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10.1080/0886022X.2024.2396448
2396448
Version of Record
Research Article
Hemodialysis and Peritoneal Dialysis
Survival rates in comprehensive conservative care compared to dialysis therapy in elderly end-stage kidney disease patients: a propensity score analysis
K. Noppakun et al.
https://orcid.org/0000-0002-5783-8766
Noppakun Kajohnsak ab
https://orcid.org/0000-0002-7913-2799
Tantraworasin Apichat cd
https://orcid.org/0000-0001-9023-0923
Khorana Jiraporn cd
https://orcid.org/0000-0003-1100-7171
Nochaiwong Surapon be
https://orcid.org/0000-0002-4551-393X
Vongsanim Surachet a
https://orcid.org/0000-0002-1569-4581
Narongkiatikhun Phoom a
https://orcid.org/0000-0002-3967-2918
Pongsuwan Karn a
https://orcid.org/0000-0002-3885-9377
Kusirisin Prit a
https://orcid.org/0009-0006-5346-5305
Manoree Chalongrat f
https://orcid.org/0000-0001-7927-1425
Ruengorn Chidchanok be
a Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
b Faculty of Pharmacy, Pharmacoepidemiology and Statistics Research Center, Chiang Mai University, Chiang Mai, Thailand
c Faculty of Medicine, Clinical Epidemiology and Clinical Statistic Center, Chiang Mai University, Chiang Mai, Thailand
d Department of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
e Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand
f Transplant and Dialysis Unit, Nursing Medicine Section, Maharaj Nakorn Chiang Mai Hospital, Chiang Mai, Thailand
Supplemental data for this article is available online at https://doi.org/10.1080/0886022X.2024.2396448

CONTACT Chidchanok Ruengorn chidchanok.r@cmu.ac.th Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, 50200, Thailand
30 8 2024
2024
30 8 2024
46 2 239644825 4 2024
10 8 2024
20 8 2024
KnowledgeWorks Global Ltd.29 8 2024
published online in a building issue29 8 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
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 (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Initiating dialysis therapy in elderly patients with end-stage kidney disease (ESKD) is a challenging decision. We aimed to examine the mortality rates among elderly patients who underwent hemodialysis, peritoneal dialysis, or comprehensive conservative care. This retrospective cohort study included elderly patients (≥70 years) with ESKD who selected their treatment options from January 2008 to December 2018. Patients were categorized into three groups: hemodialysis, peritoneal dialysis, and comprehensive conservative care. The outcome of interest was all-cause mortality analyzed using flexible parametric survival models. Propensity score analysis with inverse probability treatment weighting technique was performed, incorporating age, Charlson Comorbidity Index score, and estimated glomerular filtration rate. The study included 719 elderly ESKD patients with mean age of 78.2 ± 4.9 years, 52.3% were male, and 60.1% died during the median follow-up period of 22.1 months. In a fully adjusted model, patients receiving comprehensive conservative care (n = 50) had higher mortality rates than those receiving hemodialysis (n = 317) (adjusted hazard ratio [HR] 5.60; 95% CI 2.26–13.84, p < 0.001). However, patients who received peritoneal dialysis (n = 352) had a similar mortality rate when compared to those who received hemodialysis (adjusted HR 1.38; 95% CI 0.78–2.44, p = 0.275). The higher mortality rate in the comprehensive conservative care group remained significantly higher than in the hemodialysis group among patients aged ≥80 years (adjusted HR 4.97; 95% CI 1.32–18.80, p = 0.018). Among elderly patients (≥70 years), treatment with dialysis was associated with longer survival rates. This survival advantage persisted in patients aged ≥80 years who chose hemodialysis or peritoneal dialysis over comprehensive conservative care.

Keywords

Advance care planning
comprehensive conservative management
geriatric dialysis
inverse probability treatment weighting (IPTW)
kidney replacement therapy (KRT)
shared decision-making
Chongkolneenithi Foundation This research was supported by grants from the Chongkolneenithi Foundation. The funder had no role in determining the content of the manuscript.
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pmcIntroduction

It is challenging to select treatment options for elderly patients who have end-stage kidney disease (ESKD). While kidney replacement therapy (KRT) is considered the gold standard therapy for young ESKD patients, it can have a significantly negative impact on the daily lives of elderly patients. Nevertheless, KRT may provide some degree of symptom alleviation and occasionally prolong survival in elderly patients [1]. Selecting the most appropriate treatment modality in elderly patients necessitates consideration of various factors including the patient’s life expectancy, overall health status, personal and family preferences, and goals of care. Predicting the likelihood of death before initiating dialysis is helpful when deciding whether to perform dialysis in elderly patients [2].

