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Health-Related Quality of Life and Long-Term Survival and Graft Failure in Kidney Transplantation: A 12-Year Follow-Up Study

Griva, Konstadina1,2; Davenport, Andrew3; Newman, Stanton P.2,4,5

doi: 10.1097/TP.0b013e31827d9772
Clinical and Translational Research

Background Although the prognosis of kidney transplantation is generally good, long-term survival varies substantially between patients. This study examined whether health-related quality of life (HRQOL) predicts long-term mortality in kidney transplantation after adjustment for known risk factors.

Methods A cohort of 347 (46.77±13.96 years) kidney transplant recipients was followed up for 12 years after enrolment (1999–2001). Patients completed measures of HRQOL and medical records were reviewed to document clinical and cardiovascular risk factors and comorbidities at study entry (mean [SD], 8.57 [6.55] years after transplantation). The primary outcomes were ensuing all-cause mortality and all-cause graft failure (a composite endpoint consisting of return to dialysis therapy, preemptive retransplantation, or death with function). Cox proportional hazards multivariate models were developed to identify predictors of long-term patient and graft survival.

Results During the 12-year follow-up, 86 (24.8%) patients died, 64 (18.3%) died with a functioning graft, and 35 (11.1%) were placed back to dialysis. Physical QOL impairment increased the risk of mortality and graft failure during the follow-up period. The risk remained significant after adjusting for sociodemographic and clinical risk factors (adjusted hazard ratio, 1.89; 95% confidence interval, 1.09–2.95; P=0.022 and adjusted hazard ratio, 1.68; 95% confidence interval, 1.12–2.52; P=0.012 for patient and graft survival, respectively). Other significant risk factors were older age, time elapsed since transplantation, and Charlson comorbidity index. Risk of graft failure was also associated with glomerular filtration rate.

Conclusions Physical HRQOL predicts long-term mortality and graft failure independently of sociodemographic and clinical risk factors in renal transplant patients. Future research should identify the determinants of HRQOL and refine interventions to improve it.

1 Department of Psychology, National University of Singapore, Singapore, Singapore.

2 Centre for Health Services Research, City University London, London, UK.

3 Department of Nephrology, University College and Royal Free Hospital, London, UK.

4 Unit of Behavioural Medicine, University College London, London, UK.

5 Address correspondence to: Prof. Stanton P. Newman, Health Services Research Group, City University London, College Building Room A224, St. John Street, London, UK.

The data were gathered under grants from Onassis Heart Foundation (K.G.) and the R.L. Weston Institute for Neurological Studies of the University College London Medical School.

The authors declare no conflicts of interest.


All authors have participated sufficiently in the work to take public responsibility for the content of this work. K.G., the main author of the article, has been responsible for the design, analysis, and interpretation of data presented in this article. A.D., the on-site research coinvestigator, has provided continuing guidance during data collection and intellectual content of the critical importance to the work described and has also provided the approval of the article. S.P.N. has been involved in the conception of the study and interpretation of data and has provided the final review and approval of the article.

Received 24 February 2012. Revision requested 18 March 2012.

Accepted 9 November 2012.

Health-related quality of life (HRQOL) is an important marker of disease burden and also can be used to assess treatment effectiveness and predict risk for adverse outcomes (1). Kidney transplantation offers patients with end-stage renal disease the greatest potential for increased longevity and enhanced HRQOL (2–4). A large body of research has demonstrated positive pretransplantation to posttransplantation changes in multiple domains of specific, functional, and global HRQOL (5–7), yet kidney transplantation may not fully restore HRQOL to levels reported in the general population (8). In dialysis patient populations, low HRQOL scores measured by means of the Medical Outcomes Study Short Form-36 (SF-36) are associated with hospitalization and death (9–12). HRQOL has also been identified as a predictor of mortality in other populations, such as community dwelling and hospitalized elders (13–15), individuals with chronic obstructive pulmonary disease (16) and diabetes (17), and individuals with cardiovascular disease (18, 19). There is far less information about the associations between HRQOL and mortality in kidney transplantation (20). A recent study of a large sample of transplant recipients showed that physical HRQOL dimensions were independently associated with survival after case-mix adjustment (21). The study follow-up was limited to 7 years and comorbidities were ascertained with patient self-report without systematic recording of clinical (including cardiovascular) risk factors through medical notes or derivation of weighted comorbidity indices. Weighted indices have shown to be more reliable than total number of conditions (22, 23) and highly predictive of clinical outcomes in kidney transplantation (24, 25). Given these limitations, more detailed studies are required to explore the association between HRQOL and mortality in transplant patients controlling for alternative, and possibly competing, risk factors. Both medical records and self-report of medical conditions have potential issues about validity, namely, outdated entries or poor recall and inaccurate reporting, respectively. Extensive assessment through triangulation of methods, including careful evaluation of medical records, clinician-completed measures of severity, and comorbidity and patients’ self-report, is necessary to ensure accuracy of comorbid disease identification to avoid any confounding in the examination of the relationship of HRQOL and clinical outcomes in transplantation.

