Perceived mental health at the start of dialysis as a predictor of morbidity and mortality in patients with end-stage renal disease (CALVIDIA Study)

Katia López Revuelta1, Fernando J. García López2, Fernando de Álvaro Moreno3 and Jordi Alonso4 on behalf of the CALVIDIA Group

1 Nephrology Unit, Fundación Hospital Alcorcón, 2 Unit of Clinical Epidemiology, University Hospital Puerta de Hierro, Madrid, 3 Nephrology Department, Hospital La Paz, Madrid and 4 Health Service Research Unit, Institut Municipal d’Investigació Mèdica (IMIM-IMAS), Barcelona, Spain

Correspondence to: Fernando J. García López, Unidad de Epidemiología Clínica, Hospital Universitario Clínica Puerta de Hierro, San Martín de Porres, 4, 28035 Madrid, Spain. Email: fjgarcia{at}medynet.com. Offprint requests to: Katia López Revuelta, Servicio de Nefrología, Fundación Hospital Alcorcón, c/ Budapest, 1, Alcorcón, 28922 Madrid, Spain. Email: klopez{at}fhalcorcon.es



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 
Background. Health-related quality of life may affect morbidity and survival in end-stage renal disease, but it is not clear whether coexisting comorbidity and other known prognostic variables could account for such an association.

Methods. To study the relationship between health-related quality of life and morbidity and survival, we carried out an inception cohort study in patients starting chronic dialysis, mostly diabetics, with a follow-up of 1–3 years in 34 Spanish hospitals. Health-related quality of life was measured by the SF-36 Health Survey and Karnofsky scale. Charlson age–comorbidity index and other prognostic clinical variables were measured concurrently. The primary outcome variable was time until death and the secondary outcome was hospitalization days.

Results. Of 318 patients enrolled (208 diabetics), with a median follow-up of 771 days, 80 died. In the unadjusted analysis, all-cause mortality was associated with lower SF-36 physical and mental component scores and Karnofsky scale. In the adjusted analysis, SF-36 mental component score predicted all-cause mortality (hazard ratio for a 10 point decrease: 1.28; 95% confidence interval: 1.05–1.56). The SF-36 mental component score also predicted more hospitalization days (adjusted risk ratio of each additional hospital day associated with every 10 point decrease: 1.25; 95% confidence interval: 1.08–1.45). Among diabetics, both the SF-36 physical and mental components predicted mortality and hospitalization days.

Conclusions. In end-stage renal disease, perceived mental health is an independent predictor of mortality and morbidity, mainly among diabetics patients.

Keywords: end-stage renal disease; functional status; health-related quality of life; morbidity; number of hospitalizations; survival



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 
In addition to mortality and morbidity, health-related quality of life assessment has become an outcome measure in the evaluation of the quality and effectiveness of care in end-stage renal disease (ESRD) [1]. The concept of health-related quality of life explores the impact of disease and healthcare on patients, their functional status and their self-rated or perceived health [2]. Reports on ESRD patients have shown impairment in the level of both functional status and self-rated health compared with the general population [3].

A question emerges about the possible prognostic role of health-related quality of life for survival. Some studies have found that functional status and health-related quality of life are risk factors for mortality and hospital admissions in ESRD patients on dialysis [1,4–8]. However, most studies have focused on prevalent patients and the question of whether perceived health is independent of other predictive factors has not been well established. The aim of our study was to assess whether health-related quality of life could be considered an independent predictor of mortality and morbidity in patients starting dialysis. To test this hypothesis, we studied patients who predominantly had diabetes and, therefore, a higher risk of mortality.



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 
The CALVIDIA Study was an inception cohort study of patients starting renal replacement therapy between 1 July 1996 and 31 October 1998 in 34 hospitals throughout Spain. A research ethics committee approved the study.

This study focused mainly on diabetic patients, since they have an increased risk for mortality, but a sample of non-diabetics was also included for comparison. In 29 hospitals, every new diabetic patient starting dialysis was invited to participate, whereas in five other hospitals all consecutive new patients requiring renal replacement therapy, both diabetic and non-diabetic, were invited. Patients diagnosed as having chronic renal failure and requiring dialysis or transplantation and who had been treated with these techniques for <1 month and gave their oral consent were included. Those patients in whom the health-related quality of life questionnaire was not administered, either because they died previously or because of severe physical or psychiatric impairments, were excluded. The planned follow-up was to range between 1 and 3 years.

