Продольная оценка качества жизни взрослых, связанная со здоровьем, с использованием ОВД.

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Introduction

People with CKD experience an increased risk of mortality and reduced quality of life (QoL) compared with the general population, largely because of high symptom burden and associated comorbidities, including cardiovascular disease (CVD), cancer, and infections. 1 – 3 The effect of kidney dysfunction on the overall and domain-specific health-related quality of life (HRQoL) is most pronounced in individuals with advanced-stage CKD, especially for patients undergoing dialysis or opting for the conservative kidney management pathway. Previous research shows that fatigue is highly prevalent among those treated with dialysis and contributes to their diminished HRQoL. 4 , 5 While transplantation may improve the overall HRQoL, adverse effects, such as gastrointestinal problems, hand tremors, depression, and anxiety, are frequent occurrences in transplant recipients. 6 – 8 In addition, despite transplantation, their overall life satisfaction and physical, psychological, and social functioning may not necessarily return to pre-CKD levels. 9

Previous studies, however, are limited predominantly by the cross-sectional nature of the study designs, imprecision because of small sample sizes, and generalizability of the findings because most were single center studies. 10 – 12 In addition, prior research in adults with CKD has not assessed the changes in HRQoL estimates over time, particularly in relation to life-changing events, such as the transition from dialysis to transplantation or from earlier stage CKD to dialysis over the lifespan. 13 CKD is a life-long chronic illness that is characterized by a progressive loss of kidney function, which eventually may result in the need for KRT. Therefore, assessing the temporality of how kidney function decline affects overall health and well-being over the life course of an individual with kidney disease is essential. Interventions can then be identified, evaluated, and implemented to address the intervening factors between progressive loss of kidney function, overall HRQoL, and the symptom burden patients with CKD experience. In this study, we assessed the association between changes in CKD stages and overall and domain-specific HRQoL over time and determined the factors that are associated with these changes.

Methods

Study Design

This is a substudy of the Detecting Bowel Cancer in CKD (DETECT) study. 14 The DETECT study is a prospective, multicenter, cohort study of 1706 patients that aimed to determine the test performance and outcomes of a one-time screening for advanced colorectal neoplasia using fecal immunohistochemistry test across a broad spectrum of CKD stages. Details of the DETECT study have been published elsewhere, and the study protocol was approved by the Human and Research Ethics Committee of all participating centers (Westmead, Gosford, Royal North Shore, Blacktown, Nepean, Sir Charles Gairdiner, Concord, Royal Prince Alfred, Toronto General, Christchurch Hospitals, and the Hospital Clinic of Barcelona). In brief, the study included 1706 participants with CKD (G3-5), those treated with dialysis or had received kidney transplants across 11 different centers in Australia, Spain, New Zealand, and Canada from private, hospital, and academic clinics. HRQoL data were collected at baseline and the 2 year and 4 year follow-ups. We used the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L) multiattribute measures for all assessments. The EQ-5D-3L includes five dimensions of mobility, safe care, usual activities, pain, and anxiety/depression, with three response categories in each domain: no problems, some problems, and extreme problems. In this current analysis, we used HRQoL data from Australian participants across six different renal services: Western Renal Service (consisted of Westmead, Nepean, and Blacktown hospitals), Gosford, Royal Prince Alfred, Concord, Sir Charles Gardiner, and Royal North Shore Hospitals. We included only the Australian sites, as the Canadian, New Zealand, and Spanish sites did not complete the 4 year follow-up.

Covariates

Demographic data included age, sex, ethnicity, educational levels, and medical history, such as personal or family history of cancer, use of baseline medications, and co-existing conditions including CVD, diabetes mellitus, cancer, and cerebrovascular disease, were collected at baseline. Clinical data, including CKD stages and comorbid conditions such as CVD, hypertension, diabetes mellitus, and prior cancers, were assessed during the follow-up time points. CKD stages were considered a time-varying covariate that was modeled using random effects.

