Abstract
Objectives:
Health-related quality of life (HRQoL) measures capture the patient’s experience of the burden of chronic disease and are strongly associated with adverse health-related outcomes across multiple populations. The SF-36 score is the most widely used HRQoL measure among patients with end-stage renal disease (ESRD). Current understanding of determinants of the physical component summary (PCS) and the mental component summary (MCS) and their association with objectively measured physical performance and activity is limited.
Design:
Cross-sectional study.
Setting and Subjects:
As an index of HRQoL, we prospectively examined the association of SF-36 and its component scores with physical function among 155 incident dialysis patients from the Hemodialysis Center. We investigated associations of HRQoL with the physical performance-based components of the frailty using multivariate linear and logistic regression after adjustment for confounders.
Predictor:
HRQoL score.
Main outcome:
Impaired physical performance was defined as having either slow usual gait speed or weak handgrip strength based on standardized and validated criteria derived from a large cohort study of older adults.
Results:
The patients had a mean age of 65 ± 11 years, and 52.3% were male. After adjusting confounders, lower PCS was independently associated with decreased physical performance and reduced physical activity, but MCS was not associated. Among the PCS subscales, only physical functioning 10 (PF-10) was consistently associated with outcomes, and every 1 point increase in PF-10 score was associated with 4% lower odds of impaired physical performance (95%CI: 2 to 7%; P=0.01) after adjustment.
Conclusions:
SF-36, especially PF-10, is a valid surrogate that discriminates low physical performance and physical inactivity in the absence of formal physical function testing in patients on hemodialysis. The routine implementation of the PF-10 in clinical care has important clinical implications for medical management and therapeutic decision-making in patients undergoing hemodialysis.
Keywords: quality of life, dialysis, frailty, physical performance, QoL
Introduction
Maintenance of functional independence is the top healthcare priority among older adult patients with advanced kidney disease.1, 2 Mobility limitation is often the first sign of functional limitations leading to dependence and a critical component of the frailty phenotype, a condition characterized by physiologic vulnerability to stress, which is highly prevalent among patients with end-stage renal disease (ESRD).
Two key components of frailty are reduced physical activity and decreased physical performance,3 which are strongly associated with greater risk of mortality,4–6 and are modifiable with structured exercise interventions.7 Slow gait speed and weakness are two physical performance based measures integrated in the physical frailty phenotype strongly associated with mortality in patients on hemodialysis.3 Although the assessment of physical activity and performance is essential for clinical management and therapeutic decision-making, it is not feasible in routine practice for populations undergoing hemodialysis because it is a time-consuming method and resources are limited. An assessment tool is required that can be used conveniently, rapidly, and securely in clinical practice to screen physical conditions in patients on hemodialysis.
The Medical Outcomes Study 36-Item Instrument Short Form Health Survey (SF-36) is one of the most widely used HRQoL instruments for the routine clinical care of vulnerable patients treated with hemodialysis; it assesses the patient experience of the physical and emotional burden of kidney disease. The SF-36 subjectively assesses 2 composite measures of HRQoL: the physical component summary (PCS) and the mental component summary (MCS).8 Some researchers have advocated the use of readily available self-reported physical functioning 10 (PF-10) score of HRQoL as an initial screening tool to identify patients at risk of physical frailty in lieu of measures of impaired physical performance in patients with kidney disease.9, 10 Although self-reported physical functioning seems to be closely linked to objectively measured physical performance and activity, few studies have reported this association.
Gaps in knowledge exist on how closely correlated self-reported function by SF-36 is with directly measured function components of the physical frailty phenotype among patients with ESRD treated with hemodialysis. In the current investigation, we identified the determinants of PCS and MCS scores and describe how closely correlated the two composite measures of HRQoL, PCS, and MCS are with objectively measured physical performance and activity, key components of the physical frailty, in patients who require hemodialysis therapy.