Health-related quality of life was measured through a validated Spanish version of the generic Short Form 36JTM (SF-36) [9]. This health profile contains eight different dimensions (Physical Functioning, Role—Physical, Bodily Pain, General Health, Mental Health, Role—Emotional, Social Functioning and Vitality) and is aggregated in two summary scales: physical component score (PCS) and mental component score (MCS) [10]. Every SF-36 dimension and both SF-36 PCS and MCS are normalized to the general population, in which 50 is the population mean and the Standard Deviation (SD) is 10. All patients were instructed to complete the questionnaire on their own at home. In cases where the patient was unable to read, the questionnaire was administered by the investigator at the centre prior to a dialysis session. Functional status, defined as the degree that a person is able to develop their roles free of physical or mental impairment, was measured by the centre investigator through a modified version of the Karnofsky scale [11] at the same time as administering the SF-36.

The primary outcome variable was time until death. A patient was considered dead if he or she died between enrolment and 31 October 1999. The secondary outcome variable was hospitalization days, taken as a surrogate measure of morbidity. Every 3 months, investigators reported whether each patient was alive or dead and the number of hospitalizations they had during the previous 3 months, together with their corresponding dates of admission and discharge. If patients had died, investigators also reported the date and the causes of death. Information on the vital status or date of death of those patients on whom no follow-up data were available was collected from investigators at the close of the study.

Comorbid conditions were assessed by the Charlson combined age–comorbidity index [12]. The information required to compute the index was collected primarily from clinical records when dialysis started. Information was also collected on type of diabetes, smoking, history of blood pressure and current blood pressure considered as the average of three measurements taken on three different week days; analytical measurements taken immediately before the start of renal replacement therapy; date of starting treatment; treatment modality (haemodialysis, peritoneal dialysis, renal transplantation); and dialysis adequacy (urea reduction ratio in haemodialysis; weekly Kt/V in peritoneal dialysis).

Survival analysis was performed to test the primary hypothesis of an association between SF-36 summary scores and mortality. Log-rank tests were used to compare the Kaplan–Meier estimates of event rates between several groups. Cox proportional hazard models were used to calculate hazard ratios of death and their 95% confidence intervals (CI) both in unadjusted and adjusted analysis. The time of origin was the date of first dialysis session. The event defined was death whereas those cases alive at the end of follow-up and those lost to follow-up were censored at their last observation. The results of the hazard models were expressed as the hazard ratio of death between the group with the characteristic studied and the group without it, which is the ratio of their rates of observed death count relative to the total amount of previous follow-up time and its corresponding 95% CI.

Confounding factors were included in the final models if they reached statistical significance, measured by likelihood ratio statistics, or if they changed the main variable estimates by >15%. These potential confounding factors included gender, age, diabetes, Charlson index, smoking (number of pack-years), current systolic and diastolic blood pressure, haemoglobin, creatinine, albumin (last measurements before dialysis start), urea reduction ratio (computed in one haemodialysis session) and first dialysis modality (haemodialysis or peritoneal dialysis). Centre was included into all adjusted models. Proportional hazard assumption was checked for all variables in the final models by testing whether the interaction of time since enrolment and the variable was significant. To assess the influence of diabetes status, we fitted an adjusted model with the same variables, but the Charlson index was modified by removing diabetes points, plus diabetes status and an interaction term between SF-36 MCS and diabetes status. We also made stratified analyses for diabetic and non-diabetic patients.

For each patient we computed the days of hospitalization from the start of dialysis until the date of death, the date of admission for receiving a renal transplant or the date of the last visit, whichever came first. For examining hospital days, which are recurrent events, we used negative binomial regression to accommodate the overdispersion or extra-Poisson variation in the outcome measure [13]. This distribution allows the desirable properties of asymmetry and dependence of the variance on the mean provided by the Poisson distribution, but uses a more flexible expression for the mean–variance relationship. The results of the negative binomial regression were expressed as the ratio between two rates of observed hospital days relative to the total amount of follow-up time for patients with a given characteristic and patients without it and its corresponding 95% CI.

