study by Unruh et al. [40] demonstrated that variations in dialysis dose and membrane flux can influence patient-reported outcomes. Similarly, Weisbord et al. [41] showed that addressing symptoms such as pain, depression, and sexual dysfunction can meaningfully enhance the quality of life in patients receiving chronic hemodialysis. In this context, our study contributes valuable evidence that can support the development of targeted approaches to improve patient well-being.

Future research directions

Future studies should adopt longitudinal and multi-center designs, explore the impact of mental health and social support on HRQoL, and integrate qualitative methods for deeper patient insights.