
==== Front
Clin Kidney J
Clin Kidney J
ckj
Clinical Kidney Journal
2048-8505
2048-8513
Oxford University Press

10.1093/ckj/sfae241
sfae241
Original Article
AcademicSubjects/MED00340
The impact of quality of life on the survival of elderly patients with end-stage renal disease: a prospective multicenter cohort study in Korea
https://orcid.org/0000-0002-7193-4754
Chung Yu-Kyung Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea
Clinical Research Center for End-Stage Renal Disease, South Korea

https://orcid.org/0000-0001-5517-9886
Lim Jeong-Hoon Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea
Clinical Research Center for End-Stage Renal Disease, South Korea

https://orcid.org/0000-0002-4857-8616
Jeon Ye-na Clinical Research Center for End-Stage Renal Disease, South Korea
Department of Epidemiology & Biostatistics, University of California at San Francisco, San Francisco, CA, USA

https://orcid.org/0000-0001-7318-5753
Jeon You Hyun Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea
Clinical Research Center for End-Stage Renal Disease, South Korea

https://orcid.org/0000-0003-0232-7202
Jung Hee-Yeon Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

https://orcid.org/0000-0002-9774-3665
Choi Ji-Young Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

https://orcid.org/0000-0002-0953-3343
Park Sun-Hee Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

https://orcid.org/0000-0002-4648-0324
Kim Chan-Duck Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

https://orcid.org/0000-0002-1344-3455
Kim Yong-Lim Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

https://orcid.org/0000-0002-7031-5214
Cho Jang-Hee Division of Nephrology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea

Correspondence to: Jang-Hee Cho; E-mail: jh-cho@knu.ac.kr
Correspondence to: Yong-Lim Kim; E-mail: ylkim@knu.ac.kr
9 2024
13 8 2024
13 8 2024
17 9 sfae24114 12 2023
02 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the ERA.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

ABSTRACT

Background

Quality of life (QOL) is associated with mortality in dialysis patients. However, the impact of QOL index or score on elderly patients undergoing maintenance dialysis is unclear. We analyzed the relationship between QOL domains and survival in elderly end-stage renal disease (ESRD) patients on dialysis.

Methods

We included 492 incident ESRD patients aged ≥65 years from a Korean nationwide prospective cohort study who were assessed for QOL with a follow-up duration of 67.3 ± 34.6 months after dialysis initiation. Their QOL was evaluated using the Kidney Disease Quality of Life (KDQOL) instrument, and the effect of each QOL domain on mortality was analyzed. Multivariable Cox regression analysis was performed to identify independent risk factors for death after adjusting for confounding factors.

Results

Low physical component summary (PCS) and Short Form-36 score were significantly associated with low survival rate (P < .001 and P = .017, respectively), whereas the mental component summary and ESRD-targeted item scores were not correlated with survival rate. Multivariable Cox regression analysis confirmed that only a high PCS score was associated with better survival (hazard ratio 0.71; 95% confidence interval 0.52–0.97; P = .031). Linear regression analysis revealed that age, sex, modified Charlson comorbidity index, albumin and intact parathyroid hormone were associated with PCS. Among the PCS items, only the physical functioning score was significantly associated with mortality (P = .017).

Conclusion

PCS was an independent risk factor for death in elderly ESRD patients. A higher physical functioning score was associated with a better outcome, suggesting the importance of physical condition in elderly dialysis patients.

Graphical Abstract

Graphical Abstract

dialysis
elderly patient
end-stage renal disease
quality of life
survival
Ministry of Health and Welfare 10.13039/501100003625 HR22C1832 HI22C1529
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pmcKEY LEARNING POINTS What was known:

Quality of life (QOL) is associated with mortality in dialysis patients; however, the relationship between QOL and long-term prognosis has been studied with limited data specifically in elderly end-stage renal disease (ESRD) patients.

We aimed to analyze the association between QOL and mortality in Korean elderly ESRD patients.

