Background/aims Prevalent dialysis patients have low scores of health-related quality of life (HRQOL) which are associ- ated with increased risk of hospitalization and mortality. Also in CKD-5 non-dialysis patients, HRQOL scores seem to be lower as compared with the general population. This study firstly aimed to compare HRQOL between CKD-5 non-dialysis and prevalent dialysis patients in a cross-sectional analysis and to assess longitudinal changes over 1 year after the dialysis initiation. Secondly, the correlation between HRQOL and physical activity (PA) was explored. Methods Cross-sectional 44 CKD-5 non-dialysis, 29 prevalent dialysis, and 20 healthy controls were included. HRQOL was measured by Short Form-36 questionnaires to measure physical and mental domains of health expressed by the physi- cal component summary (PCS) and mental component summary (MCS) scores. PA was measured by a SenseWear™ pro3. Longitudinally, HRQOL was assessed in 38 CKD-5 non-dialysis patients (who were also part of the cross-sectional analysis), before dialysis initiation until 1 year after dialysis initiation. Results PCS scores were significantly lower both in CKD-5 non-dialysis patients and in prevalent dialysis patients as com - pared with healthy controls (p < 0.001). MCS scores were significantly lower in both CKD-5 non-dialysis patients ( p = 0.003), and in dialysis patients (p = 0.022), as compared with healthy controls. HRQOL scores did not change significantly from the CKD-5 non-dialysis phase into the first year after dialysis initiation. PA was significantly related to PCS in both CKD-5 non-dialysis patients (r = 0.580; p < 0.001), and dialysis patients (r = 0.476; p = 0.009). Conclusions HRQOL is already low in the CKD-5 non-dialysis phase. In the first year after dialysis initiation, HRQOL did not change significantly. Given the correlation between PCS score and PA, physical activity programs may be potential tools to improve HRQOL in both CKD-5 non-dialysis as well as in prevalent dialysis patients. Keywords Health-related quality of life · Physical Activity · End-stage renal disease · Dialysis Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1125 5-018-1845-6) contains supplementary material, which is available to authorized users. * Natascha J. H. Broers Department of Nephrology, Jessa Hospital, Hasselt, Belgium email@example.com Department of Internal Medicine, Division of Nephrology, Viecuri Medical Center, Venlo, The Netherlands Division of Nephrology, Department of Internal Medicine, Maastricht University Medical Center+, PO Box 5800, Department of Internal Medicine, Division of Nephrology, 6202 AZ Maastricht, The Netherlands Catharina Hospital Eindhoven, Eindhoven, The Netherlands 2 7 NUTRIM School of Nutrition and Translational Research Department of Internal Medicine, Division of Nephrology, in Metabolism, Maastricht University, Maastricht, Zuyderland Medical Center, Sittard-Geleen, The Netherlands The Netherlands Department of Internal Medicine, Division of Nephrology, Fresenius Medical Care GmbH, Bad Homburg, Germany Laurentius Hospital Roermond, Roermond, The Netherlands Vol.:(0123456789) 1 3 1132 International Urology and Nephrology (2018) 50:1131–1142 Introduction Materials and methods Numerous studies already showed lower health-related quality This study consisted of a cross-sectional part and a lon- of life (HRQOL) scores in dialysis patients as compared with gitudinal part (Fig. 1). The methodology (as described the general population [1–3]. Low HRQOL scores were shown below) has been described previously . to be predictive of hospitalization and mortality in this patient For the cross-sectional analyses, we included 73 patients, group [4–6]. Nevertheless, in most studies, general trends at 44 CKD-5 non-dialysis and 29 prevalent dialysis patients different time periods with a varying patient cohort were com- (dialysis vintage 3.6 ± 3.2 years) as well as 20 healthy con- pared [1, 7], and only a limited amount of studies followed trols. Patients were recruited from the following dialysis HRQOL in dialysis patients over time in the same patients [8, centers in the South East of the Netherlands and North 9]. A previous study of our group showed the prognostic value East of Belgium: Maastricht University Medical Center+, of changes in HRQOL over time in maintenance hemodialysis Catharina Hospital Eindhoven, Viecuri Hospital Venlo, (HD) patients, but did not include the stage 5 chronic kidney Zuyderland Medical Center Sittard, St Laurentius Hospital disease (CKD-5) non-dialysis phase . Few prospective stud- Roermond and Jessa Hospital Hasselt. CKD-5 non-dialysis ies have focused on the effects of starting dialysis treatment patients were ESRD patients starting with dialysis within on HRQOL [9, 10], despite the fact that in patients with end- 1 month. (Measurements were performed maximum 4 weeks stage renal disease (ESRD) the transition from the CKD-5 prior to the first dialysis session.) Prevalent dialysis patients non-dialysis phase to the start of dialysis is a major life event were treated with hemodialysis (HD) or peritoneal dialysis [11–13]. Theoretically, HRQOL may decrease following the (PD) treatment for at least 12 months. start of dialysis due to the invasiveness of the therapy, or might The longitudinal analyses with regard to HRQOL scores be improved due to the partial resolution of uremic symptoms. included 38 of the CKD-5 non-dialysis patients (who were Previous studies showed a reduction in HRQOL in patients also part of the cross-sectional analysis) in the transitional with CKD-5 in the non-dialysis phase as compared with phase for whom 12-month follow-up data after dialysis the general population [10, 14, 15], but a comparative study initiation were available (for PA analyses 39 patients