Although the final dataset only included complete observations of the health-related quality of life variables, a few observations were missing in some other variables. To assess the influence of missing observations on the final results, separate analyses were performed for the subset with complete observations and for the whole dataset with missing values estimated through a multiple imputation method. Comparison of means was made using unpaired two-sample t-tests. The statistical analyses were performed using GLIM version 4.0 and SAS version 8.2 (SAS Institute Inc., Cary, NC, USA). Statistical significance was set at P-values of <0.05. P-values were two-sided.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 
In total, 390 white patients were contacted. Of these, 351 patients were enrolled and 231 (66%) were diabetic and 120 (34%) were non-diabetic. Thirty-nine were excluded from completing the questionnaire because they refused consent (14), were physically (17) or mentally impaired (14) or they died before completing it (16). Only 318 patients provided data from all dimensions to compute both the SF-36 PCS and MCS. In 49 cases (15%) lost to follow-up, the vital status was collected from investigators at the close of the study. The median follow-up time was 771 days (range: 9–1259 days) and was complete with regard to vital status in 280 patients (88%). A total of 80 (25%) patients died between 46 and 1196 days after the start of treatment: 63 (30%) diabetic patients (0.175 deaths per person-year) and 17 (15%) non-diabetic (0.066 deaths per person-year). In 12 cases, the information on causes of death was missing. Among the 68 cases (85%) in whom the cause of death was available, this was cardiovascular in 38 patients (56%), infection in 12 (18%), cancer in three (4%), withdrawal of treatment in two (3%), other in eight (12%) and unknown in five (7%).

Our sample was principally composed of aged, male, type 2 diabetic patients on haemodialysis (Table 1). Both SF-36 PCS and MCS were well below 50, which is the expected average from the general population. Together with diabetes, enrolled patients had a high prevalence of congestive heart failure, cerebrovascular disease, chronic pulmonary disease and peptic ulcer disease (Table 2).


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Table 1. Baseline characteristics of the 318 patients at start of renal replacement therapy

 

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Table 2. Frequency of associated conditions included in the Charlson index in patients when starting renal replacement therapy

 
With regard to health-related quality of life and functional status variables, a lower SF-36 PCS, a lower SF-36 MCS and lower Karnofsky scale at the commencement of renal replacement therapy were all significantly associated with worse survival in the unadjusted analysis for all-cause mortality (Figure 1, Table 3).



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Fig. 1. Kaplan–Meier survival curves for functional status and perceived health. Dark lines indicate Kaplan–Meier survival estimates and light lines indicate their 95% CI. (A) Karnofsky scale: log-rank test, P<0.0001. (B) SF-36 PCS: log-rank test, P = 0.01. (C) SF-36 MCS: log-rank test, P = 0.007.

 

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Table 3. Cox analysis for all-cause mortality with SF-36 PCS, SF-36 MCS and Karnofsky scale

 
In the adjusted model, the hazard ratio for mortality for every 10 point decrease in SF-36 MCS was 1.28 (95% CI: 1.05–1.56), whereas neither SF-36 PCS nor Karnofsky scale reached statistical significance (Table 3). When we restricted the analysis to diabetic patients, in addition to SF-36 MCS, SF-36 PCS was also significantly associated with mortality (Table 4). In non-diabetics, the effect of SF-36 MCS on mortality was not statistically significant (hazard ratio for every 10 point decrease: 1.09; 95% CI: 0.74–1.61).


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Table 4. Cox analysis for all-cause mortality with SF-36 component scores and Karnofsky scale in diabetic patients

 
The separate survival analyses of each single SF-36 dimension showed that every dimension was a prognostic factor for all-cause mortality in the unadjusted analysis, whereas in the adjusted analyses the dimensions General Health, Mental Health and Role—Emotional were also independent predictors for mortality (data not shown).

There were 1.33 hospital admissions and 15.8 hospital days per patient-year at risk. In the adjusted analysis, lower SF-36 MCS was the only variable associated with more hospital days (Table 5). Lower SF-36 MCS were also associated with more hospital days in separate analyses among both diabetics (risk ratio of each additional hospital day associated with every 10 point decrease: 1.22; 95% CI: 1.03–1.45) and non-diabetics (risk ratio: 1.47; 95% CI: 1.17–1.84) whereas lower SF-36 PCS were associated with more hospital days only in diabetics (risk ratio of each additional hospital day associated with every 10 point decrease: 1.43; 95% CI: 1.03–1.99).


