Introduction
Chronic kidney disease (CKD) is a global health concern. As of 2017, approximately 690 million people globally were affected by CKD, with a global prevalence of 9.1% (Bikbov et al., 2020). Approximately 10 million people die from kidney-related diseases every year (Xie et al., 2018). Patients with end-stage CKD require dialysis treatment to effectively improve their survival rates and quality of life (Olaitan et al., 2019). These patients begin dialysis via either planned or unplanned dialysis. Planned dialysis refers to a patient receiving dialysis-access surgery (i.e., an arteriovenous fistula, permanent catheter implant, or peritoneal dialysis catheter implant) following the advice of a doctor (Moist & Lok, 2017). Unplanned dialysis refers to a patient having a central venous catheter inserted under life-threatening circumstances and accepting dialysis during acute hospitalization after initial rejection of a nephrologist's advice (Schanz et al., 2017). Although approximately 40%-60% of patients with end-stage CKD receive unplanned dialysis (Machowska et al., 2016), the 3-month survival rate of patients with unplanned dialysis is only 38.6%, which is significantly lower than the 90.9% for those with planned dialysis (Roy et al., 2017). In Taiwan, the mortality rate for patients with unplanned dialysis is significantly higher than that for patients who use other dialysis methods (Tu et al., 2020). Caring for patients with unplanned dialysis is an important issue for healthcare providers.
Patient-related factors, including disease denial, avoidance of discussing management, and fear of dialysis, are the main reason for patients delaying dialysis (Chia et al., 2021). Patients at the denial and avoidance levels usually do not want to know any information about dialysis (Griva et al., 2020). Poor self-management behavior has been associated with a higher mortality rate in patients on long-term hemodialysis (Golestaneh, 2018; Jasinski et al., 2018). Patients with unplanned dialysis must perform self-management behaviors to maintain their health in the community after discharge from the hospital. Understanding the predictors of postdischarge self-management behaviors in patients with unplanned dialysis is helpful for nurses to design effective discharge plans. However, few studies have been conducted to address this issue. Self-management behaviors in patients receiving hemodialysis refer to individuals changing their lifestyles, reducing complications caused by the disease, and alleviating management problems in response to end-stage renal disease (Lin et al., 2017). Self-care activities, partnership, problem solving, and emotion processing are considered important components of self-management behaviors in patients on long-term hemodialysis. Self-care activities include dialysis treatment compliance, diet control, fluid intake limitation, medication, blood pressure and blood sugar control, arteriovenous fistula care, and exercise maintenance (Gela & Mengistu, 2018). Problem-solving includes problem identification, selection of suitable problem-solving methods, and evaluation of the effects (Golestaneh, 2018). Partnership includes communication with professional personnel, information sharing, and cooperation with the medical team for disease control (Vandenberg et al., 2019). Emotional processing includes acceptance of role change, expression of feelings, learning relaxation techniques, and psychological support (El-Etreby & El-Monshed, 2019).
Many psychosocial behaviors have the potential to influence the self-management efficacy of patients on hemodialysis. Knowledge of the disease can enhance adherence to treatment and compliance with continuous treatment (Wu et al., 2016). The results of prior research indicate that self-care knowledge is positively correlated with the self-management behaviors of patients on long-term hemodialysis (Gela & Mengistu, 2018; Schrauben et al., 2020). As providing knowledge related to performing self-care before patient discharge may predict postdischarge self-management behaviors in patients with unplanned dialysis, this issue deserves further study.
Uncertainty in illness refers to uncertainty regarding events related to a disease and the inability to explain the associated changes in health status (Mishel, 1988). For patients with CKD, complexity of treatment procedure, the presence of symptoms, and comorbidities and complex self-care activities skills may cause patients to be less certain of the future, resulting in higher illness uncertainty (Llewellyn, 2017). In particular, patients with unplanned dialysis receive hemodialysis unexpectedly, which increases uncertainty at hospital admission. Several quantitative studies have found a negative correlation between uncertainty in illness and self-management behaviors in patients on long-term hemodialysis (Cho et al., 2018; Kim & Kim, 2019). Nevertheless, the issue of predicting illness uncertainty during hospitalization in relation to postdischarge self-management behaviors in patients with unplanned dialysis has rarely been discussed.
Social support, referring to individuals perceiving help and protection from others when under pressure (Barrera, 1986), includes aspects of emotion, substance, information, and evaluation support (Yu et al., 2004). Social support is considered one of the psychosocial factors that helps maintain favorable patient self-management behaviors and disease control (Jasinski et al., 2018). A systematic review discovered that social support for patients on long-term hemodialysis is positively correlated with self-management behaviors (Sousa et al., 2019), although few studies have been conducted to examine the prediction of social support during hospitalization on postdischarge self-management behaviors in patients with unplanned dialysis.