were between CKD-5 non-dialysis and prevalent dialysis patients, included). Measurements were performed before the start or a longitudinal study following the start of dialysis before of dialysis treatment (within 1 month before the first dialy - dialysis initiation, has not yet been performed. sis session), 5–6 months, and 11–13 months after dialysis In interpreting alterations in HRQOL, it is important to initiation by the same methods as used for the cross-sec- identify potentially modifiable factors. Previously, we observed tional part. In addition, PA was only measured before the a relation between nutritional parameters and changes in start of dialysis and 5–6 months after the start of dialysis, HRQOL . Recently, a larger international cohort study no data were available 1 year after the start of dialysis due observed a significant relation between physical activity (PA) to the study design. and HRQOL in maintenance dialysis patients . However, Exclusion criteria for CKD-5 non-dialysis patients were: in this study, PA was assessed by self-reported scales and not an acute start of dialysis treatment, active symptomatic coro- by objective measurements. In a previous study of our group, nary artery disease or cardiac failure New York Heart Asso- we observed a significant increase in walking speed 6 months ciation (NYHA) class III or IV, active malignancies, active after the start of dialysis as compared with the CKD-5 non- infections, and inability to provide informed consent. For dialysis phase . Correlations between changes in HRQOL walking test measurements, physical disability was meas- and changes in PA following the start of dialysis have not been ured. (Patients had to be able to walk without help.) Exclu- studied yet. sion criteria for prevalent dialysis patients were similar. Aims of this study were firstly to compare HRQOL between Healthy controls were non-diabetic, non-smokers, and healthy controls, CKD-5 non-dialysis, and prevalent dialysis not hypertensive (systolic blood pressure < 170 mmHg patients, and secondly, to assess changes from the CKD-5 non- and/or diastolic blood pressure < 100 mmHg) and were dialysis phase until 1 year after the start of dialysis and thirdly, recruited via advertisements at the university hospital. to assess correlations between HRQOL and PA parameters, Patients, as well as healthy controls, were asked to be both in a cross-sectional and longitudinal design. in a fasting state during the measurements, except for the PA measurements. Written informed consent was obtained from each patient prior to participation. The study was approved by the Ethical Committee (NL33129.068.10, NL35039.068.10) and the Hospital Board of the Maas- tricht University Medical Center+. 1 3 Longitudinal branch International Urology and Nephrology (2018) 50:1131–1142 1133 Groups - kTx (n=3) CKD-5 DIALYSIS HEALTHY Exit aer - Death (n=2) NON-DIALYSIS PATIENTS CONTROLS baseline: - Complicaons (n=1) - No PA data (n=2) PATIENTS: N = 44 N = 29 N = 20 Cross-seconal branch QOL, PA, QOL, PA, QOL, PA Baseline WS, LAB WS LAB WS, LAB Start dialysis Month 5-6 QOL (n=42) post start PA (n=39) WS, LAB Month 11-13 QOL (n=38) post start WS, LAB Fig. 1 Study design. kTx kidney transplantation, PA physical activity, CKD chronic kidney disease, N number of patients, QOL quality of life, WS walking speed, LAB laboratory parameters et al. [20, 21]. All scales were normalized via t-score trans- Quality of life measurements formation (mean, 50 ± 10 [SD]) to make it comparable to the general population and other patient groups with specific Short Form-36 (SF-36) version 1 questionnaires were disease states [21, 22]. filled out to measure physical component summary (PCS) scores for the physical domains of health, and mental com- Physical activity measurements ponent summary (MCS) for the mental domains of health. The SF-36 questionnaire is the most used tool to measure Number of steps HRQOL in the field of nephrology worldwide  and is a reliable and valid instrument for use in both general pop- All participants wore a SenseWear™ pro 3 armband ulation surveys and in studies of chronic disease popula- (BodyMedia , Pittsburg, PA) to measure PA parameter tions in the Netherlands . The SF-36 is a multi-purpose, number of steps for 2 days (CKD-5 non-dialysis patients: short-form health survey which includes 36 items. These 2.01 ± 0.41 days, mean on-body time: 94.9%; prevalent 36 items provide a measure of physical and mental health dialysis patients: 2.30 ± 0.73 days, mean on-body time: items ranging from 0 (“worst possible health”) to 100 (“best 96.7%; controls: 2.07 ± 0.54 days, mean on-body time: possible health”). The 36 items can be subdivided into eight 96.1%), which is considered to be sufficient to obtain data subscales known as physical functioning (PF), role-physical with regard to daily PA [23, 24]. The mean of the total on- (RP), bodily pain (BP), general health (GH), vitality (VT), body time was calculated (expressed as number of steps per social functioning (SF), role-emotional (RE), and mental 24 h) to include both the dialysis and non-dialysis day. No health (MH). These eight subscales were summarized in two differentiation was made between data collected on week or summary scores known as: PCS score and a MCS score. weekend days for all participants. Questionnaires were scored by scoring algorithms of Ware 1 3 1134 International Urology and Nephrology (2018) 50:1131–1142 Four‑meter walking test Comorbidity score A 4-m walking test was conducted to determine walk- Comorbidity index was determined for each patient based on ing speed (m/sec) by covering a distance of 4 m. Sev- the comorbidity checklist by the Davies comorbidity index eral studies confirmed the validity and sensitivity of this scoring system . Patients were divided into three risk widely used test for determining walking speed [25–28] groups: low, medium, and high risk of mortality. The Davies and physical performance in ESRD patients . comorbidity index is commonly used for ESRD patients [31–33]. Biochemical parameters Longitudinal analysis Albumin, hemoglobin (HB), dialysis adequacy (Kt/V), Changes in parameters of HRQOL were measured in 38 and β2-microglobulin were measured or determined dur- CKD-5 non-dialysis patients before the start of dialysis ing routine patient laboratory measurements (Table 1). (within 1 month before start), 5–6 months after starting The residual glomerular filtration rate (GFR ) was dialysis, and 11–13 months after the start of dialysis by the residual estimated with the use of β2-microglobulin as described same methods as for the cross-sectional part. Changes in by Vilar et al. . parameters of PA were measured in 39 CKD-5 non-dialysis patients before the start of dialysis and 5–6 months after the Table 1 Patient characteristics cross-sectional analysis CKD-5 non-dialysis patients Dialysis patients Healthy controls Number of patients 44 29 20 Male (%) 75.0 69.0 65.0 HD/PD – 21*/8 – Age (years) 61.3 ± 12.0 58.17 ± 14.65 59.65 ± 14.10 Height (cm) 173.8 ± 9.3 171.59 ± 9.57 174.75 ± 11.36 Weight (kg) 79.2 ± 17.3 82.75 ± 15.31 76.68 ± 15.80 BMI (kg/m ) 26.0 ± 4.1 28.06 ± 4.45 24.86 ± 3.41 Albumin (g/L) 35.34 ± 5.18 (n = 36) 40.24 ± 3.55 (n = 25) 40.24 ± 2.30 Hemoglobin (mmol/L/g/dL) 6.7 ± 0.9/10.8 ± 1.4 (n = 39) 6.9 ± 0.7/11.1 ± 1.2 (n = 28) – Kt/V (HD/PD) – 1.54 ± 0.39/2.17 ± 0.74 – eGFR (ml/min/1.73 m ) 13.8 ± 5.5 (n = 36) – 72.59 ± 11.07 Origin of end-stage renal disease Diabetic nephropathy (%) 4.5 20.7 – Polycystic kidney disease (%) 27.3 17.2 Nephrosclerosis (%) 15.9 6.9 Hypertensive nephropathy (%) 9.1 10.3 Nephrotic syndrome (%) 11.4 3.4 Unknown (%) 11.4 13.8 Other (%) 20.5 27.6 Diabetes mellitus (%) 15.9 41.4 – Cardiovascular disease (%) 34.1 37.9 – Risk of mortality by Davies index Low risk (%) 47.4 41.4 – Medium risk (%) 43.2 37.9 High risk (%) 9.1 20.7 History of prior KTx (%) 22.7 31.0 0.0 SBP (mmHg) 146.0 ± 20.9 152.4 ± 26.5 138.0 ± 13.4 DBP (mmHg) 83.1 ± 12.9 80.8 ± 12.4 82.3 ± 6.9 Data are given in mean ± SD. HD hemodialysis, PD peritoneal dialysis, BMI body mass index, eGFR estimated glomerular filtration rate, KTx kidney transplantation, SBP systolic blood pressure, DBP diastolic blood pressure * All HD patients have arteriovenous (AV) fistulas 1 3 International Urology and Nephrology (2018) 50:1131–1142 1135 start of dialysis due to the study design. It was measured by − 10.8] for CKD-5 non-dialysis patients (p < 0.001), and the same methods as for the cross-sectional part. (Data are − 15.5 [− 20.7, − 10.3] for the prevalent dialysis patients previously described elsewhere ). (p < 0.001). PCS scores did not statistically significantly differ between CKD-5 non-dialysis and prevalent dialysis Statistical analysis patients (p = 0.955) (Fig. 2). Subsequent nonparametric analyses showed that the Data are expressed as mean ± SD or median [25th–75th per- scores on the subscales which correlate with PCS scores (i.e. centile], unless indicated otherwise. PF, RP, GH, and VT) were statistically significantly lower For the cross-sectional analyses, differences in the cat- in CKD-5 non-dialysis patients, and prevalent in dialysis egorical variables were assessed using Chi-square tests. patients as compared with healthy controls, except for BP, Differences in the SF-36 summary scores (PCS and MCS) which was only statistically significantly lower in CKD-5 between groups were assessed by linear regression analy- non-dialysis patients (Fig. 3). ses. Differences in the individual subscales of the SF-36 In linear regression analyses, the difference in MCS were examined with Mann–Whitney U tests as scores of scores as compared with controls was (beta [95% CI]) − 7.3 these subscales were not normally distributed. In additional [− 12.0, − 2.6] for CKD-5 non-dialysis patients (p = 0.003), analyses, we adjusted the between-group differences in and − 6.0 [− 11.1, − 0.9] for the prevalent dialysis patients PCS and MCS scores for differences in the distribution of (p = 0.022). MCS scores did not statistically significantly age, gender, and diabetes status with the use of multivari- differ between CKD-5 non-dialysis and prevalent dialysis able regression analyses. As PA was lower in both CKD-5 patients (p = 0.540) (Fig. 4). non-dialysis patients and prevalent dialysis patients, as was Subsequent nonparametric analyses showed that the already described in our previous study , we subse- scores on the subscales which correlate with MCS scores quently examined correlations between these PA parameters (i.e., RE, SF, VT and GH) were statistically significantly and HRQOL scores, as well as correlations between bio- lower in CKD-5 non-dialysis patients, and prevalent dialysis chemical parameters and HRQOL scores with Spearman’s patients as compared with healthy controls, except for MH rank correlation coefficients. which was only statistically significantly lower in CKD-5 Changes in the CKD-5 non-dialysis patient group dur- non-dialysis patients (Fig. 3). ing the first 12 months after dialysis initiation were exam- After adjustment for age, gender, and diabetes prevalence, ined using Friedman tests, as most variables were not nor- outcomes were not materially changed (data not shown). mally distributed. Each analysis was based on all available data. Differences in change between dialysis modalities Physical activity outcomes were examined with Friedman tests. Correlations between changes in PA parameters and changes in HRQOL scores As already described in a previous study of our group were assessed with Spearman’s rank correlation coefficients. , the