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Table 5. Influence on the number of hospitalization days of SF-36 PCS, SF-36 MCS and Karnofsky scale

 


   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 
Our study in a predominantly diabetic population in dialysis showed that, in addition to known clinical prognostic variables for mortality in ESRD population, health-related quality of life measured during the first month following the start of renal replacement therapy also predicted subsequent mortality. In particular, the mental component of health-related quality of life had an independent influence on all-cause mortality, higher in diabetics than non-diabetic patients. The physical component of health-related quality of life had an independent influence on mortality only in the subgroup of diabetic patients. Although previous studies have explored the relationship between perceived health on mortality in patients on dialysis [1,4,6,8], our findings highlight the independent prognostic value of SF-36 MCS at the moment patients start dialysis. Merkus et al. [5] did not find a direct influence of low MCS at baseline on mortality, but they administered SF-36 at 3 months after the start of dialysis, somewhat later than we did. As perceived health changes with time, these differences could have been accounted for by the differences in time of administration. Other authors have found an adjusted association between SF-36 PCS and mortality [1,4,5] in dialysis patients and the reason why our results differ are not clear and might be due to the different timing of the SF-36 measure, the different patient features or the different methods for comorbidity adjustment. However, our results do not allow us to rule out a small independent influence of physical perceived health on overall mortality.

The prediction of mortality by mental health-related quality of life component is consistent with the prognostic effect of the specific SF-36 dimension scores General Health, Mental Health and Role—Emotional, as was expected since these dimension scores are part of the calculation of the SF-36 MCS. The reason for these associations is unclear. Mental dimensions of quality of life, as other authors have also shown [14], are less related to comorbidity than physical dimensions, as we showed in our sample and as suggested by the stability of SF-36 MCS estimates in comparison to SF-36 PCS estimates in our adjusted analyses. Therefore, residual confounding from biological factors is unlikely. On the other hand, depression has been related to mortality in ESRD patients [15] and an association between depression and health-related quality of life has been found at the start of dialysis [16]. In fact, the SF-36 MCS has been found to be useful in detecting patients diagnosed with a depressive disorder [10].

Our report also showed an independent influence of mental and physical health-related quality of life on the subsequent days of hospitalization in diabetic patients starting dialysis and an influence of SF-36 mental component on hospitalization days among non-diabetic patients. Other authors have also found associations between health-related quality of life components and hospital admission in prevalent dialysis patients [1,4,6–8].

The reasons for the influence of mental perceived health on hospital admission are a matter for discussion, but it can be hypothesized that they must be the same as those that affect mortality. Unfortunately, in our dataset we could not distinguish between non-elective and elective hospitalization, since the latter would potentially be more likely to be affected by changes in health-related quality of life. Our results did not support the independent prognostic value of Karnofsky scale.

Our study may have some potential limitations. Firstly, as mortality in diabetic patients is higher, our sampling method was focused mainly on diabetic patients to gain statistical power, so the sample may not be wholly representative of the average dialysis population but rather of diabetic population. Secondly, we also had a loss at follow-up rate of 12% with regard to vital status, but this does not seem to have a great significance, since we found similar results in the analysis restricted to those centres where follow-up was complete or almost complete and demography and comorbidity were similar regardless of the completion of follow-up. Thirdly, although we used the Charlson index, which has been proved to be a good prognostic tool in end-stage renal failure [17–20], we cannot rule out the possibility that some residual comorbidity was not well reflected in this age–comorbidity index or exclude confounding due to unmeasured or unknown variables. For instance, the influence of dialysis adequacy was insufficiently addressed in our sample since the urea reduction ratio measured during the first treatment month could not accurately predict long-term survival.

Given the observational nature of our study, no conclusions can be drawn with confidence with relation to the current clinical practice, but the future consequences might be significant. Our finding of the likely influence of mental health-related quality of life on survival should encourage research on ways to detect poor perceived mental health and to improve it. While the influence of perceived physical health on survival is still undetermined in non-diabetic patients and should be studied with more detail in further observational studies, its more patent influence on diabetic patients should trigger research to improve physical health-related quality of life. The hypothesis that a psychological intervention directed to improving mental health and interventions to help physical health-related quality of life may eventually affect survival should be tested in randomized controlled trials, because if confirmed, they would become a valuable additional tool to the betterment of both survival and health-related quality of life in ESRD. Measurements of health-related quality of life scores at the start of dialysis could also be useful to improve the knowledge of the risk profile of every patient.