This study adds:

We confirmed the independent impact of QOL that predicts long-term prognosis in elderly ESRD patients.

Low physical health status was associated with high mortality in elderly ESRD patients and especially physical functioning was the important factor associated with death.

Potential impact:

Because the physical performance of elderly ESRD patients could be an important prognostic predictor related to survival, attention to QOL and monitoring of physical functioning are essential for elderly ESRD patients.

INTRODUCTION

Survival of end-stage renal disease (ESRD) patients has improved due to recent advances in dialysis. However, most ESRD patients still have an impaired quality of life (QOL) compared with the general population [1]. QOL is associated with mortality in ESRD patients [1, 2]. Therefore, QOL assessment is important for predicting mortality and hospitalization of patients with ESRD [3, 4].

QOL is defined as an individual's perceived physical and mental health status. In general, ESRD patients have low physical performance and activity, and experience more anxiety and depression than the general population [5–7]. Studies have shown that dialysis patients with poor physical function have a lower survival rate than those with good physical activity [5, 8]. Poor mental health status has also been reported as a risk factor for hospitalization and death in ESRD patients [9, 10]. Several studies have investigated racial differences in QOL among dialysis patients [11–13]. Lopes et al. [14] reported that African Americans showed higher QOL scores for all three components [mental component summary (MCS), physical component summary (PCS) and kidney disease component summary (KDCS)] than whites, and Asians had higher PCS scores than whites. A Korean cohort study also demonstrated a relationship between QOL and mortality in patients with ESRD [15]. Studies on QOL have yielded inconclusive findings regarding the comparative impact of different dialysis modalities on QOL domains [16–18]; however, understanding the influence of modality on QOL is an important factor in modality selection.

The elderly population has a high prevalence of chronic diseases such as hypertension, diabetes, heart disease and lung disease [19]. Decreased muscle mass reduces their physical activity compared with the general population [20–22]. In addition, elderly dialysis patients have more cognitive impairment than younger people [23–26]. The combined physical and mental health problems may decrease QOL in the elderly [19].

It is important to investigate the impact of QOL on survival as the number of elderly patients with ESRD is increasing. However, the relationship between QOL and long-term prognosis has not been sufficiently studied specifically in elderly ESRD patients. Comparative studies on the QOL according to the dialysis modality in elderly dialysis patients are also limited [27, 28]. We aimed to analyze the association between QOL and mortality in elderly ESRD patients and clarify which QOL domains could affect patient deaths.

MATERIALS AND METHODS

Study design and participants

The Clinical Research Center for ESRD (CRC for ESRD) cohort is a nationwide, multicenter, web-based, prospective cohort of ESRD patients undergoing dialysis in 31 hospitals in South Korea (NCT00931970). The study cohort has been previously described [29, 30]. The current study included 492 patients with ESRD, from the CRC for ESRD cohort study, who had newly initiated dialysis, completed QOL questionnaires, and were 65 years or older between July 2009 and June 2015 (Fig. 1). These patients were followed up until December 2019. Dialysis modality was defined as the modality 3 months after the first dialysis or the modality was defined at dialysis initiation if death occurred before 3 months. Informed consent was obtained from all patients and they participated voluntarily. The study protocol was approved by the Institutional Review Board of each center, and the study was conducted in accordance with the 2013 Declaration of Helsinki.

Figure 1: Study inclusion flow chart for 5096 patients with ESRD (flow diagram of the study protocol).

Data collection

All data were registered in a web-based CRC for ESRD database. They were selectively extracted for analysis in this study. The baseline information collected included age, sex, height, weight, body mass index, primary renal disease, comorbid disease, modified Charlson comorbidity index (mCCI) and laboratory data. The laboratory data are the value at the start of dialysis. The mCCI was calculated for each patient at dialysis initiation [31, 32]. The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease study group equation [33]. The main outcome was the survival rate of elderly ESRD patients after dialysis initiation.

