Abstract
The current study was to explore the prevalence and risk factors elements of pre-frailty and frailty among patients undergoing hemodialysis (HD) in central China. A cross-sectional, multi-institutional investigation was conducted. From March to May 2024, using the convenience sampling method, a total of 408 HD patients from four hospitals in Xiangyang, China, were recruited for this study. The participants’ demographics, lifestyle factors, factors related to dialysis treatment, the FRAIL scale, psychological resilience, and medical coping modes were assessed using a questionnaire. Multi-categorical logistic regression was performed to examine factors associated with pre-frailty and frailty in this population. Furthermore, to evaluate the independent relationship between frailty and psychological resilience, multiple regression analysis was used to adjust potential confounders and obtain odds ratios (OR) and 95% confidence intervals (CI). Pearson correlation analysis was used for correlation analysis among various scales. Among the 408 participants, the prevalence of pre-frailty and frailty among participants for all ages was 26.2% and 38.5%. Multivariate logistic regression analysis showed that smoking status, falls, heart disease, and psychological resilience are all associated with pre-frailty and frailty. The Pearson correlation analysis showed that resignation was positively related to frailty and negatively related to psychological resilience in patients. Confrontation and avoidance were positively related to psychological resilience. Frailty was negatively related to psychological resilience. Psychological resilience was independently linearly associated with pre-frailty (OR 0.49, 95% CI 0.32–0.75, p < 0.001) and frailty (OR 0.53, 95% CI 0.35–0.80, p = 0.003). Our findings point to the necessity for active screening prevalence and risk factors for pre-frailty (26.2%) and frailty (38.5%) in adult HD patients of all ages. Pre-frailty and frailty among HD patients are associated with lower psychological resilience and a higher likelihood of using negative coping mechanisms.
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Introduction
End-stage kidney disease (ESKD), a terminal stage of chronic kidney disease (CKD) or acute kidney injury, results in a rapid decline in kidney function. Without kidney replacement therapy (KRT), ESKD patients are prone to life-threatening complications. Notably, in Asia, only 17–34% of individuals requiring KRT receive treatment, compared to 9–16% in Africa1,2. Anticipating a significant global increase in KRT utilization, reaching 5.4 million individuals by 2030, with the most notable rise in Asia1,2. Healthcare providers must be vigilant in addressing the challenges of this disease. Of the nearly 90% of ESKD patients who initiate hemodialysis (HD) therapy3.
Frailty emerges as a significant issue in ESKD patients. Frailty, defined as an age-related state of reduced physiological reserve and stress resilience4,5, heightens the risk of adverse health outcomes in ESKD patients. These include higher mortality rates, increased hospitalizations, falls, fractures, cognitive decline, delirium, disability, reduced quality of life, undernutrition, sleep disturbances, depression and anxiety, dialysis-associated complications, and vascular access failure6,7,8,9,10,11,12,13. Given the projected rise in ESKD prevalence among the elderly, the prevalence of frailty among this patient population is expected to increase.
In contrast to the estimated 7 to 24% prevalence of frailty in the general elderly population, and increases with age5,14. Studies have reported a prevalence ranging from 30 to 60% in older adults with ESKD15. This disparity highlights the need for a deeper understanding of frailty in the context of HD. The total prevalence of frailty in individuals receiving HD and peritoneal dialysis (PD), respectively was reported to be 30–80%8and 65–72%12, which is much higher than the rate of 12% in patients with early kidney disease16. Being an HD patient is a risk factor in itself for non-robust (frail or prefrail) state17. Frail people account for 42% of those undergoing regular HD15. Studies found the prevalence of frailty was 50-71.4% for older patients and 42-47.3% for younger patients, which demonstrates a very high burden of frailty throughout the entire age range of HD patients18,19. Therefore, health care providers should pay closer attention to the prevalence and symptoms of frailty in HD patients.
Frailty is influenced by a variety of causes, the most significant of which is sarcopenia5. The main factors affecting frailty include gender, age, obesity, low income, marriage, physical dysfunction, lack of exercise, muscle mass, the dialysis age ≤ 12 months, dialysis adequacy, peripheral vascular disease, heart disease, diabetes, cognitive impairment and depression, pain, sleep disturbances, laboratory indicators such as low albumin levels and serum magnesium levels, using a higher number of medications and medication literacy, social support, and family functionality8,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35. Frailty is a dynamic process that occurs when an individual interacts with those of their surroundings. Thus, the same person may experience varying degrees of frailty at different periods, and frailty is reversible. As a result, early detection and treatment of frailty can slow or even reverse decline, improving quality of life and lowering medical costs36. Besides, Identifying risk factors for frailty in HD patients may allow healthcare practitioners to provide better care.