Finally, from the clinician’s point of view, longer-term prediction of outcomes has always been the paramount goal to guide clinical care, thereby underscoring the need for more longitudinal research.

The objectives of this study, therefore, were to determine predictors of long-term survival in a kidney transplant cohort that involved detailed medical assessments at baseline. We specifically tested whether HRQOL after renal transplantation was predictive of graft and patient survival outcomes at 12 years follow-up after extensive adjustment of sociodemographic and clinical risk factors (including validated weighted indices of comorbidity and patient self-report).

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The total study sample included 347 participants surveyed at 8.57 (6.55) years after transplantation, a 76.6% overall enrolment rate (mean follow-up, 119.89±45.11 months; see Fig. 1).



Baseline characteristics of the study population by vital status (all-cause mortality) at 12-year follow-up are shown in Table 1.



According to body mass index (BMI), 15.25 % (n=53) of the patients were overweight (BMI>25) and 2.59% (n=9) were obese (BMI>30). The majority of the patients had diagnosed hypertension; however, at assessment, systolic and diastolic blood pressure levels were generally within normal limits. The majority of patients were on antihypertensive medication (n=258 [74.35%]) and on medications known to improve cardiovascular survival: angiotensin-converting enzyme inhibitors were used by 22.47% (n=78), calcium channel blockers by 41.21% (n=143), β-blockers by 39.2% (n=136), statins by 9.22% (n=32), and aspirin by 23.34% (n=81). Rates of diabetes at enrolment were low and so were the numbers of patients that subsequently developed new-onset diabetes during the 12-year observation window (n=36 [10.3%]).

Immunosuppressive medications were cyclosporine (63.7%) or tacrolimus (32.3%), mycophenolate mofetil (7.8%) and azathioprine (34.3%), and steroids (100%), reflecting the state-of-the-art immunosuppression when these patients were transplanted. Immunosuppressive medications were routinely evaluated by frequent blood assays and adjusted accordingly.

Included patients were younger, with more years of education, and more likely to have developed end-stage renal disease due to glomerulonephritis than excluded patients/nonresponders. All other socioeconomic and clinical parameters were comparable between participants and nonparticipants (data not shown).

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Quality of Life

For physical component score (PCS), the age- and sex-adjusted score was 8.49±14.93 points lower than that of the general UK population (P<0.001), and for mental component score (MCS), the score was 6.04±7.32 points lower than norms (P=0.001). The number of individuals who would be considered to have at least moderately impaired HRQOL, defined as a PCS or MCS score lower than 1 SD below general population mean (corresponding to the lowest 15.87th percentile), was 136 (39.2%) for PCS and 80 (23.05%) for MCS. Physical HRQOL impairment was associated with age (P=0.001), education (P=0.021), work status (P=0.001), income (P=0.016), Charlson comorbidity index (CCI; P=0.003), recipient risk score (P=0.002), self-report comorbidities (P=0.004), time on dialysis before transplantation (P=0.001), and cardiac disease (P=0.023). Effect sizes (Cohen’s d) ranged from 0.31 to 0.56, indicating small to moderate effects.

No significant associations were found between MCS and any of the sociodemographic and clinical factors.

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Graft and Patient Survival

During the 12-year follow-up period, overall all-cause mortality rates (including death after return to dialysis) were 24.8% (n=86) and death with functioning graft was 18.4% (n=64). A total of 35 (11.1%) patients were treated with dialysis at the end of study. Other censoring events included transfer out of facility (n=26 [7.5%]), preemptive transplantation (n=2 [0.6%]), or end of study window (n=198 [57.1%]). The most common causes of death were cardiovascular events (n=26 [30.58%]), infection (n=21 [24.7%]), and malignancies (n=14 [16.47%]); for 10 participants, cause of death could not be accurately established. Overall graft failure rates (including death with function and return to dialysis/retransplantation) were 35.16% (n=122).