Survey instruments

To evaluate the QOL of ESRD patients, we used the Korean version of the Kidney Disease Quality of Life Short Form 1.3 (KDQOL-SF 1.3), a self-reported specific questionnaire [34]. Its validity of QOL assessment using KDQOL-SF 1.3 in patients with ESRD has been previously confirmed [35]. Information about the QOL was gathered at 3 months after dialysis initiation. This questionnaire consists of 80 items in 19 domains plus 1 separate. The questionnaire survey takes approximately 15 min to complete, making it suitable for use in clinical settings. Each domain is scored on a scale of 0–100, with higher scores reflecting a better QOL.

KDQOL is divided into a KDCS and Short Form (SF)-36. The score of the ESRD-targeted items is aggregated into the KDCS score. The SF-36 score, which evaluates overall health status, is the sum of the PCS and MCS scores. The PCS includes items related to physical functioning, physical roles, pain and general health, while the MCS includes items related to emotional roles, emotional well-being, emotional energy and social functioning.

Statistical analysis

We compared sociodemographic, clinical and biochemical data using Pearson's chi-square or Fisher's exact test for categorical variables and the Student's t-test for continuous variables. Continuous variables were expressed as the mean and standard deviation. Mortality analysis was performed using the Kaplan–Meier survival graph. We performed multivariable Cox analysis, adjusting for age, sex, mCCI, albumin, creatinine, C-reactive protein (CRP), uric acid and intact parathyroid hormone (PTH). The factors associated with the PCS score were evaluated with linear regression analysis and the significantly different factors were included in the multivariable analysis. Statistical analysis was performed using IBM SPSS Statistics software (version 22.0; IBM Corp., Armonk, NY, USA) and R software (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org). P-values of <.05 were considered statistically significant.

RESULTS

Baseline characteristics

Before conducting the original analysis, baseline characteristics were compared between patients who completed questionnaires (CQ) and those who did not complete questionnaires (non-CQ) to examine potential biases (Supplementary data, Table S1). The Kaplan–Meier curve revealed that there was no difference in survival rate between the CQ and non-CQ groups (P = .242, Supplementary data, Fig. S1). Univariable and multivariable Cox regression analyses showed comparable mortality between two groups (Supplementary data, Table S2). It suggests that the enrolled patients with the questionnaire survey were not biased from the total patients in terms of mortality.

The baseline characteristics of the enrolled patients are summarized in Table 1. The mean age was 74.3 years and 308 (62.6%) were male. The primary causes of ESRD were diabetes in 300 (61%) and hypertension in 77 (15.7%). At the start of dialysis, the mean eGFR was 7.8 ± 3.4 mL/min/1.73 m2. Of the patients, 378 (77.0%) were on hemodialysis (HD) and 113 (23.0%) were on peritoneal dialysis (PD). The patients were allocated to lower or higher score groups according to the four QOL domains of SF-36, ESRD-targeted items, PCS and MCS.

Table 1: Baseline characteristics according to PCS and MCS.