Various studies have determined that older age and lower educational level correlate with poorer self-management behaviors in patients with CKD (Gela & Mengistu, 2018; Wang et al., 2019). Moreover, patients who had received CKD health education had better self-management behaviors (Wang et al., 2019). Demographic characteristics should be considered covariates when exploring predictors of self-management behaviors in patients with unplanned dialysis. The purpose of this study was to explore the prediction values of uncertainty in illness, self-care knowledge, and social support during hospitalization on self-management behaviors at 3 months postdischarge in patients with unplanned dialysis.
Methods
Study Design
This was a prospective study. The participants were patients with unplanned dialysis who were in critical condition at admission and required medical treatment during their first week of hospitalization. The demographic characteristics, uncertainty in illness, self-care knowledge of diseases, and social support details of the participants were collected within 2 weeks of hospitalization. Because all of the participants had been hospitalized for unplanned dialysis, they had not performed self-management behaviors but were expected to establish stable self-management behaviors by 3 months postdischarge. Therefore, self-management behaviors at 3 months postdischarge were measured and collected when the patients returned to the hospital for dialysis or when visiting outpatient clinics. The data were collected from February 2019 to February 2020.
Participants and Data Collection
This study used convenience sampling to recruit patients receiving unplanned dialysis at a medical center in southern Taiwan. The inclusion criteria were as follows: (a) aged 20-80 years; (b) no prior dialysis experience and no arteriovenous fistula, port-a-cath, tunneled cuffed catheter, or peritoneal dialysis tube inserted in advance; (c) had a central venous catheter inserted for dialysis after emergency assessment by a nephrologist; and (d) able to communicate in Mandarin or Taiwanese. Participants with mental diseases who could not communicate logically or who did not receive regular dialysis were excluded.
G*Power 3.1.9.2 was used to calculate the minimum sample size. f2 = 0.15 was set according to the medium effect in multiple regression analysis; power and [alpha] value were set at .8 and .05, respectively; and the estimated minimum sample size was 118 (Cohen, 1988). Thus, 200 patients with unplanned dialysis who met the inclusion criteria were recruited. Because eight of the patients had died and another 23 were lost to follow-up, valid data from 169 participants were collected and analyzed (response rate: 84.5%).
This study was approved by the institutional review board of the case hospital (IRB Case Number 20190112B0). All of the participants provided either verbal or written consent and signed a consent form. The participants were informed that they could freely refuse to participate without fear of reprisal. During hospitalization, all of the participants continued to receive related routine health education and treatment.
Measures
Data were collected using a self-reported questionnaire that included the several scales described in the following paragraphs. The test-retest reliability for a 2-week interval was tested in a pilot study of 20 patients with unplanned dialysis at hospitalization.
Demographic Characteristics
Demographic characteristics collected for this study included gender, age, marital status, educational level, employment, religion, comorbidity status, prior participation in a CKD education program, and family dialysis experience.
Uncertainty in illness scale
The 25-item Chinese version of Mishel's Uncertainty in Illness Scale was used to assess uncertainty in illness (Sheu & Huang, 1996), with 15 items addressing "uncertainty factors" and 10 items addressing "complexity factors." Each item is scored from strongly disagree (1) to strongly agree (5). The total score divided by the number of items is used to represent the item mean score, with higher item mean scores associated with higher uncertainty in illness. In this study, the overall Cronbach's [alpha] value for this scale was .96 and the test-retest reliability for the 2-week interval was .97.
Chronic kidney disease self-care knowledge scale
The 10-item chronic kidney disease self-care knowledge scale was used to assess self-care knowledge (Wu et al., 2016). The scale uses true/false responses, with correct answers scoring 1 point and incorrect or unknown answers scoring 0 points. Higher item mean scores are associated with better self-care knowledge. In this study, the overall Kuder-Richardson 20 was .72 and the test-retest reliability for the 2-week interval was .87.
Chinese version of the medical outcomes study social support survey
The 20-item Chinese version of the Medical Outcomes Study Social Support Survey was used to assess social support (Yu et al., 2004). In this scale, eight, four, three, and five items are used to assess social interactivity in the emotional-informational, tangible, affectionate, and positive subdimensions, respectively. Each item is rated from never (0 points) to always (5 points), with higher item mean scores associated with higher social support. In this study, the Cronbach's [alpha] value was .97 and the test-retest reliability for the 2-week interval was .96.