Methods
Study populations
Clinically stable outpatients in a hemodialysis unit were assessed for their eligibility to be included in this cross-sectional study. Patients were undergoing maintenance hemodialysis therapy 3 times per week. Patients were excluded from our study if they had been hospitalized within 3 months prior to the study, suffered from a recent myocardial infarction or angina pectoris, had uncontrolled cardiac arrhythmias, hemodynamic instabilities, uncontrolled hypertension, or renal osteodystrophy with severe arthralgia, or required assistance in walking from another person. This study was approved by the Research Ethics Committee and conducted in accordance with the principles of the Declaration of Helsinki.
Patient characteristics
Information on demographic factors (age, sex, dialysis vintage), physical constitution [body mass index (BMI)], primary kidney disease, comorbid conditions, laboratory parameters (serum albumin, serum hemoglobin, C-reactive protein level and serum total protein levels), and depression symptoms [The 10-item Center for Epidemiologic Studies Depression Scale (CES-D)]11 was collected at the time of the patients’ entry into the study. A comorbidity index, which was developed for patients on dialysis and comprised ESRD primary causes, atherosclerotic heart disease, congestive heart failure, cerebrovascular accident/transient ischemic attack, peripheral vascular disease, dysrhythmia, other cardiac diseases, chronic obstructive pulmonary disease, gastrointestinal bleeding, liver disease, cancer, and diabetes, was used to quantify comorbid illnesses.12 This score was calculated using the aforementioned method and showed good performance in survival analysis in patients undergoing hemodialysis.12
HRQoL
The SF-36 instrument is a self-assessment tool of physical and mental health that comprises 36 questions. These items are organized into 8 quality of life dimensions and compressed into 2 primary dimensions: PCS and MCS.8 Summary scores are transformed into a scale, 0 to 100, and higher scores equate to better HRQoL. The PCS has five domains including physical functioning 10 (PF-10), role physical, bodily pain (BP), vitality (VT) and global health. The MCS has 4 domains, VT, social functioning, role emotional, and mental health, which are scored on a positive scale. The composite score and each domain were included in the analysis.
Physical activity
The number of steps was recorded by the accelerometer. (Lifecorder; Suzuken Co., Ltd., Nagoya, Japan). The instrument was worn around the waist and measured motion as the acceleration of the body. Patients were instructed to wear the accelerometer continuously during their waking hours for 7 days and to avoid getting it wet, such as during bathing. Patients were asked to maintain their typical weekly schedules. The accuracy and reliability of the instrument have been reported.13, 14
Physical performance
Physical performance tests were conducted before or after a dialysis session based on each patient condition by physical therapists who were instructed in the assessment of physical performance by a supervisor and had received sufficient training before measuring physical performance in patients. We included the following assessments:
Handgrip strength was measured using a digital dynamometer (TKK 5101 Grip-D; Takei, Tokyo Japan) with the patients in the sitting position.15, 16 Maximal isometric voluntary contractions of the hands for 3 s each were collected for both hands. Handgrip strength was measured twice on each side, and the highest value was used in the analyses. Isometric knee extensor strength was assessed with a hand-held dynamometer (μTas MT-1; Anima, Japan). To assess knee extensor strength, patients were seated on a bed in an upright posture, feet over the side of the bed, hands on the bed, and knees flexed to 90°. The dynamometer pad was placed perpendicular to the leg just above the malleoli. Patients were told to push against the dynamometer pad by attempting to straighten their knees for a period of 5 s. The physical therapists asked patients to increase force gradually to the maximum voluntary effort. Isometric knee extensor strength was measured 3 times on each side, and the highest value for the right and left legs was used to calculate the average knee extensor muscle strength.5 The value was divided by dry weight and expressed as a percentage (%DW) to adjust for physical constitution between patients.