Hemodialysis (HD) is the primary treatment method for elderly ESKD patients worldwide [3]. HD requires regular visits to a dialysis center and necessitates the creation of permanent vascular access, which entails a surgical intervention. Given the high burden of vascular disease, obtaining permanent vascular access in the elderly is challenging [4]. Additionally, elderly patients have a low tolerance for the rapid changes in hemodynamic and fluid status that occur during HD. Peritoneal dialysis (PD) can be performed at home, giving elderly patients greater flexibility. However, it requires proper manual dexterity, cognitive ability, and a clean environment. For elderly patients who are unable to perform their PD, assisted PD is an option [5]. Despite this, self-reported quality of life and satisfaction often decline significantly after dialysis initiation [6].

Comprehensive conservative care (CCC) [7] is an alternative to KRT. It emphasizes shared decision-making and advance care planning, as well as holistic, patient-centered treatment to optimize quality of life without initiating KRT. CCC may be appropriate for elderly patients who have a limited life expectancy or significant comorbidities. The estimation of survival rates for CCC is complex because, unlike dialysis, there is no distinct initiation point. Using a fixed glomerular filtration rate (GFR) level as the starting point, there is no difference in survival rates between elderly patients with multiple comorbidities receiving CCC and those receiving dialysis [1,8]. In contrast, dialysis treatment in elderly patients resulted in a notably higher survival rate during the first three years [9]. Several studies examined the mortality rates of elderly ESKD patients who undergo dialysis or choose CCC [10], most of them only compared the two dialysis modalities or dialysis and CCC. There is a lack of data comparing all three modalities in the same cohort. Additionally, research investigating the mortality in patients who receive CCC is vulnerable to lead-time bias, while analyses focusing on a specific point of GFR in dialysis patients may be susceptible to immortal time bias. The objective of this study is to compare the mortality rates among elderly ESKD patients who undergo HD, PD, or comprehensive conservative management using a propensity score analysis.

Methods

Study design and participants

We conducted a retrospective cohort study at three dialysis sites in Chiang Mai, Thailand. All ESKD patients aged ≥70 years who selected one of the dialysis modalities or opted for CCC between January 2008 and December 2018 were included. Patients who did not have baseline or follow-up data, underwent a kidney transplant, switched from another dialysis modality, or received both HD and PD were excluded, as were those who were not suitable candidates for dialysis therapy or lacked decision-making capacity. Dialysis suitability was determined at the physician’s discretion, ensuring all patients were eligible for at least one of the available treatment modalities. The cohort entry date was the date of dialysis initiation in patients who chose dialysis therapy, and the first date with estimated GFR (eGFR) <15 mL/min/1.73 m2 for patients who chose CCC.

We collected baseline data at the time of cohort entry. The sample size was determined by the number of new patients who initiated dialysis or received CCC during the study period. Nonetheless, the sample size was calculated by assuming a mortality rate of 50% after three years in elderly patients receiving HD or PD, and a hazard ratio of 2.0 for mortality in patients receiving CCC [8]. With a statistical power of 80% and a type I error rate of 5%, 66 deaths were estimated from a total of 131 patients. To comply with privacy standards, all information was encrypted. Patient identification information was removed. A data analysis was performed by the author at Chiang Mai University. Patient consent was waived due to the retrospective nature of the study and the use of anonymous records. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline [11] and guideline for reporting propensity score analysis [12]. The Ethical Committee of Chiang Mai University approved this study (MED-2562-06375; TCTR20200102002), which followed the Declaration of Helsinki.