		PCS		MCS		
Variable	Overall (n = 492)	Lower group (n = 247)	Higher group (n = 245)	P-value	Lower group (n = 246)	Higher group (n = 246)	P-value	
Age (years)	74.3 ± 6.8	75.8 ± 6.8	72.9 ± 6.4	<.001	74.6 ± 6.7	74.0 ± 6.9	.318	
Sex (% male)	308 (62.6)	139 (56.3)	169 (69.0)	.004	153 (62.2)	155 (63.0)	.852	
BMI (kg/m2)	23.2 ± 3.5	23.0 ± 3.7	23.3 ± 3.2	.396	23.1 ± 3.5	23.3 ± 3.5	.524	
Dialysis type								
 HD	378 (77.0)	193 (78.1)	185 (75.8)	.592	190 (77.2)	188 (76.7)	.915	
 PD	113 (23.0)	54 (21.9)	59 (24.2)		56 (22.8)	57 (23.3)		
Primary renal disease, n (%)								
 Diabetes	300 (61.0)	161 (65.2)	139 (56.7)	.091	151 (61.4)	149 (60.6)	.451	
 Hypertension	77 (15.7)	39 (15.8)	38 (15.5)		33 (13.4)	44 (17.9)		
 Glomerulonephritis	38 (7.7)	13 (5.3)	25 (10.2)		19 (7.7)	19 (7.7)		
 Others	77 (15.7)	34 (13.8)	43 (17.6)		43 (17.5)	34 (13.8)		
Comorbid conditions, n (%)								
 Diabetes	322 (65.5)	173 (70.0)	149 (60.8)	.032	163 (66.3)	159 (64.6)	.705	
 Congestive heart failure	84 (17.1)	50 (20.2)	34 (13.9)	.061	43 (17.5)	41 (16.7)	.811	
 Coronary artery disease	100 (20.3)	64 (25.9)	36 (14.7)	.002	55 (22.4)	45 (18.3)	.263	
 Peripheral vascular disease	55 (11.2)	41 (16.6)	14 (5.7)	<.001	29 (11.8)	26 (10.6)	.668	
 Arrhythmia	17 (3.5)	10 (4.1)	7 (2.9)	.469	11 (4.5)	6 (2.4)	.217	
 Cerebrovascular disease	60 (12.2)	44 (17.8)	16 (6.5)	<.001	38 (15.5)	22 (8.9)	.028	
 Chronic lung disease	40 (8.1)	23 (9.3)	17 (6.9)	.336	25 (10.2)	15 (6.1)	.099	
 Peptic ulcer disease	35 (7.1)	20 (8.1)	15 (6.1)	.394	13 (5.3)	22 (8.9)	.115	
 Moderate or severe liver disease	15 (3.1)	5 (2.0)	10 (4.1)	.185	7 (2.9)	8 (3.3)	.793	
 Connective tissue disease	88 (17.9)	43 (17.4)	45 (18.4)	.782	43 (17.5)	45 (18.3)	.814	
 Malignancy	42 (8.5)	21 (8.5)	21 (8.6)	.978	22 (8.9)	20 (8.1)	.747	
mCCI	6.36 ± 2.05	6.78 ± 2.07	5.93 ± 1.94	<.001	6.27 ± 2.13	6.14 ± 1.94	.020	
Smokers, n (%)								
 Non-smoker	263 (54.2)	139 (57.4)	124 (51.0)	.255	141 (57.8)	122 (50.6)	.252	
 Smoker	40 (8.3)	21 (8.7)	19 (7.8)		20 (8.2)	20 (8.3)		
 Ex-smoker	182 (37.5)	82 (33.9)	100 (41.2)		83 (34.0)	99 (41.1)		
Systolic BP (mmHg)	143.1 ± 21.9	142.5 ± 22.2	143.8 ± 21.6	.517	143.6 ± 21.1	142.6 ± 22.7	.634	
Diastolic BP (mmHg)	75.1 ± 12.4	74.3 ± 12.1	75.9 ± 12.7	.158	75.0 ± 12.2	75.3 ± 12.6	.817	
Residual urine volume (n = 298), n (%)								
 ≥100 mL/day	252 (84.6)	130 (83.3)	122 (85.9)	.538	119 (83.2)	133 (85.8)	.537	
 <100 mL/day	46 (15.4)	26 (16.7)	20 (14.1)		24 (16.8)	22 (14.2)		
Laboratory data								
 Hemoglobin (g/dL)	9.2 ± 1.5	9.2 ± 1.5	9.1 ± 1.5	.662	9.3 ± 1.5	9.1 ± 1.4	.186	
 Albumin (g/dL)	3.4 ± 0.6	3.3 ± 0.7	3.5 ± 0.5	.008	3.3 ± 0.7	3.4 ± 0.5	.051	
 Blood urea nitrogen (mg/dL)	81.6 ± 35.4	78.1 ± 37.7	85.1 ± 32.7	.028	78.0 ± 35.9	85.2 ± 34.7	.024	
 Creatinine (mg/dL)	7.7 ± 3.0	7.3 ± 2.8	8.1 ± 3.2	.001	7.5 ± 3.0	7.8 ± 3.0	.135	
 eGFR (mL/min/1.73 m2)	7.8 ± 3.4	8.3 ± 3.5	7.7 ± 3.2	.033	8.2 ± 3.4	7.8 ± 3.4	.132	
 CRP (mg/dL)	0.5 (0.1–2.0)	0.6 (0.1–2.4)	0.4 (0.1–1.5)	.014	0.6 (0.1–2.3)	0.4(0.1–1.5)	.125	
 Total cholesterol (mg/dL)	150.0 ± 44.1	152.4 ± 46.4	147.5 ± 41.7	.255	153.7 ± 48.5	146.4 ± 38.1	.089	
 Triglycerides (mg/dL)	119.9 ± 56.2	123.7 ± 58.6	116.1 ± 53.6	.196	124.6 ± 59.9	115.7 ± 52.5	.131	
 LDL (mg/dL)	84.4 ± 34.4	86.5 ± 35.9	82.0 ± 32.6	.226	86.2 ± 37.1	82.7 ± 31.6	.344	
 Ferritin (ng/dL)	213.0 (116.1–373.2)	243.8 (112.8–395.5)	190.7 (116.4–357.9)	.126	243.3 (119.0–396.4)	196.6 (114.1–349.9)	.091	
 Calcium (mg/dL)	8.0 ± 0.9	8.0 ± 0.9	8.0 ± 1.0	.853	8.0 ± 0.9	8.0 ± 1.0	.592	
 Phosphate (mg/dL)	5.2 ± 1.6	5.2 ± 1.7	5.2 ± 1.4	.960	5.1 ± 1.7	5.2 ± 1.5	.258	
 Uric acid (mg/dL)	7.8 ± 2.5	7.7 ± 2.7	7.9 ± 2.4	.294	7.6 ± 2.5	8.0 ± 2.5	.049	
 Intact PTH (mg/dL)	227.6 ± 179.2	210.0 ± 163.2	246.5 ± 193.6	.031	225.7 ± 190.4	229.4 ± 168.5	.397	
Data are presented as mean ± standard deviation, median (interquartile range) or n (%).