Self-management scale
The 20-item Chinese version of the self-management scale was used to assess self-management behaviors (Song & Lin, 2009). In this scale, five items measure problem-solving skills, four assess emotional management, seven assess self-care, and four assess partnership. Each item is rated from never (0 points) to always (4 points), with higher item mean scores associated with better self-management behaviors. In this study, the Cronbach's [alpha] value was .96 and the test-retest reliability for the 2-week interval was .97.
Data Analysis
The IBM SPSS Statistics Version 20.0 (IBM Inc., Armonk, NY, USA) was used for data analysis. Descriptive analysis was used to examine the distribution of the variables. The independent samples t test, one-way analysis of variance, and Pearson product-moment correlation analysis were employed to analyze associations between variables. Hierarchical linear multiple regression was used to analyze the important explanatory factors related to self-management behaviors at 3 months postdischarge.
Results
Participant Characteristics
Men comprised 56.8% of the participants in this study. The mean age of the participants was 60.45 +/- 12.90 years. The relationships between demographic characteristics and self-management behaviors at 3 months postdischarge are presented in Table 1. Older age was associated with poorer self-management behaviors at 3 months postdischarge. The self-management behaviors of participants with an educational level higher than college were significantly better than those with a senior high school or lower level of education. The self-management behaviors of participants who had participated in a CKD education program were significantly better than their peers who had not. The other demographic characteristics considered in this study were not associated with significant differences in self-management behaviors at 3 months postdischarge.
The Distributions of Uncertainty in Illness, Self-Care Knowledge, Social Support, and Self-Management Behaviors 3 Months Postdischarge and Their Intercorrelations
The item mean score for uncertainty in illness was 2.86 (SD = 1.09), with the score of the uncertainty factor higher than that of the complexity factor. The item mean score for self-care knowledge was 0.76 (SD = 0.21). The item mean score for social support was 3.09 (SD = 1.20), with the lowest mean score earned in the emotional-informational subdimension. The item mean score for self-management behaviors at 3 months postdischarge was 2.43 (SD = 1.06) points, with the lowest mean score earned in the problem-solving skills subdimension (Table 2).
As shown in Table 2, at 3 months postdischarge, uncertainty in illness was significantly negatively correlated with self-management behaviors, whereas self-care knowledge and social support were significantly positively correlated with self-management behaviors.
Results of the Multiple Linear Regression Analyses With Self-Management Behaviors at 3 Months Postdischarge
Age, educational level, and previous participation in a CKD education program, which were all significantly correlated with self-management behaviors 3 months postdischarge in the bivariate analysis, were entered into the hierarchical linear multiple regression analysis. As shown in Table 3, educational level and previous participation in a CKD education program were significant predictive factors of self-management behavior in the first stage, with the regression model explaining up to 13.5% of the total variance in self-management behaviors at 3 months postdischarge. In the second stage, self-care knowledge was added to the hierarchical multiple regression analysis, where educational level and self-care knowledge were shown to be significant predictive factors, with the regression model explaining up to 21.6% of the total variance in self-management behaviors at 3 months postdischarge. Self-care knowledge increased the variance by 8.1%. In the third stage, uncertainty in illness was added to the hierarchical multiple regression analysis, where educational level and uncertainty in illness were found to be significant predictive factors, with the regression model explaining up to 37.7% of the total variance in self-management behaviors at 3 months postdischarge. Uncertainty in illness increased the variance by 16.1%. In the final stage, social support was added to the hierarchical multiple regression analysis, where self-care knowledge, uncertainty in illness, and social support were found to be important predictive factors, with this regression model explaining up to 65.6% of the total variance in self-management behaviors at 3 months postdischarge. Social support increased the variance in self-management behaviors by 27.9%.
Discussion
Few studies have examined the predictors of postdischarge self-management behaviors in patients using a prospective design, with even fewer examining this issue in patients with unplanned dialysis. The findings of this study provide useful knowledge applicable to the design of discharge planning programs for patients with unplanned dialysis. The item mean score for self-management behaviors was 2.43 +/- 1.06 out of 4. This result suggests a medium level of self-management behaviors at 3 months postdischarge, which is lower than the item mean score of 3.0 for the self-care behaviors of Taiwanese patients on long-term hemodialysis (Chen et al., 2020). Compared with other studies conducted non-Taiwanese settings that have used the same questionnaire, the item mean score of the patients in this study was lower than the 2.80 mean self-management score for patients on long-term hemodialysis in Beijing and in several countries in Africa (Gela & Mengistu, 2018; Li et al., 2014). This indicates that the self-management behaviors at 3 months postdischarge of patients with unplanned dialysis are poorer than those of patients on long-term hemodialysis. This finding should be further confirmed in future comparative research studies. With respect to the subdimensions of self-management behavior, problem-solving skills was the poorest. In this study, patients with unplanned dialysis experienced a shorter time on dialysis and had been discharged from hospital for 3 months only. Thus, they may not yet have the knowledge and experience necessary to resolve the problems encountered in postdischarge self-management. Therefore, problem-solving skills earned the poorest score among the self-management behavior subdimensions at 3 months postdischarge. Consequently, problem identification and solving skills should be taught to patients with unplanned dialysis before discharge.