Exposures and outcomes

The exposure of interest was treatment modalities for ESKD: HD, PD, and CCC. Patients and their families decided whether to undergo dialysis. The healthcare team simply offered information about the risks and benefits of each modality. Shared decision-making was implemented. Data regarding the potential burdens, quality of life, survival benefits, and reimbursement scheme of each treatment modality were provided. The patient and caregivers had sufficient time to consider and discuss the various options. Some patients may require multiple discussion sessions to reach a decision. No patient who preferred dialysis was denied this therapeutic option, though they had a poor prognosis. Patients selecting CCC were provided with treatment and counseling and were regularly monitored by nephrologists, qualified nurses, and dietitians. The CCC group received comprehensive symptom assessments and management to enhance health-related quality of life. Additionally, they participated in advance care planning discussions and received emotional support from both patients and their families. Patients were censored if they lost to follow-up, changed dialysis modalities, or switched to CCC. If patients receiving CCC decided to start dialysis of any modalities, they would be assigned to a dialysis treatment group. The known vital status refers to the determination of whether a patient was alive or deceased. It was computed by subtracting the number of patients lost to follow-up from the total number of patients.

The outcome of interest was the time to all-cause mortality which was determined through medical records and national vital statistics. Patients were followed from the entry date until they died, changed treatment method, or the study terminated.

Measurement of covariates

Variables collected at the time of study entry were selected as potential predictors including age, sex, body mass index (BMI), comorbidities, hemoglobin levels, serum albumin levels, and eGFR which was calculated using the CKD-Epidemiology 2009 creatinine equation [13]. The Charlson Comorbidity Index (CCI) score was derived from patient-level data obtained from medical records. This data included details on comorbidities including diabetes, myocardial infarction, heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic obstructive pulmonary disease, connective tissue disease, peptic ulcer, liver disease, cancer, and human immunodeficiency virus infection [14]. Propensity score analysis was performed to address selection bias caused by an unequal distribution of baseline covariates among the three exposure groups.

Statistical analysis

Patient information was summarized using standard descriptive statistics. The baseline characteristics were compared between groups using the analysis of variance or Fisher’s exact test, as appropriate. The propensity score weighting was conducted using multinomial logistic regression with the inverse probability treatment weighting (IPTW) technique. Three predictors, namely age, CCI score, and eGFR at study entry were selected into the logistic regression model. The propensity score analysis was performed using R software version 4.3.2 which was executed through the STATA statistical package. The average treatment effect (ATE) was used as the estimand of interest, with the effect size of the mean as the stopping rule.

Survival analysis was performed using Kaplan-Meier estimates and the log-rank test to compare observed differences. The Cox proportional hazards model was employed to assess survival. Flexible parametric survival analysis was applied if the proportional hazard assumption was violated. The hazard ratio and 95% confidence interval (CI) for mortality were calculated before and after the implementation of IPTW. A standardized survival curve was illustrated for each model. The sensitivity analyses were performed by (1) excluding patients who died within one month of follow-up and (2) restricting to those who had eGFR <10 mL/min/1.73 m2 at the time of cohort entry. To account for potential confounders, the models incorporated factors related to mortality. The analyses were conducted using STATA, version 16.0 (StataCorp LLC, College Station, TX, USA) and R software version 4.3.2. A two-tailed p-value <0.05 was used to determine significance.

Results

Patient characteristics

We identified 743 ESKD patients aged ≥70 years who chose HD, PD, or CCC as their treatment. After excluding patients without baseline or follow-up data, the final cohort comprised of 719 patients (Figure 1). Among them, 317 (44.0%) chose HD, 352 (49.0%) chose PD, and 50 (7.0%) chose CCC.

Figure 1. A Flow diagram illustrating participants and outcomes.

The baseline characteristics of 719 patients, categorized by their treatment modalities, are shown in Table 1. The mean age was 78.2 ± 4.9 years, and 52.3% were male, with a mean BMI of 20.6 ± 3.5 kg/m2. Diabetes was the most prevalent comorbidity. The three treatment groups differed in age, BMI, CCI score, eGFR, and serum albumin. Patients in the HD group had significantly higher serum albumin than the other two groups (p < 0.001). Since age, CCI score, and eGFR, showed a significant imbalance across the three treatment groups, they were chosen as parameters to compute the propensity score. The values of these three parameters both before and after IPTW and the standardized mean difference are shown in Table 2. Following IPTW, there was a substantial decrease in the standardized mean difference, although certain values remained >0.1. Furthermore, there remained a substantial disparity in the CCI score between the HD and PD groups after IPTW (p = 0.007). Although there was no evidence of violation in propensity score distributions, indicating that each patient had a non-zero probability of receiving each treatment (Supplementary Figure 1), the graphical assessment did not demonstrate that the three treatment groups were well-balanced after IPTW (Supplementary Figures 2 and 3). Consequently, we decided to incorporate age and CCI score as covariates in the survival models to calculate the adjusted hazard ratio.