BMI, body mass index; BP, blood pressure.

Table 1 compares the PCS and MCS groups by dividing them into median values. The higher PCS group included younger patients. Comorbidity evaluated by the mCCI was significantly less common in the higher score PCS and MCS groups (P < .001 and P = .020, respectively). Among the laboratory data, albumin, blood urea nitrogen, creatinine, eGFR, CRP and intact PTH differed between the two PCS groups. The baseline characteristics according to SF-36 and ESRD-targeted items are shown in Supplementary data, Table S3.

Survival rate in each QOL domain

The overall mean follow-up duration after dialysis initiation was 67.3 months. The Kaplan–Meier curve analysis revealed that the higher SF-36 and PCS groups had significantly better survival rates (P = .017 and P < .001, respectively), while the ESRD-targeted items and MCS score had no difference in survival rate (Fig. 2).

Figure 2: Kaplan–Meier curve mortality analysis according to QOL domain.

Univariable and multivariable Cox regression analyses of QOL are shown in Table 2. In the univariable analysis, higher SF-36 and PCS scores were significantly associated with better survival [SF-36: hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.57–0.95; P = .018, PCS: HR 0.56; 95% CI 0.43–0.72; P < .001]. Multivariable Cox regression analysis confirmed that only the PCS score was an independent factor for patient death (Model 3: HR 0.71; 95% CI 0.52–0.97; P = .031). Linear regression analysis showed that age, sex, mCCI, albumin and intact PTH were associated with PCS (Table 3).

Table 2: Associations of QOL domains and mortality in Cox regression analysis.