In this study, the average self-care knowledge of the participants was at a medium-to-high level. However, higher self-care knowledge scores were found to correlate with better self-management behaviors at 3 months postdischarge, which is consistent with previous cross-sectional studies (Kim & Kim, 2019; Schrauben et al., 2020). More and better self-care knowledge should be provided to patients with unplanned dialysis during hospitalization to improve their self-management behaviors after discharge.
Consistent with previous cross-sectional studies in patients on long-term hemodialysis (Cho et al., 2018; Kim & Kim, 2019), in this study, higher uncertainty in illness was associated with poorer self-management behaviors at 3 months postdischarge. Furthermore, uncertainty in illness independently explained 16.1% of self-management behaviors. Insufficient information and unpredictable or changing symptoms may contribute to uncertainty in patients (Llewellyn, 2017). Thus, health professionals should provide concrete information regarding the situations they will face in the future to patients with unplanned dialysis during hospitalization to reduce their uncertainty in illness and improve their postdischarge self-management behaviors.
In this study, the participants received a medium level of social support, with the emotional-informational support subdimension scoring the lowest. Emotional-informational support includes providing messages and expressing caring, which deserve particular attention (Reyes et al., 2020). Health professionals should provide care centered on patients' informational needs and active listening to provide emotional-informational support (Sousa et al., 2019). Although many previous cross-sectional studies reinforced that social support is more crucial than uncertainty in illness associated with self-management behaviors in patients on long-term hemodialysis (Kim & Kim, 2019), few prospective studies have examined the prediction of social support related to subsequent self-management behaviors in patients with unplanned dialysis. In this study, social support independently predicted up to 27.9% of variance in self-management behaviors at 3 months postdischarge, which is a greater percentage of the variance predicted by either uncertainty in illness or self-care knowledge. The postdischarge self-management behaviors of dialysis patients are complex, involving an array of concomitant risk factors; thus, patients might need a variety of support for encountered difficulties. Nevertheless, as patients with unplanned dialysis receive dialysis under emergency conditions, their ability to build adequate social support during hospitalization may be particularly limited. Therefore, assisting patients with unplanned dialysis to build social support during hospitalization is crucial.
Limitations
This study was affected by several limitations. First, the participants were recruited from one medical center using convenience sampling. Hence, the results may not be generalizable to patients with unplanned dialysis in other hospital or country settings. Sampling from a diverse range of hospitals/countries is needed in the future. Second, we did not collect or analyze biochemical indicators such as serum phosphorus, serum potassium, and glomerular filtration rate during hospitalization to assess their predictive value on self-management behaviors at 3 months postdischarge. Future studies should include a consideration of biochemical indicators. Third, this study followed up on self-management behaviors at 3 months after discharge only, leaving behaviors over a longer postdischarge period unknown. Future studies may use follow-up periods longer than 3 months to determine longer-term trends in self-management behaviors. Fourth, experimental studies should be used to examine the effect of related self-care knowledge, uncertainty in illness, and social support at discharge interventions on patients' self-management behaviors at 3 months postdischarge. Because the participants in this study did not build self-management behaviors while in the hospital, it was impractical to measure self-management behaviors at discharge and compare the differences between discharge and 3 months postdischarge. As the purpose of this study was to examine the prediction of uncertainty in illness, self-care knowledge, and social support during hospitalization on the self-management behaviors of patients with unplanned dialysis self-management at 3 months postdischarge, the independent variables were measured at discharge but not measured at 3 months postdischarge. Thus, future studies should measure self-care knowledge, uncertainty in illness, and social support at 3 months postdischarge to better clarify their ability to predict self-management behaviors at 3 months postdischarge.
Conclusions
To improve the postdischarge self-management behaviors of patients with unplanned dialysis, healthcare providers should focus on building social support for these patients during discharge planning. Because self-care knowledge and uncertainty in illness were also identified as important predictors of self-management behaviors at 3 months postdischarge, a comprehensive discharge plan for these patients should also involve interventions to improve these factors during hospitalization.
Acknowledgment
The authors thank all of those who participated in this study for their effort and time.
Author Contributions
Study conception and design: RHW
Data collection: RHW, YYK
Data analysis and interpretation: RHW, YYK
Drafting of the article: RHW, YYK
Critical revision of the article: RHW, CTL
References