Table 1. Characteristics and laboratory values of patients categorized by treatment modalities for end-stage kidney disease.

Characteristic	Overall (n = 719)	Opted for hemodialysis (n = 317)	Opted for peritoneal dialysis (n = 352)	Opted for comprehensive conservative care (n = 50)	p-Value†	
Age, years	78.2 ± 4.9	79.9 ± 4.6	76.5 ± 4.5	79.1 ± 5.5	<0.001a	
Male sex	376 (52.3)	159 (50.2)	190 (54.0)	27 (54.0)	0.6b	
Body mass index, kg/m2*	20.6 ± 3.5	21.6 ± 3.4	20.4 ± 3.6	20.9 ± 3.1	0.02a	
Comorbidities	
 Diabetes	272 (37.8)	105 (33.1)	144 (40.9)	23 (46.0)	0.06b	
 Heart failure	182 (25.3)	110 (34.7)	60 (17.1)	12 (24.0)	<0.001b	
 Cerebrovascular disease	130 (18.1)	69 (21.8)	49 (13.9)	12 (24.0)	<0.001b	
 Chronic obstructive pulmonary disease	66 (9.2)	47 (14.8)	16 (4.6)	3 (6.0)	0.01b	
 Peripheral arterial disease	58 (8.1)	36 (11.4)	20 (5.8)	2 (4.0)	0.1b	
 Cancer	32 (4.5)	24 (7.6)	6 (1.7)	2 (4.0)	0.001b	
 Liver disease	17 (2.4)	13 (4.1)	4 (1.2)	0	0.2b	
CCI score	3.9 ± 1.9	4.6 ± 2.2	3.3 ± 1.4	4.0 ± 1.8	<0.001a	
Age-adjusted CCI score	7.3 ± 2.0	8.1 ± 2.6	6.5 ± 1.5	7.4 ± 1.8	<0.001a	
Hemoglobin, g/dL*	9.3 ± 1.8	9.7 ± 1.6	9.2 ± 1.7	9.1 ± 2.3	0.1a	
Serum creatine, mg/dL	7.6 ± 3.8	7.0 ± 2.7	7.9 ± 3.7	6.6 ± 4.9	0.02a	
eGFR at study entry, mL/min/1.73 m2	8.1 ± 3.9	8.2 ± 3.1	7.7 ± 4.4	10.0 ± 3.9	<0.001a	
Serum albumin, g/dL*	3.2 ± 0.6	3.6 ± 0.6	3.1 ± 0.6	3.2 ± 0.6	<0.001a	
eGFR: estimated glomerular filtration rate; CCI: Charlson comorbidity index.

Categorical variables were presented as number (percentage), continuous variables were presented as mean ± standard deviation, or otherwise specified.

†p-Value comparing between the three treatment groups, namely hemodialysis, peritoneal dialysis and comprehensive conservative care.

ap-Value from the analysis of variance.

bp-Value from Fisher’s exact test.

*Percentage of participants who had missing data: 32.4% for body mass index, 35.1% for hemoglobin, 39.7% for serum albumin.

Table 2. Age, Charlson Comorbidity Index score, and estimated glomerular filtration rate in the original and inverse probability treatment weighted samples.