Outcome Measures

The EQ-5D-3L, a preference-based multiattribute utility instrument, calculated overall utility-based QoL on the basis of the Australian value set. 15 This value set covers the range of −0.217 (worse than dead) to 1.0 (full health). For domain-specific utility-based HRQoL, the response levels were dichotomized into any problems (combined categories of some problems and extreme problems because of a low number of participants in those categories) and no problems.

Statistical Analyses

Cohort Characterization

Demographic data were presented as frequencies (percentages) for categorical variables and the medians (interquartile range) for continuous variables. The continuous overall and domain-specific EQ-5D-3L were not normally distributed. However, we reported their means (SD) to enable application as utility estimates in health economic evaluations. We reported the change in overall HRQoL scores between baseline and the last follow-up at year 4 and compared across the different stages of CKD using generalized estimating equations (GEE), as well as logistic regression through GEE. Two separate analyses assessed the relationship between changing CKD stages and the longitudinal change in overall and domain-specific HRQoL scores over time.

Modeling for Overall HRQoL

Given the hierarchical nature of the data (repeated measurements within the individual participants) and the bimodally distributed outcomes with a high proportion of patients reporting full HRQoL (1.0), two approaches were used to assess the association between changing CKD stage (as a time-varying covariate) and overall HRQoL scores over time: a GEE (M1a) and a mixed effects linear regression (M1b; Figure 1 ).

Sensitivity Analyses

To assess whether the random effects varied by intercept, gradient, or both, we considered three variations of each model (random intercept, random slope, and random intercept and slope). Each model was compared using a likelihood ratio test to determine the significance of the random intercept. We also compared the quasi-likelihood method under independence criterion (QICs) values between M1a and M1b and fitted a null model to check for clustering effects within the cohort. To address missing data, we used two approaches: imputation (mean for continuous variables and mode for categorical variables) and complete case analysis. Each model variation (M1, M2) was run on both datasets and compared using the QIC as well as visual inspection of the resultant output.

Modeling for Domain-Specific HRQoL

We evaluated the association between changing CKD stages and domain-specific HRQoL using a GEE logistic regression. We fitted a GEE logistic regression to check for the association between CKD stage (as the time-varying covariate) and HRQoL overall scores and domain-specific HRQoL over time; all EQ-5D-3L domains were included (Figure 1 ). A random intercept null model was also fitted for each patient, given multiple measurements per individual, and akaike information criterion was used to select the best-fit model. To specify the fixed effects in the model, we used two variable selection techniques in this analysis. First is a classical approach, where a stepwise variable selection strategy defines the lowest akaike information criterion as the model's evaluation metric across all domains. Here, we included age, ethnicity, sex, diabetes mellitus, education status, and smoking history in the final models. As prior cancer, CVD, and hypertension were considered as potential mediators, we removed these variables in the final analyses (Figure 2 ).

Second, we used a machine learning approach to develop a variable selection algorithm. We fitted a random forest to the data using the same method as for the classical approach. For each dimension, a variable importance plot was built using the Inc Node Impurity score as the metric. The three most important variables, education, ethnicity, and sex were retained, and the three least important variables were dropped. We then fitted a GEE logistic regression using the R package glmtoolbox to the full model specification.

Results

Baseline Characteristics of the Cohort

Of the 1706 participants, 1290 were Australian residents and included in this analysis. Of these, 684 had CKD (G3-5), 286 were on dialysis, and 320 had kidney transplants, with 1280 participants completing the EQ-5D-3L data. Around 60% of the Australian cohort were men ( n =778, 60.3%) with a median age of 60.5 years [35–74.8] and an average difference between groups of 4.4 years. Most were of European descent ( n =891, 69.1%) and had never smoked ( n =596, 46.2%). Most patients had at least one comorbidity, with hypertension being the most common ( n =1094, 84.8%), followed by diabetes mellitus ( n =456, 35.3%; Table 1 ). With the exception of HRQoL measures, there was no reported missing data for any of the covariates used.