Several studies have found a high frequency of psychosocial issues among dialysis patients. Depression, interpersonal sensitivity, paranoia, somatic complaints, obsessive-compulsive thinking, anxiety, psychosis, violence, and morbid terror are some of the most frequent psychiatric disorders in dialysis patients37. It is evident that psychological resilience protects against depression and anxiety38, and in chronic disease, resilience is linked to treatment adherence and well-being39. Psychological resilience is low among HD patients40. It is critical to study the psychological resiliency of HD patients. Furthermore, while frailty is a well-recognized phenomenon, the role of psychological resilience in mitigating its effects remains underexplored. Psychological resilience, defined as the ability to adapt positively to adversity, may be a crucial factor in determining the outcomes of HD patients with frailty.
The Medical Coping Modes Questionnaire (MCMQ) scales examine patients’ coping mechanisms when dealing with diseases, and they are frequently employed in the posttraumatic growth assessment of HD patients due to their high reliability and validity. These patients can tolerate stress, anxiety, and psychological difficulties by strengthening their resilience and developing good coping strategies41.
The current literature lacks comprehensive studies examining the interplay between frailty, pre-frailty, psychological resilience, and medical coping modes in HD patients. This study is to bridge this gap by: quantifying the prevalence of pre-frailty and frailty in HD patients in central China; investigating the associations between pre-frailty, frailty, psychological resilience, and medical coping modes; and identifying risk factors for frailty in this patient population. The results of this study will inform healthcare practitioners about the importance of screening for frailty and promoting psychological resilience and taking a proactive coping approach across the entire age range of HD patients, ultimately leading to better care and improved outcomes.
Methods
Study design and participants
This study employed a descriptive cross-sectional methodology conducted across multiple dialysis units. The participant recruitment spanned from February 1, 2024, to May 1, 2024, encompassing 408 maintenance HD patients from diverse hospitals in Xiangyang City, Hubei Province, China. These hospitals comprised one Grade III Level-A, one Grade III Level-B, one Grade III comprehensive, and one Grade II Level-A institution.
Sampling strategy and criteria
Convenience sampling was utilized in this cross-sectional study. The inclusion criteria were designed to ensure the participants’ eligibility, including: (1) possessing clear consciousness, normal cognitive function, and no communication barriers; (2) undergoing outpatient HD therapy for at least three months; and (3) voluntarily agreeing to participate in the survey. Conversely, the exclusion criteria excluded individuals who were unconscious, had dementia, suffered from mental illness, or had difficulty communicating, active malignancy or acute illnesses of impact activities, such as amputations.
Sample size estimation
Drawing from Kendall et al.’s sample size estimation approach42, the survey’s sample size was calculated based on the medical coping mode with the highest number of entries (20 entries). To ensure adequate representation and account for potential invalid responses, the sample size ranged from 120 to 220, representing 5 to 10 times the scale entries, with an additional 10% questionnaire invalidity rate43.
Assessment of frailty
To evaluate frailty among the participants, the FRAIL scale was adopted44,45. This scale comprised five self-reported questions (Table 1), each scored as 1 for a “yes” response and 0 for a “no” response, resulting in a total score ranging from 0 to 5. Scores were interpreted as follows: a score of zero indicating robustness, one to two suggesting pre-frailty, and three or more indicating frailty. This scale has been validated as an effective and user-friendly tool for screening frailty among HD patients31,46.
Measurement of MCMQ and psychological resilience
The Medical Coping Modes Questionnaire (MCMQ), originally developed by Feifel et al.47, has become a standard instrument for evaluating patients’ coping with strategies in the context of life-threatening illnesses. This questionnaire is comprised of three distinct domains: confrontation, avoidance, and resignation. Each ___domain is further subdivided into specific items, with confrontation encompassing 8 items, avoidance 7 items, and resignation 5 items. The respondents are asked to rate each item on a 4-point Likert scale, ranging from “not at all” to “very much.” The cumulative score of each ___domain is then calculated, and the ___domain with the highest score indicates the predominant coping method adopted by the patient.