Variable	Original sample	Inverse-probability treatment weighted sample	
Hemodialysis (n = 317)	Peritoneal dialysis (n = 352)	Comprehensive conservative care (n = 50)	Hemodialysis (effective sample size = 233)	Peritoneal dialysis (effective sample size = 280)	Comprehensive conservative care (effective sample size = 22)	
Age, years	79.9 ± 4.6	76.5 ± 4.5	79.1 ± 5.5	78.7 ± 4.9	78.0 ± 4.9	78.8 ± 4.9	
Standardized mean difference (p-value*)	
 Hemodialysis vs. peritoneal dialysis	0.674 (p <0.001)	0.151 (p = 0.1)	
 Hemodialysis vs. comprehensive conservative care	0.145 (p = 0.4)	0.029 (p = 0.9)	
 Peritoneal dialysis vs. comprehensive conservative care	0.529 (p = 0.001)	0.180 (p = 0.3)	
CCI score	4.6 ± 2.2	3.3 ± 1.4	4.0 ± 1.8	4.1 ± 1.9	3.6 ± 1.9	3.9 ± 1.9	
Standardized mean difference (p-value*)	
 Hemodialysis vs. peritoneal dialysis	0.696 (p < 0.001)	0.244 (p = 0.007)	
 Hemodialysis vs. comprehensive conservative care	0.336 (p = 0.02)	0.080 (p = 0.7)	
 Peritoneal dialysis vs. comprehensive conservative care	0.360 (p = 0.009)	0.164 (p = 0.4)	
eGFR at study entry, mL/min/1.73 m2	8.2 ± 3.1	7.7 ± 4.4	10.0 ± 3.9	8.1 ± 3.9	7.9 ± 3.9	8.3 ± 3.9	
Standardized mean difference (p-value*)	
 Hemodialysis vs. peritoneal dialysis	0.115 (p = 0.1)	0.053 (p = 0.5)	
 Hemodialysis vs. comprehensive conservative care	0.465 (p = 0.002)	0.058 (p = 0.7)	
 Peritoneal dialysis vs. comprehensive conservative care	0.580 (p < 0.001)	0.111 (p = 0.5)	
eGFR: estimated glomerular filtration rate; CCI: Charlson comorbidity index.

*p-Values of standardized mean difference.

Survival analyses

During the follow-up period of 22.1 months, 432 (60.1%) patients died, with an incidence rate of 1.44, 3.16, and 2.57 per 100 patient-year in the HD, PD, and CCC groups, respectively. Before (Table 3-Cox regression and Figure 2(A)) and after (Table 3-Cox regression and Figure 2(B)) using age, CCI score, and eGFR for IPTW, it was shown that CCC and PD were associated with an increased risk of all-cause mortality when compared to HD.

Figure 2. Survival probability comparing between dialysis therapy and comprehensive conservative care before and after inverse probability treatment weighting (IPTW): Kaplan-Meier estimates (A) before and (B) after IPTW; flexible parametric methods after IPTW (C) model 1, (D) model 2, (E) model 3, (F) model 4 overall patients, (G) model 4 in patients aged 70–80 years, (H) model 4 in patients aged 80 years or older. Model 1: univariable analysis for treatment modality. Model 2: Model 1 + adjustment with age, sex, and Charlson comorbidity index. Model 3: Model 2 + adjustment with dialysis center, hemoglobin levels, and body mass index. Model 4: Model 3 + adjustment with serum albumin levels.

Table 3. Hazard ratio (HR) of all-cause mortality comparing different treatment modalities with hemodialysis as the reference before and after inverse probability treatment weighting.