Sensitivity Analyses

The imputed dataset and the dataset with data removed showed that the QIC of the data with removed missing rows fit better (QIC=132.52) compared with the data with imputed values (QIC=189.75). However, we found that the output of both models produced similar results. We therefore chose to remove missing variables from the analyses, noting that both methods produced similar results and would not have drastically altered the conclusions. When comparing models using different random variable specifications using the likelihood ratio test, we found that the random slope and random intercept and slope were insignificant ( P approximately 1.0) compared with simply using a random intercept. Therefore, we chose to specify our model using a random slope only.

Changes in CKD Stages Over Time

The transition of participants between CKD stages over the 4 years is shown in Figure 3 . Between baseline and the 2 year follow-up, 146 were lost to follow-up ( n =66 uncontactable and n =80 deceased), leaving 1142 participants who completed the EQ-5D-3L assessments at the 2 year mark. Among these participants, 47 transitioned from CKD (G3-5) to dialysis, and 21 received a kidney transplant. Of the patients who remained in the study during the 2 year follow-up, 71 (24.8%) of those who were on dialysis at baseline underwent a kidney transplant. Between the 2 year and 4 year follow-up, an additional 481 patients were lost to follow-up, resulting in 660 participants providing a complete EQ-5D-3L assessment in the final year of follow-up. Among these 660 participants, 60 received a kidney transplant by the 4 year follow-up. Of these transplant recipients, 55 (91.6%) were on dialysis at baseline, while 5 (8.4%) had CKD (G3-5). In total, 23 participants started dialysis, and eight transplant recipients experienced graft loss and returned to dialysis by the end of the 4 year follow-up Supplemental Appendix 1 .

Association between Changes in CKD Stages and Overall HRQoL

We used two methods to assess the association between changes in CKD stages and domain-specific HRQoL over time: GEE and a mixed linear model, as seen in Figure 1 . The models were compared and found to perform similarly. The GEE was used in the final analysis. Compared with patients with CKD (G3-5), those treated with dialysis experienced a reduction with a coefficient of −0.07 (−0.09 to −0.04) in the overall HRQoL scores over the 4 year period. For transplant recipients, we found no significant reduction in the overall HRQoL score (coefficient=−0.02; 95% confidence interval, −0.05 to 0.005) over the 4 years compared with patients with CKD (G3-5). The observed 0.01 per year decline in HRQoL among dialysis patients was considered clinically meaningful. 16

Association between CKD Stage and Domain-Specific HRQoL

Compared with patients with CKD (G3-5), patients treated with dialysis had higher odds of reporting having problems across all domains over 4 years: mobility (1.91 [1.53–2.38]), self-care (2.30 [1.67–3.17]), usual activity (2.70 [2.15–3.38]), pain (1.19 [0.97–1.46]), and anxiety/depression (1.40 [1.12–1.75]). Transplant recipients also had higher odds of reporting problems compared to patients with CKD (G3-5) in domains of mobility (1.74 [1.40–2.18]) and usual activity (1.35 [1.07–1.71]), but not in the other domains of self-care, pain, and anxiety/depression. Figure 4 shows the proportion of patients with any problems across the five EQ-5D-3L dimensions (mobility, care, activity, pain, anxiety/depression) and CKD stages at baseline, the 2 year follow-up, and the 4 year follow-up.

Factors Associated with Changes in Overall HRQoL Scores

Over the 4 years, sex, ethnicity, smoking, and diabetic status were the risk factors associated with overall changes in HRQoL scores. Female patients experienced a significant reduction in HRQoL scores over time compared with male patients, decreasing with a coefficient of −0.05 (−0.07 to −0.03) over the 4 years. Compared with individuals of European ancestry, those of Middle Eastern descent reported a decrease with a coefficient of −0.13 (−0.19 to −0.08) in HRQoL scores. On the contrary, those who identified as Asian experienced an increase in HRQoL by 0.04 (0.01–0.08) over the 4 years. Compared with nonsmokers, current smokers reported a decrease with a coefficient of −0.1 (−0.13 to −0.07) in overall HRQoL scores. Patients with diabetes mellitus also experienced a significant reduction with a coefficient of −0.07 (−0.08 to −0.05) in HRQoL scores compared with patients without diabetes mellitus.