The Connor-Davidson Resilience Scale (CD-RISC), formulated by Connor and Davidson in 200349, serves as a reliable tool for assessing psychological resilience. The simplified Chinese version of this questionnaire comprises 10 items, and respondents are required to rate each item on a 4-point scale, ranging from 0 to 450. The total cumulative score obtained represents the level of psychological resilience, with a higher score indicating better resilience.
Data collection on covariates
A comprehensive set of covariate data was gathered to provide a holistic understanding of the study participants. Demographic variables such as age (categorized as ≥ 60 years and < 60 years), sex (male/female), height, weight, religious affiliation (yes/no), marital status (married, single, divorced, widowed), with children (yes/no), education level (elementary school and below/above elementary school), residential situation (living with family/others), employment status (yes/no), and monthly household income (≤ 3000 yuan/>3000 yuan) were recorded. Additionally, lifestyle factors were considered, including smoking status (smokers, quit smokers, and never smokers), drinking status (drinkers, quit drinkers, and never drinkers), and exercise (yes/no).
Furthermore, factors related to dialysis treatment were also included in the data collection. These comprised dialysis duration (categorized as < 5 years and ≥ 5 years), dialysis frequency (twice a week, three times a week, and five times in two weeks), the occurrence of falls (yes/no), type of vascular access (arteriovenous fistula, artificial blood vessels, and central venous catheter), the presence of hypertension (yes/no), diabetes (yes/no), heart disease (yes/no), cerebral disease (yes/no), and the existence of other comorbidities (yes/no). Table 2 showed how the variables were operated.
Ethical considerations
This study was approved by the biomedical ethics committee of the Xiangyang No.1 People’s Hospital (NO.XYYYE20240025). Informed consent was obtained from all participants prior to the investigation. This study was conducted in accordance with the Declaration of Helsinki.
Statistical analysis approach
For the descriptive analysis of continuous data, we adhered to a meticulous approach. Specifically, for data that exhibited a normal distribution, we utilized the mean and standard deviation (SD) as measures of central tendency and dispersion. However, for data that deviated from a normal distribution, we opted for quartiles (Q2, Q4) to capture their distributional characteristics.
To facilitate comparative analysis, we segregated all participants into three distinct groups based on their frailty grades. This classification allowed us to conduct intergroup comparisons, particularly with regard to categorical variables. In order to identify potential predictors of frailty, we conducted a comprehensive review of baseline variables. Variables that were deemed clinically relevant or exhibited a significant univariate relationship with the outcome were subsequently included in a multivariate logistic regression model.
To further explore the predictors of frailty among our participants, we opted for multinomial logistic regression analysis. This technique enabled us to assess the independent contribution of various factors to the likelihood of frailty across different grades. Additionally, to assess the relationship between frailty and psychological resilience, we employed multiple regression analysis. This analysis was conducted in a stepwise manner, with each model adjusting for a different set of potential confounders. Specifically, Model 1 provided an unadjusted baseline, Model 2 incorporated age and sex, Model 3 further adjusted for monthly household income, education level, smoking status, drinking status, having children, falls, vascular access, and exercise, and Model 4 extended Model 3 by incorporating diabetes, heart disease, and cerebral disease.
Furthermore, to evaluate the correlations among various scales, we employed Pearson correlation analysis. The significance level for all statistical tests was set at a two-sided p-value of less than 0.05. Finally, to ensure the accuracy and reproducibility of our results, we employed two statistical software packages: SPSS 21.0 and FreeStatistics software version 1.7. These software tools provide robust statistical functions that were essential for the comprehensive analysis presented in this study.
Results
Demographic and clinical characteristics of the study population by categories of different frailty grades
The characteristics of the study population were presented in Table 3. Among the 408 participants in this study, more than half were male (52.9%). The median age of the participants was 59.00 years. Most participants had no religion (97.8%) and were not living alone (89.7%). Besides, most participants had not worked (88.5%) and had lower income (52.9%). In terms of dialysis duration, 65.9% of participants were within 5 years, and 34.1% of participants were more than 5 years. Among the participants’ diseases, hypertension was the most prevalent, with more than twice as many other diseases. More than half of the participants hardly exercised (72.3%), and only 27.7% regularly exercised.
Patients with frailty were older, had lower income, smoked more, had a shorter duration of exercise, had a higher prevalence of falls, diabetes mellitus (DM), heart disease, and cerebral disease, and had more arteriovenous fistulas than those in the non-frailty group (p < 0.05; Table 3).