median number of steps was statistically signifi- All statistical analyses were performed with IBM SPSS cantly lower in both CKD-5 non-dialysis patients (5435.5 Statistics for Windows, version 24 (IBM Corp. Armonk, [3212.8–7384.3]) and prevalent dialysis patients (3994.5 NY, USA). p values ≤ 0.05 were considered to be statisti- [1993.5–6712.8]) as compared with healthy controls cally significant. (11,062.0 [7687.0–13,839.0]) (p < 0.001). Results Patient characteristics Patient characteristics for the cross-sectional part are sum- marized in Table 1. Patient characteristics for the patients participating in the longitudinal part were similar to baseline characteristics for both the longitudinal measurements of HRQOL as well as PA (Supplementary Tables 1a and 1b). Quality of Life outcomes In linear regression analyses, the die ff rence in PCS scores as compared with controls was (beta [95% CI]) − 15.6 [− 20.4, Fig. 2 Physical component summary (PCS) scores. CKD-5 stage 5 chronic kidney disease 1 3 1136 International Urology and Nephrology (2018) 50:1131–1142 Fig. 3 Subscale domains SF-36. PF physical functioning, RP role-physical, BP bodily pain, GH general health, VT vitality, SF social functioning, RE role- emotional, MH mental health In PD patients, median PCS score before the start of dialyses was 37.8 [32.5–44.6], after 5–6 months of dialysis, median PCS score was 36.8 [32.2–45.8], and 1 year after the start of dialysis median PCS score was 33.5 [29.1–42.6] (p = 0.504). In HD patients, median PCS score before the start of dialyses was 40.5 [31.7–48.6], after 5–6 months of dialysis median PCS score was 42.8 [33.1–51.6], and 1 year after the start of dialysis, median PCS score was 45.5 [24.0–50.0] (p = 0.532). In PD patients, median MCS score before the start of dialyses was 48.1 [42.3–58.4], after 5–6 months of dialysis median MCS score was 51.6 [37.5–56.3], and 1 year after the start of dialysis, median MCS score was 52.2 [42.3–58.2] (p = 0.810). In HD patients, median MCS score before the Fig. 4 Mental component summary (MCS) scores. CKD-5 stage 5 start of dialyses was 51.0 [45.6–54.7], after 5–6 months chronic kidney disease of dialysis median MCS score was 54.6 [40.6–57.7], and 1 year after the start of dialysis, median MCS score was 47.3 [42.0–57.0] (p = 1.000). Also walking speed was statistically significantly lower in CKD-5 non-dialysis patients (median 1.4 [1.2–1.8] m/s) Longitudinal outcomes of physical activity as compared with healthy controls (1.8 [1.7–2.0] m/s) (p = 0.017), but not as compared with prevalent dialysis As already described in our previous study, no statisti- patients (1.5 [1.2–1.9] m/s) (p = 0.699). After adjustment cally significant changes over time in the first 6 months for age, gender, and diabetes, prevalence outcomes were not after starting dialysis were found for median number of materially changed (data not shown) . steps, which changed from 5747.0 [3137.0–7808.0] to 5486.0 [3892.0–8452.0]; p = 0.052 (n = 39) . As also described in the previous study, walking speed statistically Longitudinal outcomes HRQOL significantly increased in the first 6 months after the start of dialysis from median 1.4 [1.2–1.8] to 1.7 [1.5–2.0] m/s In the first year after the start of dialysis, PCS scores did not (p = 0.050) (n = 34) . However, an additional analysis in change statistically significantly (p = 0.275). Also, additional 29 patients for whom 1-year follow-up data were available analyses of the subscales for PCS score, PF, RP, BP, GH, and in the present study with regard to walking speed showed no VT did not show statistically significant changes over time statistically significant differences in walking speed 1 year (Table 2, Fig. 5). after the start of dialysis (p = 0.161): walking speed was, MCS scores did not change statistically significantly in respectively, (1.4 [1.3–1.9] m/s) before the start of dialysis, the first year after the start of dialysis (p = 0.900). The same (1.7 [1.6–1.9] m/s) 5–6 months after the start of dialysis, and held true for the additional analyses of the subscales for (1.5 [1.3–1.9] m/s) 1 year after the start of dialysis. MCS score; MH, RE, SF, VT, and GH did not show statisti- cally significant changes over time (Table 2, Fig. 5). When separated by dialysis modality (19 patients started with HD and 19 patients started with PD), no statistically significant changes over time were found for the different dialysis modalities. 1 3 International Urology and Nephrology (2018) 50:1131–1142 1137 Table 2 Longitudinal analyses Scale Visit n = 38 P value HRQOL scales Physical functioning Before start dialysis 43.9 [33.2–48.2] 0.089 6 months after start dialysis 45.0 [34.8–52.5] 12 months after start dialysis 41.8 [24.6–48.7] Role-physical Before start 33.6 [26.2–48.3] 0.804 6 months after start dialysis 33.6 [26.2–55.6] 12 months after start dialysis 33.6 [26.2–44.8] Bodily pain Before start 48.6 [35.6–60.5] 0.771 6 months after start dialysis 49.5 [35.6–60.5] 12 months after start dialysis 49.5 [35.6–60.5] General health Before start 37.7 [32.6–42.5] 0.271 6 months after start dialysis 40.2 [30.1–48.0] 12 months after start dialysis 39.7 [30.0–45.1] Vitality Before start 47.2 [39.4–52.0] 0.394 6 months after start dialysis 49.6 [42.4–54.4] 12 months after start dialysis 47.2 [41.8–52.0] Social functioning Before start 40.8 [35.3–57.4] 0.644 6 months after start dialysis 46.3 [40.8–51.9] 12 months after start dialysis 40.8 [35.3–53.2] Role-emotional Before start 50.6 [35.5–55.7] 0.573 6 months after start dialysis 50.6 [26.9–55.7] 12 months after start dialysis 45.6 [25.4–55.7] Mental health Before start 52.9 [44.1–57.3] 0.694 6 months after start dialysis 52.9 [44.1–59.6] 12 months after start dialysis 50.7 [44.1–57.9] Physical component summary score Before