		Multivariable analysis	
	Univariable analysis	Model 1	Model 2	Model 3	
	HR (95% CI)	P-value	HR (95% CI)	P-value	HR (95% CI)	P-value	HR (95% CI)	P-value	
SF-36									
 <51.3 (reference)	1		1		1		1		
 ≥51.3	0.74 (0.57–0.95)	0.018a	0.81 (0.63–1.05)	0.105	0.88 (0.68–1.14)	0.348	0.85 (0.63–1.15)	0.289	
ESRD-targeted item									
 <68.1 (reference)	1		1		1		1		
 ≥68.1	0.79 (0.62–1.02)	0.066	0.84 (0.65–1.07)	0.159	0.88 (0.68–1.13)	0.315	0.84 (0.62–1.13)	0.239	
PCS									
 <51.5 (reference)	1		1		1		1		
 ≥51.5	0.56 (0.43–0.72)	<0.001a	0.63 (0.48–0.82)	<0.001a	0.70 (0.53–0.91)	0.008a	0.71 (0.52–0.97)	0.031a	
MCS									
 <51.0 (reference)	1		1		1		1		
 ≥51.0	0.92 (0.72–1.18)	0.516	1.01 (0.78–1.30)	0.946	1.07 (0.83–1.38)	0.613	1.07 (0.79–1.44)	0.661	
a P for trend.

Model 1: adjusted for age and sex.

Model 2: Model 1 + mCCI.

Model 3: Model 2 + albumin, creatinine, CRP, uric acid and intact PTH.

Table 3: Factors: associated with PCS in the linear regression model.

	Univariable analysis	Multivariable analysis	
	B (SE)	β	P-value	B (SE)	β	P-value	
Age	−0.77 (0.14)	−0.24	<.001a	−0.45 (0.16)	−0.14	.005a	
Sex	7.39 (1.99)	0.17	<.001a	6.63 (2.13)	−0.15	.002a	
BMI	0.26 (0.28)	0.04	.350				
mCCI	−2.87 (0.46)	−0.27	<.001a	−2.00 (0.54)	−0.19	.000a	
Albumin	6.01 (1.64)	0.16	<.001a	4.52 (1.69)	0.13	.008a	
Creatinine	1.27 (0.32)	0.17	<.001a	0.35 (0.35)	0.05	.314	
CRP	−0.11 (0.09)	−0.06	.209				
Uric acid	0.56 (0.39)	.07	0.151				
Intact PTH	0.02 (0.01)	.14	0.006a	0.01 (0.01)	0.08	.085	
a P for trend.

BMI, body mass index; SE, standard error.

Survival rate in each PCS item

The four PCS score items were compared between the lower and higher PCS groups. All PCS item scores were significantly higher in the higher PCS group (all P < .05, Fig. 3).

Figure 3: Comparison of each PCS item score.

The Kaplan–Meier curve analysis revealed that higher physical functioning and pain scores showed better survival than in the corresponding lower groups (P < .001 and P = .008, respectively, Fig. 4). The univariable and multivariable Cox regression analyses of PCS items are shown in Table 4. Of the four PCS items, physical functioning and pain scores were significantly associated with patient death (physical functioning: HR 0.52; 95% CI 0.40–0.67; P < .001, pain: HR 0.71; 95% CI 0.56–0.92; P = .008) in the univariable analysis. Multivariable Cox regression analysis showed physical functioning to be an independent risk factor for survival (Model 3: HR 0.67; 95% CI 0.48–0.93; P = .017).

Figure 4: Kaplan–Meier curve mortality analysis according to PCS item.

Table 4: Associations of PCS items and mortality in Cox regression analysis.