 	Before inverse-probability treatment weight (IPTW)	After inverse-probability treatment weight (IPTW)	
HR (95% CI), compared to hemodialysis	p-Value	HR (95% CI), compared to hemodialysis	p-Value	
Cox regression (n = 719)	
 Peritoneal dialysis	2.11 (1.73 − 2.56)	<0.001	2.63 (2.10 − 3.30)	<0.001	
 Conservative comprehensive care	1.69 (1.02 − 2.79)	0.041	3.14 (1.50 − 6.57)	0.002	
Flexible parametric survival model	
Model 1 (n = 719)	
 Peritoneal dialysis	2.13 (1.75 − 2.60)	<0.001	2.67 (2.14 − 3.34)	<0.001	
 Conservative comprehensive care	1.72 (1.04 − 2.83)	0.035	3.20 (1.47 − 6.98)	0.003	
Model 2 (n = 719)	
 Peritoneal dialysis	3.59 (2.90 − 4.46)	<0.001	3.02 (2.35 − 3.88)	<0.001	
 Conservative comprehensive care	2.33 (1.41 − 3.86)	0.001	3.55 (1.62 − 7.74)	0.001	
Model 3 (n = 458)	
 Peritoneal dialysis	2.41 (1.53 − 3. 93)	<0.001	2.20 (1.34 − 3.60)	0.002	
 Conservative comprehensive care	2.15 (1.15 − 4.00)	0.017	3.64 (1.55 − 8.53)	0.003	
Model 4 (n = 414)	
 Peritoneal dialysis	1.49 (0.87 − 2.54)	0.145	1.38 (0.78 − 2.44)	0.275	
 Conservative comprehensive care	3.06 (1.60 − 5.89)	0.001	5.60 (2.26 − 13.84)	<0.001	
Model 4 in patients aged <80 years (n = 302)	
 Peritoneal dialysis	1.24 (0.60 − 2.55)	0.567	1.21 (0.52 − 2.78)	0.663	
 Conservative comprehensive care	3.45 (1.35 − 8.81)	0.009	7.00 (2.28 − 21.55)	0.001	
Model 4 in patients aged 80 years or older (n = 112)	
 Peritoneal dialysis	2.41 (0.97 − 5.94)	0.057	1.74 (0.55 − 5.48)	0.345	
 Conservative comprehensive care	3.17 (1.19 − 8.45)	0.021	4.97 (1.32 − 18.80)	0.018	
Model 1: univariable analysis for treatment modality.

Model 2: Model 1 + adjustment with age, sex, and Charlson comorbidity index score.

Model 3: Model 2 + adjustment with dialysis center, hemoglobin levels, and body mass index.

Model 4: Model 3 + adjustment with serum albumin levels.

Due to the violation of the proportional hazard assumption, a flexible parametric survival analysis was performed. CCC and PD revealed higher all-cause mortality in the univariable analysis as compared to HD (Table 3-Model 1 and Figure 2(C)). As the standardized mean differences in age and CCI score were >0.1 among the three treatment groups (Table 2), age and CCI score were included as adjustment covariates. An increased risk of all-cause mortality was still observed in both the CCC group and the PD group (Table 3-Model 2 and Figure 2(D)). The survival advantage of HD over PD was substantially diminished when serum albumin levels were considered (Table 3-Model 4, Figure 2(F)), compared to before serum albumin adjustment (Table 3-Model 3, Figure 2(E)). Although this final model had a reduced number of observations, all-cause mortality rates still increased in the CCC group. In comparison to HD, the final model showed that the adjusted hazard ratio for all-cause mortality was 1.38 (95% CI, 0.78–2.44; p = 0.275) for PD and 5.60 (95% CI, 2.26–13.84; p < 0.001) for CCC. Given the high risk of death during the first month of follow-up across all patient groups (Supplementary Figure 4), we conducted a sensitivity analysis by excluding patients who died during this time frame. The results remained consistent with the main analysis (Supplementary Figure 5). In comparison to the HD group, the adjusted hazard ratios for the CCC group and the PD group were 3.28 (95% CI, 1.02–10.56; p = 0.046) and 1.23 (95% CI, 0.69–2.20; p = 0.476), respectively. The adjusted hazard ratios for the CCC group and the PD group were 9.93 (95% CI, 3.10–31.81; p < 0.001) and 1.23 (95% CI, 0.64–2.39; p = 0.536), respectively, when the analysis was restricted to those with eGFR <10 mL/min/1.73 m2 at the time of cohort entry (Supplementary Figure 6).

To investigate if the different age groups had an impact on the effects of treatment modalities, the hazard ratio was estimated in Model 4 based on the patient’s age group. In patients aged 70 to 80 years, all-cause mortality did not differ significantly between those receiving PD and those receiving HD. However, the risk was significantly higher in patients who received CCC (Table 3 and Figure 2(G)). In patients aged ≥80 years, the adjusted hazard ratio for all-cause mortality was highly significant in the CCC group (Table 3 and Figure 2(H)). There was no significant evidence that the age groups had a modification effect.