start 38.9[31.7–45.8] 0.275 6 months after start dialysis 39.3 [33.0–50.7] 12 months after start dialysis 36.8 [27.3–47.3] Mental component summary score Before start 50.1 [42.5–56.6] 0.900 6 months after start dialysis 51.7 [40.4–57.6] 12 months after start dialysis 49.0 [42.2–57.3] Data are given in median [25th and 75th percentile] Fig. 5 Changes in HRQOL (subscales). Boxes represent median, 25th eral health, VT vitality, SF social functioning, RE role-emotional, MH percentile and 75th percentile. HRQOL health-related quality of life, mental health, PCS physical component summary, MCS mental com- PF physical functioning, RP role-physical, BP bodily pain, GH gen- ponent summary, ESRD end-stage renal disease 1 3 1138 International Urology and Nephrology (2018) 50:1131–1142 Fig. 6 Correlation between physical component summary (PCS) score and number of steps in both CKD-5 non- dialysis (r = 0.580; p < 0.001) and dialysis patients (r = 0.476; p = 0.009) No correlations were found for delta number of steps and Correlations between PA parameters and HRQOL both delta PCS and delta MCS scores in the first 6 months scores after the start of dialysis. The same held true for delta walk- ing speed and both delta PCS and delta MCS scores after Correlations were found for number of steps and PCS both the first 6 months as well as the first year after the start scores in both CKD-5 non-dialysis (r = 0.580; p < 0.001) of dialysis. as well as in prevalent dialysis patients (r = 0.476; p = 0.009) (Fig. 6). No correlations were found with MCS Correlations between biochemical parameters scores: CKD-5 non-dialysis patients (r = 0.041; p = 0.797) and HRQOL scores and prevalent dialysis patients (r = 0.1158; p = 0.542). The subscales PF, RP, VT, and SF, which correlate with No correlations were found for HB and PCS scores in both the PCS score, were correlated with number of steps in the CKD-5 non-dialysis group (r = − 0.061; p = 0.710), as CKD-5 non-dialysis patients: PF (r = 0.570; p < 0.001), well as in the dialysis group (r = − 0.098; p = 0.621). Also RP (r = 0.486; p = 0.001), VT (r = 0.381; p = 0.013), and s s no correlations were found for HB and MCS scores in both SF (r = 0.319; p = 0.040). In prevalent dialysis patients the CKD-5 non-dialysis group (r = 0.089; p = 0.590), as well only in subscale PF a correlation was found (r = 0.548; as in the dialysis group (r = 0.128; p = 0.518). p = 0.002). The same held true for albumin and PCS scores: CKD-5 Furthermore, correlations were found for walking speed non-dialysis group (r = 0.293; p = 0.083) and dialysis group and PCS scores in CKD-5 non-dialysis patients (r = 0.415; (r = 0.190; p = 0.362). Here also, no correlations were found p = 0.010) but not in prevalent dialysis patients (r = 0.097; for albumin and MCS scores in both the CKD-5 non-dialysis p = 0.630) (Fig. 7). No correlations were found with MCS group (r = 0.226; p = 0.186), as well as in the dialysis group scores: CKD-5 non-dialysis patients (r = -0.007; p = 0.967) (r = − 0.154; p = 0.463). and prevalent dialysis patients (r = 0.063; p = 0.756). The subscales PF, VT, and SF, which correlate with the PCS score, were correlated with walking speed in CKD-5 non- Discussion dialysis patients: PF (r = 0.537; p < 0.001), RP (r = 0.331; s s p = 0.042), VT (r = 0.351; p = 0.031), and SF (r = 0.441; s s In brief, this study firstly showed a reduction in the physi - p = 0.006). In prevalent dialysis patients, no correlations for cal domains of the SF-36 HRQOL scores in both CKD-5 subscales were found. non-dialysis and prevalent dialysis patients, compared with 1 3 International Urology and Nephrology (2018) 50:1131–1142 1139 Fig. 7 Correlation between physical component summary (PCS) score and walking speed in both CKD-5 non-dialysis (r = 0.415; p = 0.010) and dialysis patients (r = 0.097; p = 0.630) age-matched healthy controls, without significant differences findings are in line with previous studies also, whereby in between both patient groups. The summary score of the incident patients , and in both CKD (stage 2–5) patients mental domains of the SF-36 scale (MCS) was significantly  as well as in HD patients [2, 36], lower HRQOL scores lower in both CKD-5 non-dialysis patients and prevalent were observed. However, to the best of our knowledge, no dialysis patients as compared with controls, without signifi- comparative study has been performed between CKD-5 non- cant differences between CKD-5 non-dialysis and prevalent dialysis and prevalent dialysis patients. Interestingly, these dialysis patients. Secondly, in the longitudinal analysis, findings are not only common in ESRD patients, but also no significant changes in HRQOL were observed in one in other populations with chronic diseases, such as chronic of the most critical periods for ESRD patients: the phase obstructive pulmonary disease (COPD) [37, 38] and heart from CKD-5 non-dialysis phase until 1 year after the start failure , low HRQOL scores are observed. of dialysis [11–13], neither in the physical, nor in the mental In the longitudinal analysis, no significant changes in PCS components. Lastly, PA parameters were significantly asso- or MCS scores were observed in the first year after starting ciated with PCS scores and components of the PCS scale of dialysis, in which patients were followed from the CKD-5 the SF-36, which was most pronounced for the PF compo- non-dialysis phase. This is remarkable given the fact that nent. No correlations were found for changes in PA (delta the start of dialysis is a critical phase in ESRD patients [11, number of steps/delta walking speed) and changes in both 12] with both phenotypic and pathophysiologic changes PCS and MCS scores (delta PCS/delta MCS). , an increased risk of mortality [40, 41], and a signifi- The cross-sectional