		Multivariable analysis	
	Univariable analysis	Model 1	Model 2	Model 3	
	HR (95% CI)	P-value	HR (95% CI)	P-value	HR (95% CI)	P-value	HR (95% CI)	P-value	
Physical functioning									
 <70.0 (reference)	1		1		1		1		
 ≥70.0	0.52 (0.40–0.67)	<.001a	0.58 (0.44–0.76)	<.001a	0.62 (0.47–0.82)	<.001a	0.67 (0.48–0.93)	.017a	
Physical roles									
 <25.0 (reference)	1		1		1		1		
 ≥25.0	0.78 (0.61–1.00)	.051	0.79 (0.62–1.02)	.069	0.93 (0.72–1.21)	.606	0.93 (0.69–1.26)	.641	
Pain									
 <67.5 (reference)	1		1		1		1		
 ≥67.5	0.71 (0.56–0.92)	.008a	0.80 (0.62–1.03)	.088	0.84 (0.65–1.09)	.191	0.96 (0.70–1.30)	.769	
General health									
 <35.0 (reference)	1		1		1		1		
 ≥35.0	0.98 (0.76–1.26)	.846	0.92 (0.71–1.18)	.496	1.08 (0.83–1.40)	.581	1.13 (0.84–1.53)	.414	
a P for trend.

Model 1: adjusted for age and sex.

Model 2: Model 1 + mCCI.

Model 3: Model 2 + albumin, creatinine, CRP, uric acid and intact PTH.

DISCUSSION

We investigated the impact of QOL on the long-term survival of elderly ESRD patients using enrollees from a Korea nationwide prospective cohort. Among the QOL domains, a low PCS score was associated with a lower survival rate. The effect of PCS on survival was still significant after adjusting for confounding factors. Elderly ESRD patients with high comorbidity levels or low albumin levels had lower PCS scores. Among the PCS items, only physical functioning was significantly associated with mortality. This study suggests that the physical component could be an important predictor for the survival of elderly ESRD patients.

The impact of QOL has not been sufficiently studied specifically in elderly ESRD patients. As the number of aging people is increasing worldwide, more attention should be paid to elderly ESRD patients in clinical practice. It is important to study their prognostic factors regarding survival. Several studies have investigated QOL in elderly patients with ESRD [36–39]. In a cross-sectional study, Shah et al. [40] found a lower QOL in elderly ESRD patients aged >75 years managed on dialysis than in those with comprehensive conservative care. In addition, there were studies on changes in QOL before and after dialysis in elderly patients [41–43]. Trbojevic et al. [44] reported that elderly patients on PD had a worse appetite and mood than did younger adults. In the present study, the QOL domains PCS and SF-36 showed significant demographic differences in the score index, but ESRD-targeted items showed no difference according to score classification. For example, the mean age was higher in the lower PCS and SF-36 groups, whereas there was no difference between the lower and higher groups in the ESRD-targeted area. The ESRD-targeted items included questions on thoughts about kidney disease, its impact on daily life, sexual function, sleep problems, and the patient's satisfaction with dialysis staff encouragement and social support. For that reason, it appears that the ESRD-related area has few age-related differences in elderly patients.

There are several studies of QOL in elderly ESRD patients, but there are few on the relationship between QOL and prognosis. The associations between mortality and traditional risk factors for death such as body mass index, lipid abnormalities and hypertension differ between the elderly and general population [45–48]. Similarly, the association between QOL and mortality may be different in elderly ESRD patients compared with the general ESRD patient population. A prospective study in New Zealand investigating the health-related QOL of elderly ESRD patients [49] reported that initial disability and QOL could be useful in predicting disability after 12 months. Recently, a randomized controlled trial analyzed the relationship between the QOL domains and 2-year mortality in three groups including patients aged <65 years, 65–74 years and >75 years. Among the QOL domains, physical functioning, emotional health and social functioning were associated with 2-year mortality independent of age in HD patients [50]. Bastos et al. [51] reported that some QOL components are associated with mortality in dialysis patients and suggested that timely intervention measures aiming to enhance the QOL are an essential component to improving the dialysis patients’ mortality. However, this study did not focus on elderly dialysis patients or evaluate long-term mortality. Unlike previous studies, our study analyzed a cohort of elderly patients with a mean age of 74.3 years and investigated the association between QOL domains and long-term prognosis. We identified significant results from several QOL domains and long-term mortality data collected through the Korea nationwide prospective ESRD cohort. The study showed that low PCS and SF-36 scores were associated with high mortality, while the MCS and ESRD-targeted item scores showed no correlation. Multivariable Cox regression analysis confirmed that only PCS was a risk factor for mortality in this study.

