Exploring Predictors of Quality of Life in Older Adults With End-Stage Kidney Disease Receiving In-Center Hemodialysis: A Cross-Sectional Analysis of Sociodemographic, Dialysis-specific, Biopsychosocial, and Activity-Related Factors
Conditions: End Stage Renal Failure, Hemodialysis
Sponsor: University Ghent

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STUDY PROTOCOL  
Exploring Predictors of Quality of Life in Older Adults with End-Stage Kidney Disease Receiving In-
Center Hemodialysis: A Cross-Sectional Analysis of Sociodemographic, Dialysis-specific,  
Biopsychosocial, and Activity-Related Factors 
Overview 
The global prevalence of end-stage kidney disease (ESKD) is increasing, particularly among older 
adults. Maintenance in-center hemodialysis (ICHD), while life-sustaining, imposes significant 
treatment and symptom burdens that disproportionately affect older patients with coexisting frailty, 
multimorbidity, and psychosocial challenges. 
While older adults with ESKD consistently prioritize quality of life (QoL) over survival, this outcome 
remains underrepresented in clinical research. Health-Related Quality of Life (HRQoL) measures are 
often used as proxies but may not fully capture what matters most to patients, especially during 
major transitions like dialysis initiation. Differences in HRQoL instruments also create variability in 
evaluations and cost-effectiveness estimates. 
This study seeks to address these gaps by examining overall QoL using an individualized, validated 
instrument, and identifying its key determinants among older ICHD patients. 
 
Study objectives 
Primary Objective: 
To identify key factors associated with overall QoL, as measured by the Amnestic Comparative Self-
Assessment (ACSA) and a range of sociodemographic, dialysis-specific,  biopsychosocial, and activity-
related factors, in older adults receiving ICHD. 
Secondary Objectives: 
To examine the relationship between overall QoL (ACSA) and HRQoL instruments (EQ-5D-3L, EORTC 
QLQ-C30, QLQ-MY20). 
To explore clusters of patients with similar QoL profiles using multivariate and cluster analysis. 
 
Study design 
Type: Observational 
Model: Cross-Sectional 
Time Perspective: Prospective 
Reporting Guidelines: STROBE 
 
Study population 
Inclusion Criteria 
Adults aged ≥65 years 

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Receiving maintenance in-center hemodialysis (ICHD) 
Able to provide informed consent 
Sufficient cognitive and language ability to complete questionnaires in Dutch 
 
Exclusion Criteria 
Undergoing ICHD due to an acute hospital admission 
Formal diagnosis of severe psychiatric or cognitive disorders (e.g., dementia) 
Inability to understand or respond adequately to study assessments 
 
Study setting and recruitment 
Participants are recruited from four hospitals in Flanders, Belgium. Hospitals are selected to 
represent a mix of urban, rural, academic, and regional settings. Recruitment and data collection 
occurs during one to three consecutive dialysis sessions, depending on the participant's condition. 
 
Study procedures and data collection 
Assessments are administered face-to-face by an independent researcher unaffiliated with the 
dialysis units. The assessment session lasts approximately 45 minutes and includes validated 
instruments across several domains. 
 
Ethical considerations 
This study was approved by the Ethical Committee of Ghent University Hospital (B670201837264) 
and is conducted in accordance with the Declaration of Helsinki. All participants receive detailed 
information about the study and provide written and verbal informed consent prior to participation. 
 
Outcome measures 
Primary outcome/measure 
Measure: Overall Quality of Life (QoL) 
Instrument: Amnestic Comparative Self-Assessment (ACSA) 
Description: Participants self-rate their current QoL on a scale from -5 (worst time in life) to +5 (best 
time), anchored to their own past experiences. 
 