analysis showed PCS score values cantly decreased functional status, which was observed in below norm-based scores in CKD-5 non-dialysis patients elderly nursing home patients after dialysis initiation . as well as in prevalent dialysis patients and significantly We should acknowledge that our patient group may consist lower scores as compared with healthy controls, suggest- of a selected group, as all patients were recruited from the ing reduced HRQOL in this patient group. In particular, CKD-5 non-dialysis out-patient clinic. Nevertheless, the subscales physical functioning, role-physical, and general severely impaired HRQOL in the CKD-5 non-dialysis phase health, which are highly associated with the physical com- underscores the need for strategies to improve HRQOL ponent , were low as compared with norm-based values. already in earlier stages of CKD . Previous research in a large US dialysis cohort showed a One of these potentially modifiable factors includes PA. comparable reduction in PCS and MCS scores of the SF-36 We observed a significant relation between objectively in both incident and prevalent dialysis patients . Our measured PA parameters and physical domains of the PCS. 1 3 1140 International Urology and Nephrology (2018) 50:1131–1142 A study of Painter et al. showed increased self-reported of the 44 patients who started dialysis, 2 patients died after physical scale and PCS scores in patients receiving exer- the first 6 months of dialysis. cise training . A more recent study of Lopes et al. also In conclusion, the physical domain of HRQOL (as meas- showed that higher self-reported aerobic physical activity ured by PCS of the SF-36) is already severely decreased level was associated with better physical HRQOL (higher in the CKD-5 non-dialysis phase. Additionally, in the first PCS scores) in HD patients . The results of our study year after the start of dialysis treatment, HRQOL scores did thus confirm findings from earlier research although in our not change significantly, suggesting no major effect of the study objective measures were used to assess PA by the start of dialysis in our ESRD patients, in whom the reduced SenseWear Pro3™. Next to that, the correlation between HRQOL appears to be primarily related to the renal disease changes in HRQOL and changes in PA was longitudinally per se. Next to HRQOL also PA is low in both CKD-5 non- assessed over a 1-year time period. Despite the correlation dialysis as well as prevalent dialysis patients. Given the cor- between PA and PCS scores in the cross-sectional analysis, relation between the physical domain of health (PCS) and contrastingly, no correlations were found for changes in PA PA, the importance of physical activity programs should be parameters and changes in PCS scores over a period of time underscored and strongly encouraged in both CKD-5 non- of 12 months. This might be due to the fact that changes in dialysis as well as in dialysis patients in order to increase PA following the start of dialysis were not significant in the the physical domains of HRQOL in these patients groups. overall group and relatively small in individual patients, or Authors’ contributions JPK and NJHB developed the research idea and due to the possibility that SF-36 scores might not be sensi- study design; NJHB and NMPD acquired the data; NJHB, RJHM, TC, tive enough to detect dialysis-specific changes in HRQOL. FMvdS, MMHH, JJJMW, CJAMK, TD, BC, JPK, and FS interpreted Several limitations of the study deserve consideration; the data; statistical analysis was performed by NJHB and RJHM; and first, due to the small study sample, in particular, the longi- JPK, KMLL, and FMvdS supervised or mentored the study. tudinal analyses are underpowered, as reflected by the wide Funding Jeroen P. Kooman, Frank M. van der Sande, Remy J. H. Mar- interquartile ranges in these analyses. Nevertheless, our tens, and Natascha J. H. Broers are supported by an unrestricted grant cross-sectional analyses showed a large clinically relevant from Fresenius Medical Care Europe. Bernard Canaud is an employee difference between both patient groups and the healthy con- of Fresenius Medical Care Europe. trol group, even when in the analyses the lower bound of the 95% confidence interval was considered. Furthermore, our Open Access This article is distributed under the terms of the Crea- tive Commons Attribution 4.0 International License (http://creat iveco findings are comparable with earlier studies [2 , 4, 5], which mmons.or g/licenses/b y/4.0/), which permits unrestricted use, distribu- supports the outcomes in this present study. Second, both tion, and reproduction in any medium, provided you give appropriate PD and HD patients were included, with apparently little credit to the original author(s) and the source, provide a link to the differences between both groups. It is, however, important Creative Commons license, and indicate if changes were made. to realize that this study focused on the effects of starting dialysis treatment per se and not on differences in dialysis modalities, for which a larger sample size would be required. However, a previous study did not conclude notable differ - References ences in HRQOL between HD and PD treatment also . 