To test the ability to rise from a chair, patients were asked to fold their arms across their chest and to stand up once from a chair. If successful, they were asked to stand up and sit down five times as quickly as possible and were timed from the initial sitting position to the final standing position at the end of the fifth stands.17, 18
Gait speed was measured as indicator of walking ability. To measure usual gait speed, the patients were asked to walk at their usual pace, and were timed over the middle 10-m of a 14-m walkway with acceleration area. To measure the maximum gait speed, patients were instructed to walk safely as fast as possible without running. The maximum gait speed was defined as the higher value of 2 attempts.5, 19
Timed up and go was used to test the patient’s mobility and measured by recording the time to get up from a fully seated position, walk around a cone placed 4 m away, and then return to a seated position. The faster of 2 trials entered the analysis.20
Impaired physical performance was defined as having either slow usual gait speed or weak handgrip strength. Cut-off point of gait speed was stratified by sex and height, and that of handgrip strength was stratified by gender and BMI (Supplementary Table 1).21 This definition of impaired physical performance was informed by evidence suggesting that, over time, the development of frailty is most commonly characterized by having objective measures of slow gait speed and weak handgrip strength.3 Furthermore, the development of slow gait speed captures nearly all the associations of frailty with mortality.3
Statistical analysis
Data were presented as mean±SD or number (percentage). Patient characteristics, physical performance, and physical activity according to the tertiles of distribution of HRQoL were compared using the Kruskal-Wallis test or chi-square test. The Spearman rank correlation coefficients were used to explore the correlation between HRQoL and physical performance or activity. Multiple linear regression models were used to estimate the association of PCS or MCS with physical performance or activity, adjusting for potential confounders including age, sex, BMI, dialysis vintage, comorbidity score, serum albumin, hemoglobin, and CES-D. These covariates were chosen by authors who contributed to the conception and design of the study based on physiological relevance. We used the Youden Index to determine the optimal cut-off points for PCS and PF-10. To calculate the areas under the curves (AUCs) of PCS and MCS plus patient characteristics for impaired physical performance, receiver-operating characteristic (ROC) curve analysis was performed. We compared AUCs between the 2 models. We investigated the association of PCS, MCS, and 3 major subscales of the PCS, PF-10, vitality and BP, with impaired physical performance using logistic regression analysis adjusted for potential confounders. All statistical analyses were conducted using the JMP® Pro 13.2 (SAS Institute Inc., Cary, NC) and the STATA version 15.1 (StataCorp, College Station, TX). In all analyses, P values of 0.05 or less were used to determine statistical significance.
Results
Patient characteristics and correlates of HRQoL
A total of 284 outpatients were assessed for their eligibility. Of these patients, 30 patients not satisfying the inclusion criteria were excluded, 65 patients declined to participate in this study, and 34 patients with missing data were excluded. Therefore, 155 patients were included in this study (Supplementary Figure 1). Patient characteristics are listed in Table 1 and Supplementary Table 2. Supplementary Figures 2, 3, and 4 show the correlations between HRQoL and physical performance or activity. PCS was significantly correlated with physical performance and activity measures. The strongest correlations were noted with usual gait speed (r=0.50, P<0.001), maximum gait speed (r=0.43, P<0.001), timed up and go test at usual pace (r=−0.51, P<0.001), and timed up and go test at maximum pace (r=−0.43, P<0.001). PCS was also correlated with the number of steps on a nondialysis day (r=0.37, P<0.001), and number of steps on a dialysis day (r=0.44, P<0.001). In contrast, MCS was not significantly associated with physical performance or activity.
Table 1.