Discussion

This study examined 719 elderly patients aged ≥70 years who decided on their treatment for ESKD. We discovered that patients who chose CCC had a higher risk of all-cause mortality compared to those who chose HD or PD. Although PD appeared to have a greater mortality rate than HD in univariable analysis, this difference was no longer evident in the fully adjusted model. Retrospective studies evaluating mortality rates in ESKD patients who were able to choose different treatment options at varying GFR levels are necessarily subjected to lead-time bias and immortal time bias. To mitigate the effect of lead-time bias, it is possible to conduct a survival analysis at specific GFR levels when patients make their treatment decisions [15, 16]. However, directly comparing therapies would be unattainable because some patients who choose dialysis therapy may die before receiving the dialysis treatments [16]. To address immortal time bias, a survival analysis can be performed at the time when treatment decisions are made [8, 16, 17]. In this study, we employed a different methodology from previous studies to address lead-time bias and immortal time bias. We utilized a propensity score analysis with an inverse probability treatment weighting (IPTW) technique, incorporating age, CCI score, and eGFR as covariates to calculate the propensity score. These factors were selected as they differed between treatment groups and were strongly associated with mortality. In addition, we employed the CCI score as a covariate adjustment in the flexible parametric survival analysis because this factor remained imbalanced even after applying the IPTW approach.

Our study revealed that elderly patients who received either HD or PD had comparable survival outcomes after accounting for substantial confounding factors, particularly serum albumin. Following the implementation of the IPTW technique, both univariable and multivariable analyses were conducted to assess the effect of various covariates, except for serum albumin (Models 1, 2, and 3). The findings consistently indicated that patients who underwent PD had higher mortality rates compared to those who underwent HD. However, the inclusion of serum albumin in the model eliminated these differences. It is implied that serum albumin is a significant predictor of survival which is widely acknowledged. Lower serum albumin levels at the time of PD initiation are strongly associated with the subsequent risk of peritonitis [18], which subsequently contributes to the risk of mortality. Our findings align with previous research, as it is widely acknowledged that patients who begin therapy with either HD or PD experience comparable survival outcomes, after accounting for potential confounding factors [19].

We demonstrated that CCC in elderly ESKD patients resulted in worse survival outcomes than HD or PD. This finding held even after applying the IPTW technique and the flexible parametric survival model to account for various confounding factors including serum albumin levels. These results were in line with another study [9], which found that dialysis treatment was associated with longer survival. We found that the survival advantage persisted in patients over the age of 80 years. It may be argued that the higher mortality rate in the CCC group is due to sicker patients being more inclined to choose supportive care than dialysis therapy. This is supported by our findings that the highest risk of mortality occurred within the first month after treatment initiation. The sensitivity analysis, which excluded patients who died within one month after receiving their selected treatment, revealed that the CCC group continued to exhibit higher mortality rates compared to the HD and PD groups. Additionally, the CCC group maintained its higher mortality rates although the analysis was restricted to patients who had eGFR <10 mL/min/1.73 m2 at the time of cohort entry. This finding confirmed the robustness of the main analysis. Nevertheless, our findings contradict previous data that reported no survival advantages from dialysis therapy in elderly patients with significant comorbidities [1]. After incorporating comorbidities in the propensity score model and as covariates in survival models, we discovered that comorbidities did not affect the survival benefits of HD or PD when compared to CCC. In accordance with a previous study [2], we advocated that patients’ performance status rather than comorbidities should be taken into account when deciding whether to initiate dialysis in an elderly patient.

Remarkably, our findings revealed that the survival advantage observed in patients who received HD or PD was unchanged even among patients aged ≥80 years. This discovery contradicts previous studies, which showed that patients aged ≥80 years who opted for CCC had comparable survival rates to those who chose dialysis therapy [8, 16]. In these studies, the starting time for analysis was determined by the point at which patients selected their treatments, not when they received their selected treatment. Consequently, certain patients might die before receiving dialysis treatment. This differed from our study, where the analysis was based on when patients received the treatment of their choice. Furthermore, previous studies did not include serum albumin as a covariate in their analysis. While our findings support the benefits of dialysis therapy for patients ≥80 years, we completely concur that ESKD patients should not base their treatment decisions exclusively on the survival advantage. Considering frailty, functionality, quality of life and cost-effectiveness is sometimes more important for patients and families than survival gain. This is supported by a study demonstrating that initiating dialysis in elderly patients causes a significant and sustained deterioration in the functional capacities [20].