1. Mazairac AH, de Wit GA, Penne EL, van der Weerd NC, de Jong Third, no matching procedure in group formation of the B, Grooteman MP, van den Dorpel MA, Buskens E, Dekker FW, CKD-5 non-dialysis group and the prevalent dialysis group Nube MJ, Ter Wee PM, Boeschoten EW, Bots ML, Blankestijn PJ, was conducted, which increased the possibility of bias. How- Investigators C (2011) Changes in quality of life over time–Dutch haemodialysis patients and general population compared. Neph- ever, in additional analyses, we adjusted the between-group rol Dial Transplant 26(6):1984–1989. https://doi.or g/10.1093/ndt/ differences for differences in the distribution of age, gender, gfq68 0 and diabetes status. In addition, outcomes were not materi- 2. Walters BA, Hays RD, Spritzer KL, Fridman M, Carter WB ally changed. Fourth, only the SF-36 version 1 question- (2002) Health-related quality of life, depressive symptoms, ane- mia, and malnutrition at hemodialysis initiation. Am J Kidney Dis naire was used to measure HRQOL instead of the Kidney 40(6):1185–1194. https ://doi.org/10.1053/ajkd.2002.36879 Disease Quality of Life-36 (KDQOL-36) which is especially 3. DeOreo PB (1997) Hemodialysis patient-assessed functional designed for patients with ESRD. Nevertheless, the SF-36 health status predicts continued survival, hospitalization, and is the most widely used tool to measure HRQOL in the field dialysis-attendance compliance. Am J Kidney Dis 30(2):204–212 4. Broers NJ, Usvyat LA, Kooman JP, van der Sande FM, Lacson of nephrology worldwide , and scores were normalized E Jr, Kotanko P, Maddux FW (2015) Quality of life in dialysis which made it comparable to the general population (healthy patients: a Retrospective Cohort Study. Nephron 130(2):105–112. controls) . Last, due to the fact that we have included https ://doi.org/10.1159/00043 0814 relatively young and possibly less frail controls and patients, 5. Mapes DL, Lopes AA, Satayathum S, McCullough KP, Good- kin DA, Locatelli F, Fukuhara S, Young EW, Kurokawa K, there might be a possibility of selection bias. In addition, out Saito A, Bommer J, Wolfe RA, Held PJ, Port FK (2003) 1 3 International Urology and Nephrology (2018) 50:1131–1142 1141 Health-related quality of life as a predictor of mortality and hos- of the SF-36 Health Survey in community and chronic disease pitalization: the Dialysis Outcomes and Practice Patterns Study populations. J Clin Epidemiol 51(11):1055–1068 (DOPPS). Kidney Int 64(1):339–349. https ://doi.or g/10.104 20. Ware JE, Snow KK, Kosinski M, Gandek B (1993) Health survey 6/j.1523-1755.2003.00072 .x manual and interpretation guide. The Health Insititute, New Eng- 6. Mapes DL, Bragg-Gresham JL, Bommer J, Fukuhara S, McKevitt land Medical Center, Boston P, Wikstrom B, Lopes AA (2004) Health-related quality of life in 21. Ware JE, Kosinski M, Keller SD (1994) SF-36 physical and men- the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am tal health summary scales: a user’s manual, 2nd edn. The Health J Kidney Dis 44(5 Suppl 2):54–60 Institute, New England Medical Center, Boston 7. Gabbay E, Meyer KB, Griffith JL, Richardson MM, Miskulin DC 22. Ware JE (2001) SF-36 health survey update. SPINE (2010) Temporal trends in health-related quality of life among 25(24):3130–3139 hemodialysis patients in the United States. Clin J Am Soc Nephrol 23. Dontje ML, van der Wal MH, Stolk RP, Brugemann J, Jaarsma 5(2):261–267. https ://doi.org/10.2215/CJN.03890 609 T, Wijtvliet PE, van der Schans CP, de Greef MH (2014) Daily 8. Merkus MP, Jager KJ, Dekker FW, De Haan RJ, Boeschoten EW, physical activity in stable heart failure patients. J Cardiovasc Nurs Krediet RT (1999) Quality of life over time in dialysis: the Nether- 29(3):218–226. https ://doi.org/10.1097/JCN.0b013 e3182 83ba1 4 lands Cooperative Study on the adequacy of dialysis. NECOSAD 24. Almeida GJ, Wasko MC, Jeong K, Moore CG, Piva SR (2011) Study Group. Kidney Int 56(2):720–728. https ://doi.org/10.104 Physical activity measured by the SenseWear Armband in women 6/j.1523-1755.1999.00563 .x with rheumatoid arthritis. Phys Ther 91(9):1367–1376. https://doi. 9. Wu AW, Fink NE, Marsh-Manzi JV, Meyer KB, Finkelstein FO, org/10.2522/ptj.20100 291 Chapman MM, Powe NR (2004) Changes in quality of life dur- 25. Ostir GV, Volpato S, Fried LP, Chaves P, Guralnik JM, Wom- ing hemodialysis and peritoneal dialysis treatment: generic and en’s H, Aging S (2002) Reliability and sensitivity to change disease specific measures. J Am Soc Nephrol 15(3):743–753 assessed for a summary measure of lower body function: results 10. Korevaar JC, Jansen MA, Merkus MP, Dekker FW, Boeschoten from the Women’s Health and Aging Study. J Clin Epidemiol EW, Krediet RT (2000) Quality of life in predialysis end-stage 55(9):916–921 renal disease patients at the initiation of dialysis therapy. The 26. Penninx BW, Ferrucci L, Leveille SG, Rantanen T, Pahor M, NECOSAD Study Group. Perit Dial Int 20(1):69–75 Guralnik JM (2000) Lower extremity performance in nondisa- 11. McIntyre CW, Rosansky SJ (2012) Starting dialysis is dangerous: bled older persons as a predictor of subsequent hospitalization. J How do we balance the risk? Kidney Int 82(4):382–387. https :// Gerontol A Biol Sci Med Sci 55(11):M691–M697 doi.org/10.1038/ki.2012.133 27. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace 12. Kooman JP, Usvyat L, van der Sande FM, Thijssen S, Levin N, RB (1995) Lower-extremity function in persons over the age of Leunissen KM, Kotanko P (2012) ‘Time and time again’: oscil- 70 years as a predictor of subsequent disability. N Engl J Med latory and longitudinal time patterns in dialysis patients. Kidney 332(9):556–561. https ://doi.org/10.1056/NEJM1 99503 02332 Blood Press Res 35(6):534–548. https ://doi.org/10.1159/00034 0902 0022 28. Guralnik JM, Ferrucci L, Pieper CF, Leveille SG, Markides KS, 13. Broers NJ, Cuijpers AC, van der Sande FM, Leunissen KM, Ostir GV, Studenski S, Berkman LF, Wallace RB (2000) Lower Kooman JP (2015) The first year on haemodialysis: a critical extremity function and subsequent disability: consistency across transition. Clin Kidney J 8(3):271–277. https ://doi.org/10.1093/ studies, predictive models, and value of gait speed alone compared ckj/sfv02 1 with the short physical performance battery. J Gerontol A Biol Sci 14. Loos C, Briancon S, Frimat L, Hanesse