Patient characteristics, physical performance and physical activity
| overall | |
|---|---|
| (n=155) | |
| PCS | 36.6 ± 14.5 |
| MCS | 49.3 ± 9.6 |
| Age (years) | 64.9 ± 10.9 |
| Men (%) | 81 (52.3) |
| Dialysis vintage (years) | 10.5 ± 9.8 |
| BMI (kg/m2) | 21.5 ± 4.0 |
| Primary kidney disease (%) | |
| Diabetes | 46 (29.7) |
| Hypertension | 12 (7.7) |
| GN/cystic kidney disease | 54 (34.8) |
| Others | 24 (15.5) |
| Unknown | 19 (12.3) |
| Comorbidity (%) | |
| ASHD (%) | 36 (23.2) |
| CHF (%) | 12 (7.7) |
| CVA/TIA (%) | 32 (20.6) |
| PVD (%) | 62 (40.0) |
| Other cardiac diseases (%) | 46 (29.7) |
| COPD (%) | 5 (3.2) |
| GI (%) | 13 (8.4) |
| Liver disease (%) | 14 (9.0) |
| Dysrhythmia (%) | 31 (20.0) |
| Cancer (%) | 29 (18.7) |
| Diabetes (%) | 61 (39.4) |
| Comorbidity score (points) | 5.7 ± 3.3 |
| Laboratory data | |
| Total protein (g/dL) | 6.4 ± 0.5 |
| Serum albmin (g/dL) | 3.7 ± 0.3 |
| Hemoglobin (g/dL) | 11 ± 2.4 |
| CRP (mg/dL) | 0.5 ± 0.7 |
| Physical performance | |
| Handgrip strength (kg) | 24.9 ± 8.4 |
| Isometric knee extensor strength (%DW) | 44.2 ± 13 |
| Usual gait speed (cm/s) | 119.5 ± 31 |
| Maximum gait speed (cm/s) | 162 ± 41.9 |
| Chair stand test (s) | 9.1 ± 3.3 |
| Timed up and go test at usual pace (s) | 10.6 ± 4.1 |
| Timed up and go test at maximum pace (s) | 7.9 ± 2.5 |
| Number of steps per a day (steps) | |
| Non-dialysis day | 4518 ± 3327 |
| Dialysis day | 3282 ± 2810 |
| CES-D 10 (score) | 7.8 ± 4.4 |
PCS, physical component summary; MCS, mental component summary; GN, glomerulonephritis; ASHD, atherosclerotic heart disease; CHF, congestive heart failure; CVA/TIA, cerebrovascular accident/transient ischemic attack; PVD, peripheral vascular disease; COPD, chronic obstructive pulmonary disease; GI, gastrointestinal bleeding; CES-D 10, Center for Epidemiological Studies Depression Screening Index 10 items; DW, dry weight.
Data are expressed as mean ± SD or number (percentage).
Association of HRQoL on physical performance and physical activity
PCS was independently associated with most measures of physical performance and activity even after adjustment for the effect of patient characteristics (Table 2). After adjustment, the strongest correlates of PCS were usual gait speed and number of steps (Supplemental Table 3 presents adjusted correlations). Following adjustment, each 1 point increase in PCS score was associated with a 0.11%DW greater knee extension strength (95% confidence interval (CI): 0.01 to 0.20; P=0.03), a 0.93 cm/sec greater usual gait speed (95%CI: 0.54 to 1.33; P<0.001), a 0.62 cm/sec greater maximum gait speed (95%CI: 0.11 to 1.13; P=0.01), a 0.10 second faster timed up and go (95%CI: 0.04 to 0.15; P P<0.001), and 88.5 greater steps (95%CI: 40.2 to 136.7; P<0.001). In contrast, MCS was not associated with these outcomes.
Table 2.
Coefficients represent the estimated change in strength, physical performance or physical activity for each 1-point increase in PCS or MCS.