In summary, we found a relationship between perceived mental health at the first month of renal replacement treatment and morbidity and mortality in dialysis patients independently of comorbidity. This relationship is stronger in diabetic dialysis patients, in whom both physical and mental health-related quality of life are associated with morbidity and mortality. Our results highlight the importance of health-related quality of life in general and mental factors in particular, on clinical outcomes in ESRD patients. Research into mental health should be expanded. It is time to consider whether there may be some intervention that could modify mental health in renal patients and subsequently improve their prognosis.



   Appendix: CALVIDIA Group
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 
Coordinators
K. López Revuelta (Fundación Hospital Alcorcón), principal investigator; F. García López (Clínica Puerta de Hierro, Madrid), epidemiology and statistics.

Scientific committee
F. de Álvaro Moreno (Hospital ‘La Paz’, Madrid); A. Martínez Castelao (Hospital de Bellvitge, Hospitalet de Lobregat); J. Alonso (Institut Municipal d’Investigació Mèdica, Barcelona); F. Álvarez-Ude Cotera (Hospital General, Segovia); I. Gimeno, J.Ma. Martínez García (Hospital de Cruces, Barakaldo); E. Gago González (Hospital Central de Asturias, Oviedo); J.L. Miguel Alonso (Hospital Universitario La Paz, Madrid).

Investigators
E. Hernández, D. Bernal (Fundación Hospital Alcorcón); M. Albalate, A. Galera (Fundación Jiménez Díaz, Madrid); E. Martínez Camps, T. Doñate Cubells (Fundación Puigvert, Barcelona); J. Portolés Pérez, C. Gómez Roldán (Hospital General, Albacete); C. del Pozo Fernández (Hospital ‘Virgen de los Lirios’, Alcoy); A. Fidalgo González (Hospital Ntra. Sra. de Sonsoles, Avila); J.A. Rodríguez, L. Piera Robert (Hospital Vall d’Hebrón, Barcelona); E. Fernández, Ma.M. García, (Hospital Puerto Real, Cádiz); L. Lozano Maneiro, J. Usón (Hospital ‘Virgen de la Luz’, Cuenca); J.Ma. Galcerán Gui, R. García Osuna (Hospitalde Palamós, Gerona); P. Galindo, C. Soriano (Hospital ‘Virgen de las Nieves’, Granada); G. de Arriba, A. Monasor (Hospital General, Guadalajara); J.Ma. Cruzado Garrit (C.S.U. de Bellvitge, Hospitalet); R. Virto Ruiz, J.Ma. Logroño González (Hospital General San Jorge, Huesca); E. Huarte Loza, M. Artamendi (Complejo Hospitalario San Millán-San Pedro, Logroño); J.A. Sánchez Tomero, A. Cirugeda (Hospital ‘La Princesa’, Madrid); J.L. Teruel, L. Orofino (Hospital Ramón y Cajal, Madrid); C. Díaz Corte (Hospital Central de Asturias, Oviedo); J.Ma. Monfá, E. Hernández (Hospital ‘Río Carrión’, Palencia); E. Pons, N. Mañe, (Hospital Parc Taulí, Sabadell); R. Sánchez (Hospital General, Segovia); L.M. Pallardó Mateu, J.L. Górriz Teruel (Hospital ‘Dr Peset’, Valencia); A. Molina Miguel, C. Ruiz Erro (Hospital ‘Río Hortega’, Valladolid); F. Viana Apraiz (Hospital Santiago Apóstol, Vitoria); R. Ruiz de Gauna, J.I. Minguela (Hospital Txagorritxu, Vitoria); J. Montenegro Martínez, M. Lanzagarte (Hospital de Galdakao, Vizcaya); A. Sanjuán Hernández-Franch, Ma.P. Martínez Rubio (Hospital ‘Miguel Servet’, Zaragoza).



   Acknowledgments
 
The authors wish to thank Víctor Abraira for statistical advice and Martin Hadley-Adams for his assistance with the English language. An earlier version of this paper was presented at the 33rd Annual Meeting of the American Society of Nephrology, 10–16 October 2000, Toronto, Ontario, Canada and an abstract was published in J Am Soc Nephrol 2000; 11: 156A. This work was supported by a grant from el Fondo de Investigación Sanitaria, from the Spanish Ministry of Health (FIS 96/031).

Conflict of interest statement. None declared.



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Appendix: CALVIDIA Group
 References
 

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Received for publication: 31. 7.03
Accepted in revised form: 4. 6.04





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