1 MRC Biostatistics Unit, Cambridge, 2 Renal Unit, Addenbrookes Hospital, Cambridge, UK
Correspondence and offprint requests to: Dr J. Firth, Box 118, Addenbrookes Hospital, Hills Road, Cambridge, CB2 2QQ, UK.
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Abstract |
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Methods. Quantitative techniques were used to estimate relative risk of death in the seven studies quoted in the RASD document and 17 other papers identified in a systematic literature search. Relative risk data from each study were pooled using a fixed effects model (f). A random effects model (r) was applied to pool relative risks if heterogeneity was found to exist between studies. A meta-regression analysis was also carried out to investigate whether study covariates substantially explained the heterogeneity between studies.
Results. Pooling the papers identified in the systematic literature search with those from the RASD gave rise to a relative risk of death of 1.029 (95% CI 1.0131.045) (r) associated with each year's increase in age. The relative risk associated with the presence of diabetes was 1.91 (95% CI 1.672.17) (r), whilst that associated with heart disease was 1.59 (95% CI 1.491.69) (f), and with peripheral vascular disease 1.58 (95% CI 1.291.93) (r). Heterogeneity was found in the estimates of risk associated with age, diabetes, and peripheral vascular disease. Important study covariates included the use of incident or prevalent cases, the use of routine data sources or data collected specifically for a particular study, the country in which the study was located, the use of a P value to infer the standard error of a relative risk estimate in a particular study, and the method of classifying diabetes.
Conclusions. Published studies can be used to quantify the relative risk of death for dialysis patients with various comorbidities. This information is important if attempts are to be made to set standards for the performance of dialysis units, and to compare the performance of one dialysis unit with that of another.
Keywords: chronic kidney failure; comorbidity; dialysis; renal replacement therapy; risk factors; survival
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Introduction |
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`Survival of patients during treatment for End Stage Renal Failure ... [has] been shown to be influenced by age and by many comorbid conditions such as diabetes mellitus, ischaemic heart disease, congestive heart failure, liver disease, respiratory disease and peripheral vascular disease.' (Renal Association Standards Document) [3].
In order to support this statement, the authors of the Renal Association Standards Document (RASD) cited seven papers [410]. Here, we investigate whether these seven papers can provide us with valid, quantitative estimates of the modulating effects of age, presence of diabetes and other comorbidities on the risk of death in patients with end-stage renal disease (ESRD). Since bias could have arisen in the selection of the papers cited, we also carried out a systematic literature search and quantitative review of 17 other papers identified. We deal here with the relative risk associated with age, diabetes, heart disease, and peripheral vascular disease as there were insufficient data provided in the papers to investigate the relative risks associated with other comorbidities listed in the RASD [3].
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Methods |
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Data extraction and pooling of data
Data on the modulating effects of age, diabetes and other comorbidity were extracted from the text and tables of the selected papers. To estimate relative risk of death, according to the patient characteristics of interest, data were pooled, firstly across the RASD papers and then across the 17 retained papers identified in the literature search. Finally, pooled estimates of risk were derived for all papers combined.
Initially, data were pooled using a fixed effects model (f), which assumes that the relative risk from each study is an estimate of the global relative risk of death. The method of data pooling takes account of the quality of individual studies, such that a larger study with a more precise estimate of risk contributes more to the pooled result than does a smaller study with a less precise estimate. In brief, each observed relative risk described in any study represents an estimate of the true relative risk between, for example, a patient with diabetes and one without diabetes. Each estimated relative risk is qualified by its standard error, which was either stated in the paper, estimated from the 95% confidence interval ((upper limitlower limit)/3.92), or derived from a P value (P value of<0.001 was taken to be P=0.001) and its associated z-score, whereby ln(RR)/z-score=se(ln(RR)). The larger the standard error, or variance (var(RR)=(sei) [2]) in study i, the less precise is the observed relative risk as an estimate of the true risk. It therefore follows that a less precise estimate should contribute less to the pooled estimate than a more precise relative risk. The information that each estimate contributes is defined as the inverse of the variance. The weight (wi) given to each relative risk is equal to the information in this particular (i [th]) study divided by the sum of information across all n studies. The weights clearly add to one. The pooled relative risk estimate is the sum of the individual relative risks (on a logarithmic scale), each multiplied by its corresponding weight. If a single study estimate provides 10% of the information, it also contributes 10% of its estimated relative risk to the pooled relative risk estimate. The information was defined as the inverse of the study variance, and so, to obtain the variance of the pooled estimate, we take the inverse of the total information.
A test for heterogeneity across studies was carried out using STATA [33], to test the null hypothesis that the relative risk in each study is an estimate of the same global relative risk of death. Heterogeneity is said to be present if the test statistic lies at the extreme end of a 2 distribution on n-1 degrees of freedom. Where heterogeneity across studies was found, data were then pooled using a random effects model (r) [34] to allow for variation in relative risk between studies.
Only those results from multifactorial analyses were pooled, as confounding was expected to have been well controlled in such analyses. We did not pool data on age when treated as a categorical variable, as each of the studies reviewed had used different age categories. Two studies [7,13] gave several measures of relative risk according to varying severity of comorbidity; all of these measures of relative risk were included in the pooled estimates.
Finally, a meta-regression analysis was carried out to investigate whether any marked heterogeneity could be explained by study covariates, namely whether incident or prevalent cases were studied, whether data were drawn from an existing database or specifically collected for the study, the country of study, the effect of inferring the standard error of a relative risk estimate from its associated P value, and the method of classifying diabetes. Meta-regression was carried out using an iterative procedure, restricted maximum likelihood [35].