| Univariate | Multivariatea | |||||
|---|---|---|---|---|---|---|
| Coefficient | 95%CI | p | Coefficient | 95%CI | p | |
| Handgrip strength (kg) | ||||||
| PCS | 0.21 | 0.12 to 0.30 | <0.001 | 0.04 | −0.04 to 0.13 | 0.35 |
| MCS | −0.08 | −0.22 to 0.06 | 0.23 | −0.03 | −0.15 to 0.09 | 0.64 |
| Isometric knee extensor strength (%DW) | ||||||
| PCS | 0.23 | 0.14 to 0.32 | <0.001 | 0.11 | 0.01 to 0.20 | 0.03 |
| MCS | −0.09 | −0.24 to 0.06 | 0.47 | −0.01 | −0.14 to 0.12 | 0.75 |
| Usual gait speed (cm/s) | ||||||
| PCS | 1.19 | 0.90 to 1.47 | <0.001 | 0.93 | 0.54 to 1.33 | <0.001 |
| MCS | 0.10 | −0.42 to 0.63 | 0.69 | 0.25 | −0.30 to 0.81 | 0.14 |
| Maximum gait speed (cm/s) | ||||||
| PCS | 1.49 | 1.07 to 1.90 | <0.001 | 0.62 | 0.11 to 1.13 | 0.01 |
| MCS | −0.24 | −0.96 to 0.47 | 0.51 | 0.04 | −0.67 to 0.75 | 0.91 |
| Timed up and go test at usual pace (s) | ||||||
| PCS | −0.15 | −0.19 to −0.11 | <0.001 | −0.10 | −0.15 to −0.04 | <0.001 |
| MCS | −0.03 | −0.10 to 0.04 | 0.44 | −0.02 | −0.10 to 0.05 | 0.54 |
| Physical activity (number of steps on a non-dialysis day) | ||||||
| PCS | 107.18 | 73.16 to 141.22 | <0.001 | 88.45 | 40.24 to 136.65 | <0.001 |
| MCS | −0.7 | −58.27 to 56.88 | 0.98 | 48.18 | −17.32 to 113.69 | 0.15 |
PCS, physical component summary; MCS, mental component summary; DW, dry weight; CI, confidence interval.
Multivariate model is a model with PCS, MCS, age, sex, dialysis vintage, body mass index, comorbidity score, serum albumin, serum hemoglobin, and Center for Epidemiological Studies Depression Screening Index 10 item score.
Association of HRQoL with impaired physical performance
Seventy-nine (51.0%) of the patients with either slowness or weakness were identified as impaired physical performance in this study. Tables 3 and 4 showed the associations of the PCS subscales and PCS and MCS with impaired physical performance. Among the PCS subscales, only PF-10 was consistently associated with impaired physical performance after adjusting for patient characteristics, and every 1 point increase in PF-10 score was associated with 4% lower odds of impaired physical performance (95%CI: 2 to 7%; P=0.01).
Table 3.
Coefficients represent the estimated difference in gait speed, physical activity or handgrip strength for each 1-point increase in Physical functioning 10, vitality, or bodily pain score.
| Univariate | Multivariate model 1a | Multivariate model 2b | Multivariate model 3c | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Coefficient | 95% CI | p | Coefficient | 95% CI | p | Coefficient | 95% CI | p | Coefficient | 95% CI | p | |
| Usual gait speed (cm/s) | ||||||||||||
| PF-10 | 0.89 | 0.72 to 1.07 | <0.001 | 0.84 | 0.65 to 1.02 | <0.001 | 0.75 | 0.53 to 0.98 | <0.001 | 0.74 | 0.50 to 0.97 | <0.001 |
| VT | 0.45 | 0.20 to 0.69 | 0.01 | 0.44 | 0.21 to 0.66 | <0.001 | 0.26 | −0.00 to 0.52 | 0.05 | 0.11 | −0.13 to 0.36 | 0.35 |
| BP | 0.43 | 0.21 to 0.64 | 0.01 | 0.40 | 0.20 to 0.60 | <0.001 | 0.20 | −0.02 to 0.43 | 0.08 | −0.02 | −0.23 to 0.20 | 0.88 |
| Physical activity (number of steps per a non-dialysis day) | ||||||||||||
| PF-10 | 78.68 | 57.04 to 100.33 | <0.001 | 68.8 | 45.9 to 91.6 | <0.001 | 62.0 | 34.2 to 89.8 | <0.001 | 56.36 | 27.71 to 85.00 | <0.001 |
| VT | 50.98 | 23.91 to 78.06 | 0.01 | 48.6 | 23.4 to 73.8 | <0.001 | 47.1 | 16.9 to 77.3 | 0.01 | 36.53 | 6.35 to 66.71 | 0.01 |
| BP | 34.99 | 10.51 to 54.48 | 0.01 | 30.5 | 7.18 to 53.7 | 0.01 | 15.7 | −11.4 to 42.8 | 0.25 | −5.85 | −32.45 to 20.75 | 0.66 |
| Handgrip strength (kg) | ||||||||||||
| PF-10 | 0.15 | 0.09 to 0.21 | <0.001 | 0.08 | 0.04 to 0.12 | <0.001 | 0.06 | 0.01 to 0.11 | 0.03 | 0.06 | 0.01 to 0.11 | 0.03 |
| VT | 0.04 | −0.03 to 0.11 | 0.24 | 0.03 | −0.01 to 0.07 | 0.16 | 0.00 | −0.04 to 0.06 | 0.69 | 0.00 | −0.05 to 0.06 | 0.93 |
| BP | 0.05 | −0.01 to 0.11 | 0.09 | 0.03 | −0.01 to 0.07 | 0.15 | 0.00 | −0.05 to 0.05 | 0.99 | −0.01 | −0.06 to 0.03 | 0.55 |
PF-10, physical functioning 10; VT, vitality; BP, bodily pain; CI, confidence interval.