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Survival Analyses

Because our primary analytic goal was to estimate the associations of HRQOL with patient/graft survival after adjusting for risk factors that might explain the associations, we first identified demographic and clinical factors that were associated with these endpoints. Kaplan-Meier survival curves or univariate Cox regressions (as appropriate) indicated that age, employment status, income, cardiovascular disease, hypertension, weighted comorbidities indices (i.e., CCI and risk recipient score), time elapsed since transplantation, time on dialysis after transplantation, transplant source, estimated glomerular filtration rate (eGFR), and physical HRQOL (PCS) were significantly associated with survival endpoints (P<0.01; see Table 2). The unadjusted Kaplan-Meier survivor function by HRQOL classification (Fig. 2) demonstrates lower survival in patients with PCS lower than 1 SD relative to normative means (P<0.001, log-rank test).





All remaining demographic and clinical covariates, including biochemical values, medication/primary immunosuppressant and Karnofsky scores, and emotional HRQOL, were not significantly associated with patient or graft survival.

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Multivariate Analyses

All variables significantly associated with survival in unadjusted univariate analyses (P<0.01) were included in the multivariate Cox regression models. Beginning with models including PCS and the identified demographic and clinical risk factors, variables were excluded using backward elimination procedures.

In the initial model comprising all selected covariates (i.e., age, income, work status, time since transplantation, transplant source, CCI, risk recipient score, and cardiovascular disease), the adjusted hazard ratio (HR) association of physical HRQOL was 1.823 (95% confidence interval [95% CI], 1.038–2.859). In the backward stepwise regression analysis, work status, cardiovascular disease transplant source, and risk recipient score were excluded from the model (exit criterion P>0.05). Physical HRQOL remained in the final model that otherwise included age, time elapsed since transplantation, and CCI (Table 3). Physical QOL impairment increased risk of death by 65% (adjusted HR, 1.891).



Similar results were found when overall graft failure was made the clinical outcome in the analysis. Physical HRQOL was associated with a HR (95% CI) of 1.568 (1.036–2.374) in the complete model comprising all selected covariates: age, work status, income, time since transplantation, eGFR, transplant source, CCI, and risk recipient score (Table 4).



In the final multivariable model, the adjusted associations of physical HRQOL with graft failure were still significant with impairment in physical HRQOL increasing risk of graft failure by 62% (adjusted HR, 1.680). Age, GFR, and CCI also all met inclusion criteria for the final multivariable model and had P<0.05 for the adjusted association with overall graft failure.

Similar results also were obtained when patient and graft survival times were determined by the number of months between the receipt of transplant and the end-of-study observation period or event (death or graft failure) and when physical HRQOL scores were considered as continuous variables (data not shown).

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In this prospective study, we examined mortality in a cohort of 347 prevalent renal transplant patients and performed a risk-factor analysis to predict graft and patient survival outcomes. After a 12-year observation period, 24.8% (n=86) of patients had died, with 75% of these (n= 64) with functioning graft. Thirty-five (11.1%) patients were placed back on dialysis. The observed rates for overall mortality and death with function in this patient cohort were similar to those reported in other registries (26, 27). The main causes of death in this study were cardiovascular disease, malignancy, and infection; all these have been reported to be closely related to chronic immunosuppression and inflammation (28, 29).

Our results showed that physical HRQOL predicts long-term clinical outcomes in kidney transplant patients. Physical HRQOL conferred an increased risk of mortality and graft failure that was comparable and in most cases higher than that associated with clinical risk factors. Cardiovascular disease emerged as the leading cause of death in our renal transplant patients (30, 31), but traditional cardiovascular and other clinical risk factors measured in this study did not fully account for the 12-year mortality rates in our study cohort or negated the effect of HRQOL. The data showed that the predictive power of physical HRQOL for patient and graft survival was somewhat attenuated yet remained significant following adjustment for a range of sociodemographic and clinical variables, highlighting that HRQOL provides additional prognostic and relevant information above and beyond traditional objective indicators of health and the biomedical variables routinely obtained in clinical practice. Although our data cannot identify the optimal time to assess HRQOL, the observed prognostic effects highlight the need for regular monitoring of HRQOL in clinical settings.