B, Kessler M (2003) Med Sci 55(4):M221–M231 Effect of end-stage renal disease on the quality of life of older 29. Hartmann EL, Kitzman D, Rocco M, Leng X, Klepin H, Gordon patients. J Am Geriatr Soc 51(2):229–233 M, Rejeski J, Berry M, Kritchevsky S (2009) Physical function in 15. Gorodetskaya I, Zenios S, McCulloch CE, Bostrom A, Hsu older candidates for renal transplantation: an impaired population. CY, Bindman AB, Go AS, Chertow GM (2005) Health-related Clin J Am Soc Nephrol 4(3):588–594. https ://doi.org/10.2215/ quality of life and estimates of utility in chronic kidney dis-CJN.03860 808 ease. Kidney Int 68(6):2801–2808. https ://doi.or g/10.111 30. Vilar E, Boltiador C, Wong J, Viljoen A, Machado A, Uthayaku- 1/j.1523-1755.2005.00752 .x mar A, Farrington K (2015) Plasma levels of middle molecules to 16. Lopes AA, Lantz B, Morgenstern H, Wang M, Bieber BA, estimate residual kidney function in haemodialysis without urine Gillespie BW, Li Y, Painter P, Jacobson SH, Rayner HC, Mapes collection. PLoS ONE 10(12):e0143813. https://doi.or g/10.1371/ DL, Vanholder RC, Hasegawa T, Robinson BM, Pisoni RL (2014) journ al.pone.01438 13 Associations of self-reported physical activity types and levels 31. Davies SJ, Russell L, Bryan J, Phillips L, Russell GI (1995) with quality of life, depression symptoms, and mortality in hemo- Comorbidity, urea kinetics, and appetite in continuous ambulatory dialysis patients: the DOPPS. Clin J Am Soc Nephrol 9(10):1702– peritoneal dialysis patients: their interrelationship and prediction 1712. https ://doi.org/10.2215/CJN.12371 213 of survival. Am J Kidney Dis 26(2):353–361 17. Broers NJH, Martens RJH, Cornelis T, van der Sande FM, 32. Davies SJ, Phillips L, Naish PF, Russell GI (2002) Quantify- Diederen NMP, Hermans MMH, Wirtz J, Stifft F, Konings C, ing comorbidity in peritoneal dialysis patients and its relation- Dejagere T, Canaud B, Wabel P, Leunissen KML, Kooman JP ship to other predictors of survival. Nephrol Dial Transplant (2017) Physical activity in end-stage renal disease patients: the 17(6):1085–1092 effects of starting dialysis in the first 6 months after the transition 33. Van Manen JG, Korevaar JC, Dekker FW, Boeschoten EW, period. Nephron 137(1):47–56. https ://doi.org/10.1159/00047 Bossuyt PM, Krediet RT, Group NE-S (2003) Adjustment for 6072 comorbidity in studies on health status in ESRD patients: Which 18. Wight JP, Edwards L, Brazier J, Walters S, Payne JN, Brown CB comorbidity index to use? J Am Soc Nephrol 14(2):478–485 (1998) The SF36 as an outcome measure of services for end stage 34. Merkus MP, Jager KJ, Dekker FW, Boeschoten EW, Stevens P, renal failure. Qual Health Care 7(4):209–221 Krediet RT (1997) Quality of life in patients on chronic dialysis: 19. Aaronson NK, Muller M, Cohen PD, Essink-Bot ML, Fekkes M, self-assessment 3 months after the start of treatment. The Necosad Sanderman R, Sprangers MA, te Velde A, Verrips E (1998) Trans- Study Group. Am J Kidney Dis 29(4):584–592 lation, validation, and norming of the Dutch language version 35. Pagels AA, Soderkvist BK, Medin C, Hylander B, Heiwe S (2012) Health-related quality of life in different stages of chronic kidney 1 3 1142 International Urology and Nephrology (2018) 50:1131–1142 disease and at initiation of dialysis treatment. Health Qual Life 40. Noordzij M, Jager KJ (2014) Increased mortality early after dialy- Outcomes 10:71. https ://doi.org/10.1186/1477-7525-10-71 sis initiation: a universal phenomenon. Kidney Int 85(1):12–14. 36. Perlman RL, Finkelstein FO, Liu L, Roys E, Kiser M, Eisele G, https ://doi.org/10.1038/ki.2013.316 Burrows-Hudson S, Messana JM, Levin N, Rajagopalan S, Port 41. Robinson BM, Zhang J, Morgenstern H, Bradbury BD, Ng LJ, FK, Wolfe RA, Saran R (2005) Quality of life in chronic kidney McCullough KP, Gillespie BW, Hakim R, Rayner H, Fort J, Aki- disease (CKD): a cross-sectional analysis in the Renal Research zawa T, Tentori F, Pisoni RL (2014) Worldwide, mortality risk is Institute-CKD study. Am J Kidney Dis 45(4):658–666 high soon after initiation of hemodialysis. Kidney Int 85(1):158– 37. Habraken JM, van der Wal WM, Ter Riet G, Weersink EJ, Toben 165. https ://doi.org/10.1038/ki.2013.252 F, Bindels PJ (2011) Health-related quality of life and functional 42. Kurella Tamura M, Covinsky KE, Chertow GM, Yaffe K, Lan- status in end-stage COPD: a longitudinal study. Eur Respir J defeld CS, McCulloch CE (2009) Functional status of elderly 37(2):280–288. https ://doi.org/10.1183/09031 936.00149 309 adults before and after initiation of dialysis. N Engl J Med 38. Wilke S, Janssen DJ, Wouters EF, Schols JM, Franssen FM, Spruit 361(16):1539–1547. https ://doi.org/10.1056/NEJMo a0904 655 MA (2012) Correlations between disease-specific and generic 43. Fassbinder TR, Winkelmann ER, Schneider J, Wendland J, health status questionnaires in patients with advanced COPD: a Oliveira OB (2015) Functional capacity and quality of life in one-year observational study. Health Qual Life Outcomes 10:98. patients with chronic kidney disease in pre-dialytic treatment and https ://doi.org/10.1186/1477-7525-10-98 on hemodialysis—a cross sectional study. J Bras Nefrol 37(1):47– 39. Lesman-Leegte I, Jaarsma T, Coyne JC, Hillege HL, Van Veldhu- 54. https ://doi.org/10.5935/0101-2800.20150 008 isen DJ, Sanderman R (2009) Quality of life and depressive symp- 44. Painter P, Carlson L, Carey S, Paul SM, Myll J (2000) Physi- toms in the elderly: a comparison between patients with heart cal functioning and health-related quality-of-life changes with failure and age- and gender-matched community controls. J Card exercise training in hemodialysis patients. Am J Kidney Dis Fail 15(1):17–23. https ://doi.org/10.1016/j.cardf ail.2008.09.006 35(3):482–492 1 3
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