Factors Associated with Changes in Domain-Specific HRQoL

Factors associated with changes in domain-specific QoL were sex, ethnicity, education, diabetes mellitus, and smoking status (Figure 5 ). Female patients had higher odds of reporting problems compared with male patients across domains of mobility (1.45 [1.21–1.73]), usual activities (1.52 [1.27–1.82]), pain (1.45 [1.23–1.70]), and anxiety/depression (1.41 [1.18–1.67]), but not self-care. Patients with diabetes mellitus had higher odds of reporting some problems compared with patients without diabetes mellitus in domains of self-care (2.45 [1.86–3.22]), usual activities (1.93 [1.61–2.33)] and pain (1.44 [1.22–1.69]), and mobility (2.29 [1.19–2.73]) At the same time, no significant associations were observed with anxiety/depression (Figure 5 ).

Individuals who self-identified as Asians had lower odds of reporting problems in mobility (0.70 [0.49–1.00]) and usual activities (0.68 [0.48–0.97]) compared with other ethnicities. However, patients of Middle Eastern ethnic backgrounds had higher odds of reporting having problems across all five domains: mobility (2.2 [1.45–3.52]), self-care (3.50 [2.01–6.07]), usual activities (2.41 [1.53–3.79]), pain (2.15 [1.41–3.28]), and anxiety/depression (2.66 [1.75–4.03]).

Patients with higher educational attainment had lower odds of reporting problems in domains of mobility and anxiety/depression. For example, compared with those who had only completed primary school, patients who had a university degree had significantly lower odds of reporting problems in usual activities (0.61 [0.42–0.88]) and anxiety/depression (0.67 [0.46–0.97]). Participants who had only completed technical and further education also had marginally lower odds of reporting problems in mobility (0.71 [0.5–0.99]) than those who had completed a diploma. Current smokers experienced higher odds of reporting problems in domains of mobility (2.06 [1.51–2.81]), usual activities (1.80 [1.30–2.49]), pain (1.89 [1.40–2.56]), and anxiety/depression (1.79 [1.31–2.44]) compared with nonsmokers (Never).

Discussion

We found a statistically significant decline of 0.06 in the overall HRQoL scores over 4 years among patients treated with maintenance dialysis, but not in those with CKD (G3-5) or who had received a transplant. While this is a small difference over time, it may be clinically significant to some patients. At the domain-specific level, both transplant recipients and patients treated with dialysis had higher odds of experiencing problems in domains of EQ-5D-3L assessments compared with individuals not requiring KRT. Patients treated with dialysis consistently reported problems across all five domains, while transplant recipients reported problems only in mobility and usual activities. The key factors associated with a decline in overall and domain-specific HRQoL over time included female sex, lower education attainment, having diabetes mellitus, being a current smoker, and being of Middle Eastern ethnicity.

A previous study indicated that dialysis patients experience a substantial reduction in overall and domain-specific QoL, with utility estimates ranging between 0.7 and 0.75. 13 After transplantation, the study found there is typically a significant improvement in QoL scores, with increases of approximately 0.1–0.15. 13 However, these findings are predominantly derived from cross-sectional studies, which do not account for time trends or the effect of patients transitioning through different stages of CKD over time.

The progressive decline in HRQoL among patients undergoing dialysis was not unexpected because it was likely driven by several interrelated factors. These included the burdensome nature of dialysis treatments, co-existing comorbidities such as vascular disease, diet restrictions, and psychosocial factors. 17 – 20 Over time, these factors were likely to intensify, further diminishing patients' overall well-being and QoL. 21 Across all domains, we found that patients treated with dialysis had significant deficits across four of these domains, including mobility, usual activities, pain, and anxiety/depression compared with patients with CKD stages 3–5, and the effects are pervasive over the 4 year follow-up period. In a study that used the Kidney Disease Quality of Life Short-Form instrument, a cohort of 88 patients undergoing peritoneal dialysis exhibited significant decline in QoL scores across multiple domains, specifically the burden of having kidney disease, emotional well-being, general health, and patient satisfaction, over a 2 year period. 22 This decline was primarily driven by the strain and challenges associated with managing dialysis care and the complications arising from the procedures.