In this study, only the physical HRQOL was significant predictor of mortality and graft failure outcomes, whereas the mental health summary scores (MCS) showed no relationship in the multivariate models. This finding is in accordance with other studies that reported either a null or a weak association between emotional HRQOL as measured with SF-36 and survival in dialysis (32) or other organ transplant populations (33). The lack of heterogeneity in scores and low prevalence of emotional HRQOL impairments in our and others’ samples may account for the lack of significant effects on clinical endpoints. Patients with psychiatric conditions, more likely to score poorly on mental health QOL, were excluded in our study, thereby restricting range of scores and generalizability of findings. Moreover, the assessment of mood by means of a standardized screening test for psychiatric morbidity was not used in this study and may be a more sensitive measure of poor psychologic well-being than emotional HRQOL (34). In light of these methodologic considerations, further work is necessary to establish the association between emotional well-being and clinical outcomes in the transplant population.

The mechanism of how physical HRQOL could influence mortality/graft failure is likely to be very complex and multifactorial. Physical HRQOL is a broad measurement tool that most likely represents a composite picture of many different illnesses, disease processes, and psychosocial factors. One possible explanation is that HRQOL can influence health behaviors and adherence/self-care, which in turn affect clinical outcomes. Another explanation is that physical HRQOL may also be a marker of disease severity. In line with previous work (35), significant associations were found between physical HRQOL and clinical markers, yet these were only moderate indicating that HRQOL reflects more than simple assessments of physical health or (sub)clinical disease processes. Most importantly, that the risk estimates of physical HRQOL on mortality and graft survival remained significant after controlling for the objective measures of risk and time elapsed since transplantation suggests that the association between HRQOL and mortality may reflect processes different from those underlying a simple relation between comorbid conditions and mortality. QOL may be a more sensitive and inclusive prodromal marker of poor health status. HRQOL ratings reflect a complex interplay of biological factors, health-relevant information (e.g., diagnoses, symptoms, and bodily sensations), as well as psychologic filters and cognitive processes that are subjective and contextual such as perception, interpretation, and memory (health expectations and values, satisfaction with levels of functioning after transplantation, comparisons with others and past health, etc.). HRQOL provides a measure that is genuinely sensitive to the patient’s perspective and subjective experience of health and illness that can significantly complement more specific and objective health measures in clinical practice. The predictive power of HRQOL confirms the importance of the centrality of the patient point of view in monitoring the quality of medical care outcomes.

There may also be a biological pathophysiologic mechanism linking HRQOL to clinical endpoints. Inflammatory processes may play a key role in this link. There is evidence to suggest that self-rated health is associated with serum inflammatory markers, particularly cytokines such as interleukin-1β and interleukin-6 (36–38). Chronic inflammation increases mortality risk in renal transplantation because it may lead to diseases with endothelial cell damage and endothelial dysfunction, predisposing to atherosclerotic processes and infection and ensuing mortality (39–42). More studies are necessary to elucidate the physiologic mechanisms and inflammatory processes linking poor HRQOL to mortality.

Several other baseline risk factors such as age, time since transplantation, time on dialysis before transplantation, eGFR, and increasing burden of comorbidity were, as expected, associated with reduced patient and graft survival time (28, 43–45).

There are several study limitations. First, in comparison with the only two other published cohort studies (20, 21), ours was smaller than one study (21), and although our study had a much longer follow-up, deaths were fewer. As expected, there were several different causes of death in the present sample, but the subgroups were too small for meaningful analyses. Thus, although the magnitude of the hazards ratio is large, issues related to study power limitations highlight the importance of revisiting the question with larger samples. Second, the study was conducted only in two transplantation units in the United Kingdom. However, it included patients with variation in sociodemographic and clinical characteristics, which are common in patients seen in clinical practice and represent well the National Kidney Transplant populations (46). Selection bias could not be excluded because the healthiest patients, both physically and mentally, might be more likely to participate in the study. Mean time since transplantation at the time of HRQOL assessment in our sample was more than 8 years, indicating that participants may also have been self-selected for better clinical outcomes (survivor bias). Although our response rate was high at 76.6%, there are clear demographic and clinical differences with other transplant cohorts that preclude generalization of these findings to the broader transplant population. As renal transplantation is not universally offered to all dialysis patients, renal transplant centers differ in their approach to rationing which patient is given access to transplantation. This study was started 13 years ago (with patients having received their graft in the 1990s or earlier), when access to transplantation may have been more restricted than today with the expansion in living organ donation and after cardiac death organ donation, and our centers may have taken a more conservative approach to offering transplantation to some patients than others.