Multivariate model 1 is adjusted for age, sex, and body mass index.
Multivariate model 2 is adjusted for age, sex, body mass index, dialysis vintage, comorbidity score, serum albumin, hemoglobin and Center for Epidemiological Studies Depression Screening Index 10 item score.
Multivariate model 3 is adjusted for age, sex, body mass index, dialysis vintage, comorbidity score, serum albumin, hemoglobin, Center for Epidemiological Studies Depression Screening Index 10 item score, PF, VT and BP.
Table 4.
Health-related quality of life dimensions and physical performance
| Outcome (impaired physical performancea) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Univariate | Multivariate model 1b | Multivariate model 2c | |||||||
| OR | 95% CI | P | OR | 95% CI | P | OR | 95% CI | P | |
| Self-report | |||||||||
| PCS | 0.94 | 0.91 to 0.96 | <0.001 | 0.92 | 0.88 to 0.95 | <0.001 | 0.93 | 0.89 to 0.97 | 0.01 |
| PF-10 | 0.95 | 0.93 to 0.97 | <0.001 | 0.94 | 0.92 to 0.97 | <0.001 | 0.96 | 0.93 to 0.98 | 0.01 |
| VT | 0.98 | 0.96 to 0.99 | 0.02 | 0.97 | 0.95 to 0.99 | 0.01 | 0.98 | 0.96 to 1.01 | 0.26 |
| BP | 0.98 | 0.97 to 0.99 | 0.02 | 0.97 | 0.95 to 0.99 | 0.01 | 0.98 | 0.96 to 1.01 | 0.18 |
| MCS | 1.00 | 0.96 to 1.03 | 0.99 | 0.98 | 0.94 to 1.02 | 0.38 | 1.02 | 0.96 to 1.08 | 0.37 |
| Objective | |||||||||
| Isometric knee extensor strength | 0.94 | 0.91 to 0.97 | <0.001 | 0.91 | 0.88 to 0.95 | <0.001 | 0.92 | 0.88 to 0.96 | 0.01 |
| Physical activity (per 1000 steps) | 0.75 | 0.65 to 0.85 | <0.001 | 0.77 | 0.67 to 0.89 | <0.001 | 0.82 | 0.70 to 0.95 | 0.01 |
PCS, physical component summary; PF-10, physical functioning 10; VT, vitality; BP, bodily pain; MCS, mental component summary; OR, odds ratio; CI, confidence interval.
If subjects correspond to either slow walk time or low grip strength, it was defined as impaired physical performance.
Multivariare model 1 is adjusted for age, sex, and body mass index.
Multivariare model 2 is adjusted for age, sex, body mass index, dialysis vintage, comorbidity score, serum albumin, hemoglobin, and Center for Epidemiological Studies Depression Screening Index 10 item score.
The addition of PCS to a base model including demographics improved the ability to discriminate impaired physical performance compared with a model including demographics and MCS score. The AUCs on ROC analysis were 0.85 (95% CI: 0.79–0.91) for the model including PCS and 0.79 (95% CI: 0.71–0.86) for the model including MCS (P=0.025) (Figure 1). The cutoff values for PCS and PF-10 discriminating those at risk of impaired physical performance based on the Youden Index were 40.5 (AUC: 0.72; 95%CI: 0.63–0.79) and 60.0 (AUC: 0.75; 95%CI: 0.67–0.82), respectively.