Previous studies have shown that PCS is a prognostic factor in patients with ESRD [51–53]. This is consistent with a previous prospective cohort study conducted in Taiwan, which reported that only a poor physical dimension in QOL domains was a strong predictor of mortality in 888 stable HD patients. Conversely, psychological depression does not predict mortality [8]. Pei et al. [54] reported that a lower PCS was associated with an increased mortality risk, whereas MCS was not. Dobronravov et al. [52] reported that PCS was independently related to all-cause mortality in 238 HD patients aged 18–64 years. Similarly, a Korean cohort study of 568 HD patients demonstrated that a low PCS correlates significantly with overall mortality [15]. Our study focused on the elderly patient over 65 years with ESRD and also showed that PCS is an important factor in the prognosis of elderly ESRD patients, consistent with previous studies.

The physical functioning component includes questions about strenuous and moderate activity, stair climbing, 200 m walking, 50 m walking and self-dressing. This item seems to be related to the prognosis because it evaluates physical performance. In the present study, patients with lower PCS are characterized by older age, more comorbid conditions and lower albumin. The linear regression analysis confirmed the correlation between these factors and PCS. Therefore, PCS may be regarded as a comprehensive marker reflecting not only chronological age but also coexisting diseases and nutritional status. Furthermore, our study suggests that the physical performance of elderly ESRD patients could be an important predictor related to survival. Therefore, evaluation of the patient's physical functioning, and attention to exercise and rehabilitation might improve patient survival.

Despite these strengths, there are several limitations to this study. First, this was an observational study and there may be unmeasured confounding factors. However, we used a nationwide ESRD cohort with a relatively large number of patients and adjusted for several confounding factors. Second, this was a subjective assessment of patients’ physical activity with a questionnaire and subjective evaluations of physical activity may be an under- or overestimate. Third, we only analyzed the association between quality of life at 3 months after initiation of dialysis and prognosis. Further studies of QOL at different time points and its relevance to prognosis are warranted. Fourth, subgroup analysis according to dialysis modality could not be conducted due to insufficient patient number and events. A larger study may suggest that QOL could be a considerable factor in elderly patients when choosing a dialysis modality. Lastly, this study only included a Korean population. Considering international differences in QOL by race, caution should be taken when generalizing and applying our findings to other populations.

In conclusion, we found that low physical health status increases mortality in elderly ESRD patients. Physical function, in particular, was the important factor associated with death. In contrast, mental health status did not show significant association with mortality. This study showed the importance of QOL in elderly ESRD patients’ long-term prognosis. Nephrologists should pay attention to the physical activity of elderly ESRD patients and educate them to improve their physical ability through regular monitoring.

Supplementary Material

sfae241_Supplemental_Files

FUNDING

The study was supported by a grant from the Korean Society of Geriatric Nephrology and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR22C1832 and HI22C1529).

AUTHORS’ CONTRIBUTIONS

Conceptualization: Y.-K.C., J.-H.L., J.-H.C. and Y.-L.K. Data curation: Y.H.J., H.-Y.J., J.-Y.C., S.-H.P., C.-D.K. and Y.-L.K. Investigation : J.-H.L., J.-H.C. and Y.-L.K. Formal analysis: Yu Y.-K.C., J.-H.L. and Y.-n.J. Funding acquisition: J.-H.C. Methodology: Y.H.J., J.-H.L., H.-Y.J., J.-Y.C., S.-H.P., C.-D.K. and J.-H.C. Visualization: Y.-K.C. and Y.-n.J. Writing—original draft: Y.-K.C. Writing—review and editing: Y.-K.C., J.-H.C. and Y.-L.K.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on reasonable request from the corresponding authors. The data are not publicly available due to privacy or ethical restrictions.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests
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