Frailty, medical coping modes, and psychological resilience in HD patients
The frailty score of the participants ranged from zero to five, with a mean of 1.96 (SD 1.85). A quarter of the participants were pre-frail (26.2%), and 38.5% were frail. The prevalence of pre-frailty and frailty was 43% and 59.9%, respectively, in participants aged ≥ 60 years, and 57% and 40.1% in participants aged < 60 years. Nearly half of the participants had fatigue (48.8%); 176 (43.1%) had low resistance; 131 (32.1%) had aerobic disorder; 151 (37.0%) had more than 5 illnesses; 141 (34.6%) had unexpected weight loss > 5% in the past year.
The total scores of MCMQ and CD-RISC didn’t satisfy normal distribution. The confrontation, avoidance, and resignation dimensions of MCMQ scored 24.0 (21.0, 26.0), 12.0 (12.0, 14.0) and 14.0 (13.0, 16.0), respectively, whereas for psychological resilience, it was 23.0 (20.0, 30.0). Patients with frailty had lower psychological resilience and had more resignation than those in the non-frailty group (p < 0.05; Table 3).
The Pearson correlation analysis showed that resignation is positively related to frailty and negatively related to psychological resilience in patients. Confrontation and avoidance were positively related to psychological resilience. Frailty was negatively related to psychological resilience. The details are shown in Table 4.
Univariate logistic regression analyses on factors affecting pre-frailty and frailty of HD patients
The unordered multi-categorical logistic regression was performed using frailty status (pre-frailty, frailty, and robust) as the outcome variable and the robust group as the reference category. Based on the results, factors influencing pre-frailty included those who weigh (odds ratio [OR] 1.02; 95% CI 1.00–1.04), have no smoking status (OR 0.52; 95% CI 0.29–0.93), have no falls (OR 0.25; 95% CI 0.09–0.66), have no heart disease (OR 0.49; 95% CI 0.26–0.90), and have psychological resilience (OR 0.96; 95% CI 0.92–0.99). Factors influencing frailty included older age (OR 1.03; 95% CI 1.02–1.05), with children (OR 0.43; 95% CI 0.18–0.98); educational level (OR 0.60; 95% CI 0.38–0.95), monthly household income (OR 0.57; 95% CI 0.36–0.89), no smoking status (OR 0.39; 95% CI 0.23–0.67), no drinking status (OR 0.6; 95% CI 0.37–0.97), exercise (OR 0.31; 95% CI 0.18–0.54); no falls (OR 0.12; 95% CI 0.05–0.30), artificial blood vessels (OR 2.34; 95% CI 1.09–5.01); central venous catheter (OR 2.85; 95% CI 1.27–6.43), no diabetes (OR 0.36; 95% CI 0.22–0.58), no heart disease (OR 0.30; 95% CI 0.17–0.52), no cerebral disease (OR 0.09; 95% CI 0.02–0.37), resignation (OR 1.13; 95% CI 1.02–1.24) and psychological resilience (OR 0.94; 95% CI 0.91–0.97; Table 5).
Multivariate logistic regression analysis of frailty grades and psychological resilience in HD patients
Table 4 shows that age, vascular access, and resignation were positively correlated with the frailty, and with children, education level, monthly household income, smoking status, drinking status, exercise, falls, heart disease, cerebral disease, and psychological resilience were negatively correlated with the scale in univariate models. In multivariate models that adjusted for age, with children, education level, monthly household income, smoking status, drinking status, exercise, vascular access, falls, heart disease, and cerebral disease, which were variables with p < 0.05 in univariate analyses. On account of sex being a risk factor for frailty26, it was also included in the adjusted model. Psychological resilience was independently linearly associated with pre-frailty (OR 0.49, 95% CI 0.32–0.75, p < 0.001) and frailty (OR 0.53, 95% CI 0.35–0.80, p = 0.003) (Table 6).