In our cohort, only 55 (out of 286) patients receiving dialysis at study inception underwent a kidney transplant over the 4 years. Among those who received kidney transplants, their HRQoL remained relatively stable, except for some decline in mobility and usual activity. While transplantation offers significant survival and QoL benefits, 23 it is not a cure and does not necessarily restore full health. Patients still experience considerable adverse effects from long-term immunosuppression, such as steroids and steroid-induced osteoporosis and muscle wasting, are the key drivers for reduced mobility and inability to regain full activity. 24 Over time, transplant recipients may face many other complications, including acute and chronic rejection, infections, cancer, and chronic allograft dysfunction, which can further affect their overall physical and mental health. 25

In our previously published cross-sectional analyses, we found that female sex was associated with a lower overall HRQoL scores at baseline (by around 0.03) compared with male patients. 13 Our current findings, based on longitudinal study of the participants, further suggest that female patients have a greater decline in the overall HRQoL scores by 0.05 over time compared with their male counterparts. Such decrement in QoL was observed across all domains. Our current study findings contrast that of a recent study, which found men with advanced CKD experienced more rapid decline in HRQoL over time than women. There have been few longitudinal studies that explored the effect of sex and gender on the HRQoL trajectories, but all found inconsistent results. None of these studies, however, explored the influences of sex across the entire CKD spectrum over time. Many sex-specific or gender-specific clinical and social factors may explain reasons for the observed differences between the two sexes. For example, prior research has indicated hormonal changes are important factors that may affect on life changes, such as early menopause in patients with CKD. 26 , 27 Findings from a study conducted in Korea suggested that women treated with dialysis and experienced early menopause had lower QoL scores across domains of physical functioning, role–physical functioning, role–emotional functioning, vitality, and general health perception compared with women without CKD. 28 Female patients generally have lower muscle mass than men and having CKD can further exacerbate muscle wasting, which can disproportionately affect the overall physical functioning, mobility, and activity. 29 Female patients may experience higher rates of depression and anxiety than male patients, which are also associated with lower QoL. 30 Other factors may include differential access to health care utilization between female patients and male patients and differences in the perceived caregiver and gender roles within the families. Previous work has indicated that female patients with CKD have reduced access to quality care, 31 more likely to take on the caregiver role and household responsibilities even while managing their own chronic illness. 32

In contrast to our previous work, we found that self-declared ethnicity in Australians is associated with changes in overall and domain-specific HRQoL. Compared with other ethnicities, patients who identified as Middle Eastern experienced significantly lower HRQoL (−0.13 per year), while individuals who identified as Asian had higher HRQoL over time (0.04 per year). Reasons for the disparity between HRQoL over time for Middle Eastern, Asian, and European Australians may be due to the Healthy Migrant Effects in the Australian general population. Some studies found that because of mandatory health checks for all migrants moving to Australia, migrants from specific regions or cultural backgrounds experience better overall health outcomes and a slower deterioration in health compared with migrants from other regions or cultural backgrounds. 33 A study indicated that Lebanese Australians experienced a significant decline in healthy life expectancy between 2006 and 2016, compared with Australian-born populations. 34 However, the true underlying causes of these disparities in Australian participants are complex and outside of the scope of this study, requiring further investigations.

The strengths of our study include the diverse representation of participants from different ethnic backgrounds and age groups and across all CKD stages and underwent repeated HRQoL assessments at baseline and 2 year and 4 year follow-up, using a validated HRQoL tool in patients with CKD. While several studies have examined the longitudinal trajectories of HRQoL in people with CKD, no prior studies have defined the dynamic changes of QoL scores as patients transitioned through the different CKD stages over time. Patient demographics, including clinical and socioeconomic factors, were collected prospectively across multiple centers in Australia.