Figure 1.
ROC curves of patient characteristics + PCS and + MCS for impaired physical performance.
AUC, area under the curve; BMI, body mass index; CI, confidence interval; MCS, mental component summary; PCS, physical component summary.
Discussion
We investigated the association between HRQoL and physical performance-based components of the frailty in patients undergoing hemodialysis. According to our review of the literature, this study is the first study of patients receiving hemodialysis to evaluate the discriminant ability of PCS and MCS to identify impairments in the physical performance-based components of the frailty phenotype. Importantly, we used validated physical performance cut-points originally defined in the Cardiovascular Health Study to define either slow walk time or low handgrip strength. The section of the SF-36 questionnaire was motivated by its widespread use and well-validated psychometric properties for assessing HRQoL in patients treated with hemodialysis in multiple countries and in multiple languages. We found the PF-10 subscore of the SF-36 was most strongly associated with decreased physical performance and physical inactivity. These findings support the routine implementation of the PF-10 in clinical care to help kidney health providers assess risks of impairments in the physical performance components of frailty to mobilize early, effective disease management to mitigate further functional decline.
Physical frailty is common among patients treated with hemodialysis and strongly associated with adverse health-related outcomes. The range of the prevalence of frailty in patients with CKD who require hemodialysis therapy can be 36.8 to 70% among older patients treated with chronic hemodialysis10, 22, 23 compared with 7.4% in Japanese older individuals without CKD.24 It is known that physical frailty is associated with significantly higher risks of developing disability in basic and instrumental activities of daily living,25 and patients undergoing hemodialysis experience a rapid development of disability, which is associated with an elevated risk of mortality among patients requiring hemodialysis.26 Early identification of physical frailty for patients on hemodialysis using an objectively measured physical performance-based criteria is optimal to diagnose frailty and guide management in patients treated with hemodialysis.
Physical frailty among patients treated with hemodialysis is most commonly characterized by poor physical performance and physical inactivity,3 which can be readily screened by routinely assessed self-reported HRQoL measures in the clinical management of patients. Gait speed is particularly central to physical frailty because the development of slow gait speed has been shown to capture nearly all the association of frailty with mortality. We show that the PCS of the SF-36 and, particularly, the PF-10 domain, are useful for capturing and identifying meaningful impairments in the physical performance-based components of frailty in the clinical setting. Although performance-based frailty criteria remain the gold standard for the diagnosis of frailty, routine objective assessment may interfere with clinical practice flow in dialysis facilities.27 Initial screening using the PCS or more simply the PF-10 introduces a meaningful and readily available clinical tool to help identify patients at risk of functional decline and focus on tailored interventions to improve physical functioning.
We identified the cut-off point of 60 on the PF-10 as the optimal cut point for identifying impaired physical performance among patients undergoing hemodialysis. This value is lower than that from other studies in patients treated with hemodialysis using a cutoff value of 75 derived from the Women’s Health Initiative Observational Study (WHI-OS).2, 28 One of the main reasons for this gap in cutoff point is the distinctive difference in score distributions of PF-10 between the cohort in this study and healthy adults. Another study revealed that 85% to 90% of the patients who were older and dialyzed participating in the Dialysis Morbidity and Mortality Study fulfilled the WHI-OS PF-10 criteria for identifying frailty.10 Reducing the cut-off point for the PF-10 score to 60 may reduce physical frailty misclassification of patients with ESKD without overt mobility limitation. Further investigation is needed to determine the impact of lowering this cut-off point on the effectiveness of rehabilitative therapies to improve mobility and resource utilization in comparison to higher PF-10 thresholds.