Our study has some notable strengths. First, the analyses employ propensity score analysis with the IPTW approach, together with flexible parametric survival models. Although we employed a different approach to address confounding factors and biases, including indication bias, reclassification bias, lead-time bias, and immortal time bias, our findings were consistent with those of earlier studies [9]. This supports the validity of the finding that elderly patients who receive CCC have significantly higher mortality rates than those who receive HD or PD, even though a different analytic approach was used. Second, this study is a multicenter in which the variation across centers is considered in the analysis. Finally, this study is among the first to report survival rates separately for patients who received HD, PD, and CCC within the same cohort. Undoubtedly, our study has some limitations. First, our study may violate the assumptions of IPTW in terms of exchangeability [21], as several standardized differences did not fall <0.1 after applying IPTW. We addressed this issue by adopting a flexible parametric survival analysis that included an unbalanced element, the CCI score, together with well-accepted variables associated with survival, specifically BMI, hemoglobin, and serum albumin levels [2]. Furthermore, the multinomial logistic regression model with IPTW only incorporated three variables (age, CCI score, and eGFR). The constraints on the number of parameters in the model inevitably restrict the confidence of the study’s conclusions. Second, while propensity score analysis can mitigate indication bias, it only addresses measured confounders. Residual confounders remain a primary concern. While we adjusted for a proxy variable of frailty, such as albumin level, we were missing data on frailty status, functionality, and comprehensive geriatric assessment, which were the primary factors that influenced the selection of a modality and could have significantly influenced residual confounding. As indication bias is inevitable in retrospective study like ours, conducting a randomized controlled study to determine the optimal treatment choice for elderly patients with ESKD is not feasible due to significant ethical concerns. The most optimal study design to address this question would be a rigorous oversight prospective observational study that includes elderly patients who are eligible for all KRT modalities and CCC [22] and gathering data on dialysis indications which would include kidney failure-related signs and symptoms (such as volume overload), nutritional status, frailty, and functionality. Additionally, the estimation of GFR using a cystatin-based equation would be more accurate in estimating the kidney function of patients, which is a critical determinant of patient survival. Finally, the sample size in our study was rather small, especially in the CCC group, and consisted of only one ethnicity, which could limit the generalizability of the findings. The robustness of propensity score analysis and advanced statistical techniques were restricted by the small sample sizes, particularly in the CCC group. Future research that involves a multinational, multicenter study with a larger sample size is recommended.

In conclusion, this study demonstrates that elderly ESKD patients aged 70 years or older could benefit from survival advantages through dialysis therapy, regardless of whether it is HD or PD when compared to CCC. It is crucial to note that the survival advantages of HD or PD appear to persist in patients who are 80 years of age or older. However, the results must be interpreted with caution due to the retrospective nature of the observational study. Additionally, the decision to initiate dialysis in elderly ESKD patients should not be based solely on survival advantages. When making decisions, it is important to consider patients’ quality of life and the engagement of their families.

Supplementary Material

2_Supplementary_Materials_revised new.docx

Acknowledgments

We wish to express our appreciation to Professor Emeritus Visith Sitprija, MD, PhD, and gratitude for his generous support of this work as part of a PhD dissertation. We also thank Ms. Jirawan Anankum, Ms. Orapan Pankum, Ms. Parichat Rinsinjoy, and Ms. Salinee Srithiang for their hard work in collecting data.

Authors contributions

Conceptualization: KN, AT, JK, CR; data curation: KN, SV, PN, KP, PK, CM; formal analysis: KN, AT, JK, SN, SV, PN, KP, PK, CM, CR; methodology: KN, SV, PN, KP, PK, CM, CR; supervision: SN, CR; validation: KN, CR; validation: KN, CR; writing–original draft: KN, SN, CR; writing–review and editing: KN, AT, JK, CR, SN, SV, PN, KP, PK, CR. All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

This study does not cover data posting in public databases. However, data are available upon reasonable request to the corresponding author and are subject to approval by the Faculty of Medicine, Chiang Mai University Ethics Committee.
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