Our study also has several limitations. Patients enrolled on the DETECT study were likely healthier than those who did not agree to participate; the health volunteer bias may potentially skew the QoL data distribution. Our participants may have higher HRQoL scores compared with the general CKD population. In addition, it is a relatively stable population, where approximately only 35% of the participants moved between health states over the 4 year follow-up period. We also lacked information on the transplant evaluation and listing status among dialysis populations. However, because the DETECT study was a bowel cancer screening study in patients with CKD, participants were likely relatively healthier, and therefore more likely to have undergone transplant evaluation, and been listed on the waitlist. In addition, this study also failed to account for the number and severity of comorbidity symptoms, such as using a Dialysis Symptom Index. While EQ-5D-3L is a widely used, preference-based QoL measure, the three-level measure has previously been shown to be less sensitive to changes in health problems than the five-level measures. Therefore, detecting subtle changes in health conditions may be compromised. Our analysis also did not include the visual analogue scale, which also limited the capacity to detect health changes. Furthermore, only 51% of the initial cohort completed all three follow-up assessments, with more than 60% of the incomplete data attributed to participants' deaths. These findings emphasize the severity of the underlying comorbid conditions in this population and the effect of mortality on study retention and outcome assessment. In addition, the relatively small reduction in overall HRQoL scores may be attributed to potential survival bias because individuals with lower HRQoL and greater comorbidities were more likely to have died earlier in the study.

In conclusion, this multicenter longitudinal study found that patients treated with maintenance dialysis reported a consistent reduction in HRQoL scores across all domains, highlighting the profound effects of ongoing treatment and related comorbidities. Our findings also suggest the need for targeted strategies to support vulnerable groups, including women, individuals with lower educational attainment, and culturally diverse migrants, to improve health care access and outcomes for those facing multiple health challenges. Finally, because previous studies have not captured the dynamic changes in the utility estimates between health states over time, the transition utility estimates generated from this study provide relevant inputs for future economic evaluations in kidney trials. These estimates will ensure more accurate modeling of cost-effectiveness, better inform policy decisions, and guide allocation of resources in kidney diseases.

Supplementary Material

Footnotes

J.Y. and G.W. are co-senior authors.

Disclosures

Disclosure forms, as provided by each author, are available with the online version of the article at http://links.lww.com/KN9/B419 .

Author Contributions

Formal analysis: Owen Ng, Armando Teixeira-Pinto.

Investigation: Kirsten Howard, Anh Kieu, Anoushka Krishnan, Wai H. Lim, Simon D. Roger, Anita van Zwieten.

Supervision: Germaine Wong, Jean Yang.

Writing – original draft: Owen Ng.

Writing – review & editing: Michael J. Bourke, Steven J. Chadban, Jeremy R. Chapman, Jonathan C. Craig, Allison Jaure, Carol A. Pollock, Shaundeep Sen.

Funding

None.

Declarative Statements

This study includes clinical experimentation and received Institutional Review Board or Ethics Committee approval. All patients provided written informed consent. This study includes clinical experimentation and complies with the Declaration of Helsinki. The clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the Declaration of Istanbul on Organ Trafficking and Transplant Tourism.

Data Availability Statements

Original data generated for the study will be made available upon reasonable request to the corresponding author. Data Type: Health Care Data; Observational Data. Reason for Restricted Access: Contains patient information with sensitive attributes including age, sex, deceased status etc .

Supplemental Material

This article contains the following supplemental material online at http://links.lww.com/KN9/B420 .

Supplemental Appendix . Sankey plot showing the movements of patients between CKD Stages at baseline ( 1 ), CKD Stages at the 2 year follow-up ( 2 ), and CKD Stages at the 4 year follow up ( 3 ).

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Associated Data

Data Availability Statement

Original data generated for the study will be made available upon reasonable request to the corresponding author. Data Type: Health Care Data; Observational Data. Reason for Restricted Access: Contains patient information with sensitive attributes including age, sex, deceased status etc .