Discussion
Prevalence of pre-frailty and frailty in HD patients
The incidence of pre-frailty and frailty among patients undergoing HD is a significant concern, especially given the physical and psychological implications it brings. Our study has revealed that the prevalence of frailty among HD patients stands at 38.5%, with a substantial proportion of these patients being aged 60 and above (59.9%). This finding aligns with previous studies conducted in the USA (31.6–52%)21,50, Spain (29.6–53.8%)51,52, Latin America (50.54%)53, Japan (23-39.7%)29,32, Taipei (31%)54, Canada (52%)55, Australia (24.1%)56, Brazil (36.5%)46, Italy (55%)57, New Zealand (37%)58, and Korea (34.8%)59, where the prevalence ranged from 23 to 55%. Surprisingly, the incidence of frailty in Indian HD patients is as high as 82%60. A literature, as compiled in a systematic review and meta-analysis8, indicates a wide range of frailty prevalence among patients with ESRD undergoing HD, spanning from 29.6 to 81.5%. However, in the new 2024 study, the prevalence of frailty ranged from 12.6 to 82% in patients undergoing maintenance HD61. These differences in prevalence rates were associated with the use of different frailty assessment tools, sample sizes, and regions. These were also evidence of the need for increased attention to frailty in HD patients around the world.
Of note, the Fried phenotype has emerged as a prevalent screening tool for frailty, with studies reporting a prevalence ranging from 31.0–81.5%21,49,60,61,62. In our study, we delved deeper into the risk factors for pre-frailty among HD patients. We observed a considerable prevalence of pre-frailty in this study, accounting for 26.2% of the patients. This finding is comparable to the 35.4% range reported in studies conducted in the East China region21. Furthermore, a recent 2022 study revealed that pre-frailty and frailty are prevalent even in younger HD patients, with a prevalence of 23.0% among those under 50 years old, which increases significantly with age17.
While the majority of frailty research has focused on elderly HD patients, our findings highlight the significant burden of pre-frailty and frailty among non-elderly patients. Our study revealed that the prevalence of pre-frailty and frailty among non-elderly participants was over 40% and more than half, respectively. This aligns with a two-center prospective analysis62, which reported a frailty prevalence of 71.4% among older patients and 47.3% among younger patients, indicating a substantial burden among the younger age group. Unlike their healthy peers, some younger HD patients have years of cardiovascular disease load and other comorbidities, thus, their disease profiles frequently mimic those of older adults. Their particular medical history, along with the physical and mental strain of dialysis, can frequently trigger the cycle of multisystem dysregulation, propelling them into a frail state. However, it is noteworthy that the prevalence of frailty and damage to multiple organ systems increases as people age, with older age being associated with a 2.90-fold increase in frailty62. Therefore, early screening of pre-frail and frail younger dialysis patients and early intervention is crucial for their survival and prognosis.
To mitigate the substantial incidence of frailty among elderly dialysis patients, preemptive measures are crucial, especially when implemented at an earlier age. Multiple variables, each with its own unique influence, contribute significantly to the progression of frailty. In addition to the relevant factors mentioned in the background, patients undergoing HD undergo inevitable the accumulation of toxic substances, amino acid, and protein losses due to the dialysis process itself63. Additionally, the triad of insulin resistance, chronic inflammation, and vascular calcification acts synergistically to promote the depletion of musculoskeletal mass64,64,66, thereby further heightening the risk of frailty among these patients.
Factors influencing pre-frailty and frailty in HD patients
When delving into the factors that influence pre-frailty and frailty in HD patients, we uncovered a diverse array of determinants. We found that factors influencing frailty in HD patients were advanced age, having children, low monthly household income, smokers, non-exercise, falls, vascular access, diabetes, heart disease, cerebral disease, resignation, and psychological resilience. Multivariate logistic regression analysis showed that age, having children, education level, monthly household income, smoking status, drinking status, exercise, falls, vascular access, diabetes, heart disease, cerebral disease, resignation, and psychological resilience are associated with frailty. Multivariate logistic regression analysis showed that weight, smoking status, falls, heart disease, and psychological resilience are associated with pre-frailty.
We have observed a clear association between advancing age and the emergence of frailty. This correlation aligns well with previous research67. Aging as a significant risk factor for frailty, particularly in the context of chronic diseases, depressive symptoms, and a general decline in cognitive and functional abilities. As individuals age, the likelihood of developing frailty increases, reflecting the cumulative impact of physiological changes and environmental factors. Moreover, our study has uncovered an intriguing trend regarding familial structures and frailty. Specifically, we have noted a higher incidence of frailty among HD patients who are part of families with children. This finding may be attributed to the increased demands placed on caregivers, who often devote significant time and energy to their children’s education, potentially compromising their own health and well-being. When considering socioeconomic factors, we have observed that frailty is less prevalent among patients with higher per capita monthly household incomes. This could be explained by the fact that a greater financial cushion enables patients to access superior HD treatments and a broader range of healthcare services, thereby mitigating the risk of frailty.