Our data may underestimate the effect of HRQOL on clinical outcome, as our cohort was younger, leaner and with lower rates of diabetes and obesity compared to other cohorts and excluded patients with psychiatric diagnosis, or current hospitalization. Our inclusion criteria were applied in the main to rule out factors that could hinder completion of study assessment or confound measurement/effects (e.g., current hospitalization); It would be interesting to explore outcomes and associations in the broader transplant population and in other settings where the clinical profile of patients may be worse than that of our sample e.g., higher obesity and diabetes rates.

Third, study participants were not assessed at a uniform posttransplantation time. However, to control for the influence of years after transplantation, it was included in all analyses but did not attenuate the effect of HRQOL on clinical outcomes. Furthermore, HRQOL remained significant in sensitivity analyses when survival times were calculated based on date of transplant operation rather than date of HRQOL assessment, which allows us some confidence in inferring that the associations are robust and should be replicated in other samples with different distributions of HRQOL measurement in relation to transplantation.

Fourth, it was not deemed possible to conduct repeated sequential assessments of QOL or posttransplantation treatment-induced complications; as a consequence, this report does not include time-dependent analyses. Clinical markers and HRQOL were measured only once at baseline; thus, their course/trajectories over time cannot be established. Serial measures of clinical markers and QOL will enable a determination of how changing QOL relate to posttransplantation complications as well as the overall prognosis and clinical outcomes in renal transplantation. Finally, as with all nonexperimental designs even in longitudinal data such as these, issues of causality cannot be answered definitively and interpretations should be made with caution. Thus, the effects of HRQOL on mortality cannot imply a major role in a biological causative chain leading to death or graft failure but should be seen as an “independent” statistical predictor of clinical outcomes.

Despite these limitations, the present study has several strengths, including the extensive data collection and detailed documentation of clinical profile of the study cohort and derivation of the most sensitive comorbidity indices (typically not available in analyses of administrative databases) to enable comprehensive analysis of factors associated with graft and patient survival in renal transplantation. Findings indicate that a substantial proportion of kidney transplant recipients despite being younger, leaner, and mostly nondiabetic report physical HRQOL impairments and these impairments are important independent risk factors for poor clinical outcomes during a 12-year follow-up period. HRQOL provides additional clinical information regarding disease course and outcomes that is not captured by traditional indices of clinical status. Regular assessment of HRQOL in a clinical setting may help to identify high-risk patients who may benefit from increased attention and risk modification interventions. Finally, future research should identify the determinants of QOL and develop interventions to improve it.

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Study Design

A prospective observational study was conducted on transplant recipients who were followed for 12 years.

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Participants and Setting

Ethical approval for this study was obtained from the relevant National Health Service research ethics committees and written informed consent was obtained from each participant. Between 1998 and 1999, renal transplant recipients were enrolled into this observational prospective study. Participants were recruited from the renal transplant clinics at Middlesex and Royal Free Hospitals (London, UK) (8). Clinical practice and health-care procedures (posttransplantation follow-up care and patient support services) were identical on both sites. The two transplant units were subsequently merged in January 2006.

As the original study involved neuropsychologic evaluation (47), specific inclusion criteria were applied. All participants met the following inclusion criteria: (a) age 18 years or above; (b) capable of giving informed consent; (c) no impairment in vision, hearing, sensory, or motor function that might interfere with completion of questionnaires; (d) English language fluency; (e) absence of acute illness or currently hospitalized for any reason; (f) no psychiatric diagnoses necessitating ongoing treatment; and (g) a minimum of 3 months after transplantation with a successful kidney graft and stable kidney functioning.

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All measurements were taken at baseline (mean [SD], 8.57 [6.55] years after transplantation at enrolment; minimum, 0.5; maximum, 28.9) with no sequential interim data collected over the 12-year study window.

Sociodemographic information included age, gender ethnicity, education, work status and perceived ability to work, income, and living arrangements were collected using self-report questionnaire.