Secondary outcomes/measures 
Measure: Dialysis and sociodemographic characteristics 
Instrument: Medical records 

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Description: Age, marital status, living situation, (grand)children, dialysis vintage, hospital admissions 
 
Measure: Comprehensive Geriatric Assessment 
Instrument: Multi-domain assessment  
Description: Includes (risk of) falls using St. Thomas's Risk Assessment Tool in Falling Elderly 
Inpatients (STRATIFY), risk of dementia using Mini-Cog, nutritional risk using the NRS-2002, pain using 
a VAS-scale, questions about vision, hearing, and continence 
 
Measure: Frailty 
Instrument: Groningen Frailty Indicator (GFI) 
Description: A multidimensional frailty measure including biopsychosocial factors 
 
Measure: Activity 
Instrument: Brussels Integrated Activity Scale (BIA) 
Description: Measures basic-, instrumental-, and advanced activities of daily living 
 
Measure: Health-Related Quality of Life 
Instruments: EuroQol 5-Dimension 3-Level questionnaire (EQ-5D-3L), the EORTC QLQ-C30, the EORTC 
QLQ-MY20 
Description: the EORTC QLQ-C30 is a multidomain HRQoL instrument validated in chronic illness 
populations focusing on symptom and functioning scales, the EORTC QLQ-MY20 is often used as a 
supplemental module, focusing on disease burden, side effects, future perspective, body image 
 
 
 
 
 
 
 
 
 
 
 

Study protocol and statistical analysis plan v1.0 dd 01/01/2018 
STATISTICAL ANALYSIS PLAN  
General 
Statistical analysis consists of four stages: descriptive analysis, univariate analyses, multivariate 
analyses, and cluster analysis. Two-tailed tests are used, with p < 0.05 considered statistically 
significant unless otherwise specified. Data distributions to be assessed for normality using the 
Shapiro-Wilk test. Missing data are handled using listwise deletion per questionnaire, with the 
assumption that missingness was random. All statistical analyses are performed using IBM SPSS 
Statistics version 29.0.2.0. 
 
Stage 1: Descriptive analysis 
Descriptive statistics are calculated for all demographic, clinical, functional, psychosocial, and quality 
of life variables. This includes: 
-Overall QoL (ACSA) 
-Participant demographics (age, sex, dialysis vintage, hospital setting) 
-Comprehensive Geriatric Assessment (STRATIFY, Mini-Cog, GDS-4, NRS-2002, Pain VAS-scale, 
Questioning on vision, hearing, continence) 
-Frailty score (GFI) 
-ADL functioning (BIA; basic, instrumental, advanced activities of daily living) 
-HRQoL scores (EQ-5D-3L, EORTC QLQ-C30, QLQ-MY20) 
 
Stage 2: Univariate analyses 
Univariate analyses are conducted to identify associations between the independent variable (ACSA) 
and the dependent variables (secondary outcomes).  
Analyses are stratified based on variable type: Dichotomous variables: Mann-Whitney U test; 
Categorical variables with ≥3 groups: Kruskal-Wallis test; Ordinal and continuous variables: 
Spearman’s rank correlation coefficients 
Variables with a p-value < 0.05 in univariate testing are considered for multivariable modeling. 
Additional exploratory analyses examine the relationship between the ACSA score and HRQoL 
instruments/domains, including the EQ-5D utility index. 
 
Stage 3: Multivariate Analysis 
A stepwise multiple linear regression analysis is performed to identify independent predictors of 
overall QoL (ACSA score). Variables significantly associated with the ACSA in univariate analysis are 
entered sequentially using forward selection. Multicollinearity is assessed, and variables with high 
collinearity were excluded.  

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The model is evaluated using Adjusted R² to assess the proportion of explained variance. Regression 
coefficients (unstandardized B), standardized beta weights, t-values, and significance levels are 
reported for each step. 
 
Stage 4: Cluster Analysis 
To explore subgroups of participants with distinct QoL profiles, a two-step cluster analysis is 
performed: 
-Hierarchical clustering using Ward’s method to determine the optimal number of clusters based on 
the dendrogram. 
-K-means clustering to define group composition using the dependent variable (ACSA) and significant 
independent variables identified in prior analyses. 
Sensitivity Analyses 
The robustness of regression findings is checked using alternative model orders and bootstrapping 
methods. Variance inflation factors (VIF) are used to monitor multicollinearity.