In addition to socioeconomic considerations, lifestyle habits also play a pivotal role in the development of frailty. This aligns with previous research that has linked unhealthy lifestyle choices to an increased risk of frailty. For instance, Sho Kojima and his team have demonstrated a connection between frailty and a decline in exercise capacity among HD patients32. Similarly, Cynthia Delgado and her colleagues have found that newly initiated dialysis patients who report frailty have a significantly higher incidence of medically urgent falls or fractures compared to those without frailty36. This finding resonates with our own observations, further highlighting the importance of preventing falls in dialysis patients, as they can significantly elevate the risk of pre-frailty. Furthermore, a study has also observed a link between frailty and specific medical conditions in HD patients. Serum parathyroid hormone concentrations, as well as CKD-mineral and bone disorder (CKD-MBD) medication, have been associated with an increased risk of fracture in patients with ESRD patients36. Similarly, a prospective Taiwanese cohort study has revealed a correlation between frailty and a higher incidence of vascular access thrombosis54. Notably, in this study, central venous catheters and artificial blood vessels were identified as posing a greater risk of frailty compared to arteriovenous fistulas.
The phenomenon of frailty in HD patients is intricately linked to a range of comorbidities, prior among them being their primary illness, congestive heart failure, and other cardiac afflictions. Our current research has revealed a significant association between the presence of comorbidities such as diabetes, heart disease, and cerebral disease and a heightened incidence of frailty in HD patients, compared to those without such comorbidities. A noteworthy study has underscored diabetes mellitus as a pivotal risk factor for the progression and manifestation of frailty8. Kakio and colleagues have further corroborated this by highlighting the elevated predisposition of diabetic renal disease patients to frailty compared to their non-diabetic CKD counterparts68. The heightened risk in diabetic patients can be attributed to the confluence of neuropathy and physiological dysfunction, cognitive impairment stemming from cerebrovascular disease or brain degeneration, inflammatory mechanisms, and the loss of self-care abilities69,70. Additionally, insulin resistance, hyperglycemia, and diabetic neuropathy in diabetic patients may contribute to skeletal muscle loss, thereby enhancing frailty. Moreover, HD patients suffering from congestive heart failure or other cardiac conditions exhibit a fivefold higher risk of developing pre-frailty and frailty compared to patients with other diseases26. This correlation can be explained by the shared etiological factors between frailty and cardiovascular disease, including inflammatory response, endothelial dysfunction, and testosterone insufficiency. Chronic inflammation, specifically, plays a pivotal role in frailty by precipitating other illnesses. Cerebrovascular disorders, in particular, are linked to atherosclerosis, which alters systemic metabolism and pathophysiology, thus escalating the risk of frailty. Consequently, the imperative lies in the management and control of comorbidities to bolster the physical constitution and reduce the incidence of frailty among HD patients. This approach not only addresses the comorbidities themselves but also is to mitigate their detrimental impact on the overall frailty status of HD patients.
Frailty status, medical coping modes, and psychological resilience in HD patients
The state of frailty, medical coping mechanisms, and the psychological resilience exhibited by HD patients rarely face scrutiny in the medical literature. The frequency of HD sessions, often exceeding two per week71, poses unique challenges for patients. The recurring punctures, limitations on fluid intake, and dietary restrictions, as well as potential complications that may arise during these regular treatments, can significantly contribute to psychological distress among HD patients. This distress often manifests in the form of anxiety72, depression73, and a heightened sense of symptom burden74. Moreover, the economic burden of regular dialysis sessions, prolonged medication regimens, and frequent laboratory tests can impose a substantial financial strain on patients’ families. This financial pressure can lead to feelings of guilt and further exacerbate the psychological stress experienced by these patients75.
When it comes to psychological resilience, this refers to a person’s capacity to adapt to and overcome physiological stress, particularly the kind that significantly disrupts normal physiological equilibrium. It is noteworthy that as individuals age, their psychological resilience tends to diminish while frailty concurrently increases. Notably, research has indicated that HD patients exhibit consistently weaker psychological resilience compared to the general population76. Early research suggested that the loss of early physiological resilience is a factor of frailty77. This study found a negative association between psychological resilience, pre-frailty, and frailty among HD patients, which is similar to the early study7. As a result, psychological resilience could be regarded as a protective factor against pre-frailty and frailty. Patients with higher psychological resilience function better in the face of physical challenges. In the quest to understand and manage these complex psychological and medical challenges, it is crucial to delve deeper into the various coping strategies and resilience factors that may help HD patients navigate their treatment journey with greater ease. As a result, psychological treatment should be addressed in order to promote psychological resilience and reduce the incidence of pre-frailty and frailty in HD.