Clinical information was collected from medical records, including laboratory results obtained at the time of baseline assessment: smoking history (ever vs. never) based on self-report; nurses’ report or hospital medical records; BMI defined as kg/m2 (cutoff >25 corresponding to the World Health Organization’s grade 1 overweight) (48); eGFR (mL/1.73 m2) (49); albumin, hemoglobin; current medications including immunosuppressive regimen, primary kidney disease diagnosis, time on dialysis before transplantation, time elapsed since transplantation and comorbid conditions, used to generate the CCI (50). CCI scores were calculated using the method previously described by Jassal et al. (51).

We also calculated the risk recipient score based on diabetes, time spent on dialysis before transplantation, and cardiovascular history (52).

History of vascular disease was systematically recorded. Prior vascular disease was defined as history of stroke, myocardial infarction, coronary artery bypass grafting, angioplasty (excluding dialysis vascular access), amputation for peripheral vascular disease, or angiographic evidence of atherosclerotic vascular disease. Family history of cardiovascular disease was defined as having a first-degree relative who had a myocardial infarction or stroke before age 55 years in males or before age 65 years in females. Hypertension was defined as those patients with blood pressure more than 140/90 mm Hg or on antihypertensive medications. Diabetes (preoperatively and new-onset diabetes after transplantation) was defined as use of antidiabetic medication.

Functional status of the patients was assessed by transplant staff using the Karnofsky scale (53).

HRQOL was assessed with the SF-36 UK version 2, a validated tool for assessment of HRQOL (54–56). It contains 36 questions that can be used to compute a physical component summary score (PCS) and a mental component summary score (MCS). These scores are standardized to the UK population (mean [SD] score, 50 [10]) (55). The SF-36 has been proven reliable and valid in various patient populations including dialysis and transplant patients (57, 58). A difference of five points is considered a minimal clinically significant change (59). HRQOL impairments were defined as scores more than 1 SD (i.e., 10 points) below normative means (corresponding to the lowest 15.87th percentile of general population).

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Primary outcomes modeled included were all-cause mortality (including death with functioning graft or death after return to dialysis) and all-cause graft failure (a composite endpoint consisting of return to dialysis therapy, preemptive retransplantation, or death with function).

We used hospital’s computerized medical records system, individual case records, and contact with primary renal physicians to determine the date and cause of death of all subjects for up to 12 years (±5.11 months).

Survival times were calculated as the number of months from the baseline assessment (time zero at 8.57 [6.55] years after transplantation) until event (all-cause death/graft failure) or censoring (transfer out of the facility or the end of the observation period [September 30, 2011, at a maximum of 152 months]).

For sensitivity analyses, (patient and graft) survival times were also calculated based on the date of transplantation (time 0).

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Statistical Analyses

Descriptive statistics were expressed as mean (SD) for continuous variables and percentage for categorical variables. The differences between groups (responders and nonresponders; QOL impairment vs. nonimpairment) were analyzed by chi-square test for categorical variables and by one-way analysis of variance test for continuous variables.

The associations between the candidate independent variables (PCS, MCS, and demographic and clinical variables) and mortality/graft failure were examined using Cox regressions or Kaplan-Meier plots with log-rank test. Variables associated with study endpoints in the univariate analyses (P<0.01, to control for inflated type I error) were considered in the multivariate modeling. This approach was chosen to reduce as much as possible the number of variables included in multivariate models given the relatively small sample and limited number of overall events. The goal of our multivariate analyses was to determine the relative magnitude of PCS and MCS as predictors of clinical endpoints after adjusting for demographic and clinical risk variables. A backward elimination model selection procedure was implemented to generate parsimonious predictive models and generate odd ratios with 95% CI. Covariates were eliminated at a P value of 0.05 when using backward elimination. Given the variable times of assessment since transplantation in our sample, this covariate (time elapsed since transplantation) was forcedly kept in all multivariable models. Comorbidity was modeled as the count of self-reported diseases and weighed indices (i.e., CCI and risk recipient score) as well as by including individually the disease states found to be associated with an increased risk of death and graft failure in univariate analyses.

To confirm findings, we performed several sensitivity analyses using survival times based on date of transplantation and HRQOL as continuous scores.

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The authors thank the clinic staff and respondents who contributed to this study and Dr. Tonia Griva for her support.

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Quality of life; Mortality; Graft failure; Kidney transplantation

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