The average scores for PCS and MCS observed among Japanese patients treated with chronic hemodialysis in our study were consistent with the results of prior large investigations in the United States. Cohen DE et al. recently conducted a large, nationwide epidemiological cohort study and evaluated HRQoL in 413,951 patients receiving dialysis.29 The average scores of PCS and MSC were 36.6±12.2 and 49.0±13.4 in those study populations, respectively, and consistent with average scores in our study, underscoring the generalizability of our findings. Although another study investigated the association between HRQoL and physical performance in patients treated with hemodialysis, the younger average age of the populations treated with dialysis and limited sample size limited the generalizability of their findings.30 In contrast, our larger sample size and comprehensive measurement of clinical characteristics and laboratory markers allowed us to account for the potential confounding by biologically and clinically relevant factors.
The European Renal Best Practice Guideline Development Group published a new clinical practice guideline for older patients with CKD in 2016; the guideline recommended the use of physical functional assessment tools and interventions aimed at increasing functional status in older patients with renal failure.31 A meta-analysis indicated the effectiveness of supervised exercise interventions on physical performance and PCS of HRQoL for individuals undergoing hemodialysis.32 Exercise interventions are inexpensive, safe, and feasible for patients who are vulnerable and undergoing hemodialysis.
This present study had some limitations. First, our study was limited to a single center potentially limiting the generalizability of our findings to the broader international population receiving hemodialysis. Therefore, further large-scale studies are required. Second, because we evaluated physical performance, physical activity, and HRQoL of patients treated with hemodialysis only at baseline, we could not evaluate fluctuation of those over time. Third, we excluded patients who had overt mobility limitation indicated by their need for assistance with walking. However, the restriction of our study to those free of overt mobility limitation may suggest the estimates of our association of HRQoL with impairments in the physical performance components of frailty may be conservative.
In conclusion, SF-36, especially PF-10, can be conveniently assessed in dialysis facilities as a screening tool for decreased physical performance and physical inactivity and has notable clinical implications for medical management and therapeutic decision-making in patients undergoing hemodialysis.
Practical Application
The physical functioning 10 (PF-10) subscore of the SF-36, one of the most widely used health-related quality of life (HRQoL) instruments, is strongly associated with decreased physical performance and physical inactivity. In the absence of formal physical performance testing, the PF-10 score is a valid surrogate that discriminates low physical performance and physical inactivity. This finding supports the routine implementation of the PF-10 in clinical care to help kidney health providers assess risks of physical frailty to mobilize early, effective disease management to mitigate further functional decline.
Supplementary Material
Supplementary Figure 2. Correlations between HRQoL and muscle strength
MCS, mental component summary; PCS, physical component summary.
Supplementary Figure 1. Flowchart of study participants.
Supplementary Figure 3. Correlations between HRQoL and physical performances
MCS, mental component summary; PCS, physical component summary.
Supplementary Figure 4. Correlations between HRQoL and habitual physical activity
MCS, mental component summary; PCS, physical component summary.
Acknowledgement:
The authors thank the patients for giving their time and the study nurses, physicians, and medical directors for all the time and attention they have devoted to our study. This study was supported in part by grants from the JSPS KAKENHI: 20K19332 (RM) and the NIDDK: K23DK099442 (BR), R03 DK114502 (BR), Dialysis Clinics Incorporated C-4112 (BR).
Support and financial disclosure:
R.M., B.R. and A.M. contributed to the conception and design; Y.S., S.Y., M.H., T.W., and T.S contributed to the acquisition of data or analysis and interpretation of the data; Y.S., S.Y., and R.M. contributed to making figures; R.M., B.R., A.M., A.Y., C.D., and A.T. contributed to drafting the article or revising it critically for important intellectual content; and all authors approved the final version of the manuscript. The authors declare that they have no relevant financial interests. The content is solely the responsibility of the authors. The results presented in this article have not been published previously, in whole or part, except in abstract format.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Figure 2. Correlations between HRQoL and muscle strength
MCS, mental component summary; PCS, physical component summary.
Supplementary Figure 1. Flowchart of study participants.
Supplementary Figure 3. Correlations between HRQoL and physical performances
MCS, mental component summary; PCS, physical component summary.
Supplementary Figure 4. Correlations between HRQoL and habitual physical activity
MCS, mental component summary; PCS, physical component summary.