The concept of coping style denotes the diverse strategies individuals adopt when confronted with diverse traumatic life experiences. These varying coping techniques exhibit differing impacts on the progression of various illnesses. Three fundamental styles of coping are commonly recognized: confrontation, avoidance, and resignation. Notably, the confrontational approach is deemed positive, whereas the other two methodologies are regarded as negative78. Our in-depth analysis has unearthed a positive correlation between frailty and the resignation-based coping mode, whereas psychological resilience is positively linked to confrontation-based and avoidance-based coping, while inversely related to resignation-based coping.
The confrontation-oriented coping style signifies that patients maintain a high level of attention to their illness and actively seek constructive help and support. This approach mitigates feelings of anxiety and despair, fostering both physical and mental well-being, while also alleviating caregiver stress79,80. Conversely, patients’ negative coping mechanisms intensify caregiving demands, augmenting the responsibilities associated with daily living and social interactions, ultimately elevating the risk of mental health issues such as depression and anxiety81. This, in turn, undermines the quality of care provided82. Our findings align with previous research conducted by Alshammari et al., which revealed that patients’ attitudes towards their illness, the duration of care, and the nature of their therapy all contribute to the caregiver’s burden83,84. A qualitative study found avoidance, vigilance, and resignation were some HD patients’ primary coping strategies by individual, interpersonal, organizational, community, and societal influences85. To mitigate the caregiver burden and improve quality of nursing, medical professionals can evaluate and address patients’ coping styles through cognitive-behavioral therapy. Additionally, they should remain vigilant to patients’ psychological fluctuations and offer tailored psychological support to foster a positive approach towards disease management.
Strengths and limitations
This study has several strengths. Firstly, it was conducted at multiple centers in central China. Additionally, it further elucidates the relationship between psychological resilience and pre-frailty and frailty. However, there are also limitations to consider. The use of convenience sampling may introduce selection bias and limit the generalizability of the results. Furthermore, the cross-sectional design impedes the establishment of a temporal relationship among the variables studied and pre-frailty and frailty. Future research should utilize longitudinal designs, ideally with samples collected from various medical centers and geographic locations. Besides, our study is a locality-based study. As a result, conclusions may not be universally applicable, necessitating additional research in other regions. Additionally, due to financial constraints, laboratory indicators were not included in this study. Given the importance of laboratory indicators in HD patients, future studies should investigate the association among these indicators and pre-frailty and frailty.
Conclusion
Our findings point to the necessity for active screening for pre-frailty (26.2%) and frailty (38.5%) in adult maintenance HD patients of all ages. Frailty is positively associated with increasing age, poor monthly household income, smoking, drinking, non-exercise, falls, various vascular access, diabetes, heart disease, cerebral disease, resignation, and low psychological resilience. Weight gain, smoking, falls, heart disease, and a lack of psychological resilience are all related to pre-frailty. Pre-frailty and frailty among HD patients are associated with lower psychological resilience and a higher likelihood of using negative coping mechanisms. As a result, medical workers must give patients greater health education and psychological support in order to boost their confidence in illness prevention and improve their quality of life.
Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
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We appreciate the staff working at the Blood Purification Center.
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This work was supported by the Hubei Provincial Natural Science Foundation (2022CFB873) and the Innovative Research Programme of Xiangyang No.1 People’s Hospital (XYY2023SD11).
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S.J. and Y.Z.: Methodology, data analyses and writing-original draft. N.Z.: Supervision and funding acquisition. S.Z., Y.X., Q.Q. and X.Q.: Data collection. L.R. and Y.J.: Review and editing. All the authors read and approved the final manuscript.
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Jiang, S., Zhou, Y., Zhang, N. et al. Prevalence and risk factors of pre-frailty and frailty in hemodialysis patients in central China. Sci Rep 14, 30660 (2024). https://doi.org/10.1038/s41598-024-79855-5
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DOI: https://doi.org/10.1038/s41598-024-79855-5