Assessing possible sources of error in the pooled estimates of risk
We carried out a regression asymmetry test [36] (Egger's test), using STATA, to look for publication bias in the data. The concept behind this test is that, if we assume that all studies in the analysis are estimating the same true effect, we would expect the study estimates to be distributed around the unknown true estimate of risk, with a spread proportional to the study variance of each. That is, small studies would be spread widely around the estimated true risk, and larger studies more narrowly around the true risk. Lack of symmetry in such a distribution indicates the possibility that not all studies are represented in the analysis. By applying regression techniques, Egger's test is a formal means of quantifying whether this is indeed the case. In Egger's test, the standardized effect estimate (ln(RR)/se(ln(RR))) was plotted against the precision of the estimate (1/se(ln(RR))), and a variance-weighted regression line and confidence interval fitted. Failure of the confidence interval to include zero provides evidence of possible publication bias. Other possible sources of error were assessed qualitatively.
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Results |
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Gender
Few of the papers provided information on the effects of gender on mortality, hence it was not possible to provide separate analyses for mortality in men and women.
Diabetes
The relative risk associated with the presence of diabetes was found to be 1.76 (95% CI 1.332.32) (r) from the RASD papers (Tables 1 and 2), yet was somewhat higher, at 1.98 (95% CI 1.712.30) (r), when data were also drawn from the studies identified in the literature search. Due to heterogeneity between studies, a random effects model was used to pool data from all the papers, giving an overall estimate of relative risk at 1.91 (95% CI 1.672.17) (r) (Figure 2
). Some of the heterogeneity in the pooled estimate from all the papers was explained by the use of incident or prevalent cases in the studies (P<0.0001). The pooled estimate of relative risk was 2.62 (95% CI 2.243.08) (f) if both incident and prevalent cases were included in the sample, as compared to 1.70 (95% CI 1.551.86) (f) if only incident, or 1.80 (95% CI 1.602.03) (f) if only prevalent cases were used. The use of routine data sources as opposed to data collected specifically for the study did not contribute to heterogeneity between studies. The relative risk of death associated with the presence of diabetes in the studies carried out in USA was 1.60 (95% CI 1.401.82) (f), whilst it was 1.97 (95% CI 1.822.13) (f) elsewhere. The use of standard errors inferred from P values, as compared to those stated did not affect the pooled estimates of relative risk gained.
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Other comorbidity |
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Discussion |
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Study selection bias
Selection bias may have been introduced if the inclusion of patients in a study cohort was associated with one of the patient characteristics of interest, namely age, diabetic status, or the presence of another comorbid condition. Such bias could arise both from the characteristics of the database itself, and from the method by which patients were selected from the database for the purposes of any particular investigation. Regarding database characteristics, several sources of selection bias can be identified. Firstly, 20 of the studies drew data from existing databases or registries of patients (Table 2), and of these all but two [5,12] were carried out retrospectively, such that the authors did not control the nature of the data collected. Second, 18 of these 20 studies employed data from more than one dialysis centre, and only five [8,9,11,20,29] investigated between centre differences. Selection bias could therefore have been introduced, for example, by centres using different protocols for deciding the time at which to start maintenance dialysis, by whether or not acute dialysis patients were included on their database, or by the centre transplantation policy. Third, some databases included only incident cases of ESRD, whereas others included prevalent as well as incident cases (Table 2
). The relative risks for age, diabetes, and comorbidity in studies based on prevalent cases were higher than those based on incident cases. This suggests that the risk of death increases with time spent on dialysis, and may be due to the fact that patients on long-term dialysis have a gradual deterioration in health, or due to the fact that patients with a higher risk of death do not receive a transplant and hence are those who remain on dialysis. Fourth, according to the standard practice of the US Renal Data System, two of the studies explicitly excluded patients who had died within the first 90 days after the start of dialysis [7,29]: it is likely that those who died earlier were older or burdened with more comorbidity. Finally, data quality will also have been affected by the method by which databases were updated: whether they were modified continuously, or irregularly.
Apart from selection bias in the database itself, further bias may have been introduced when patients were selected from databases or registries for inclusion in a study. For example, many studies excluded patients who had transferred between dialysis units [7,8,10, 12,16,20,22,23,25], and some selected patients in specific age-groups only [5,9,20,24].
In five of the reviewed papers [4,6,13,15,19], data were collected specifically for the study. However, these were not free of selection bias. Patients with dementia, an age-related condition, were excluded in one [15]. Another described only patients on CAPD [19], and the choice of treatment modality would almost certainly have been influenced by the presence of comorbidities. Recruitment into a third study [4] was voluntary, leading to only 40% of incident dialysis patients being included. In summary, each of the 24 reviewed papers contained at least one possible source of selection bias.
Patient characteristics misclassification bias
Misclassification bias may have been introduced with regard to patient characteristics, namely, age, diabetic status and the presence of other comorbidities. For example, it was not clear in Medina et al. [15] whether age was treated as a linear or categorical variable in the Cox proportional regression. The relative risk associated with age in this study is far lower than that in the other studies (Figure 1), due either to age being treated as categorical, or because the study was based on diabetics only. This study contributed to the high degree of heterogeneity between relative risk estimates. It is therefore appropriate to use a random effects model to pool the data on age.
There was confusion in many of the studies over whether diabetic status referred to diabetic nephropathy as the primary cause of renal failure, or whether the same classification would be given to diabetics whose renal failure had another cause. Even in patients with incident ESRD, the presence of diabetes may or may not imply that it had caused renal failure. Similarly, none of the studies based on prevalent cases of ESRD reported the date of onset of diabetes in patients on dialysis, and hence it was not possible to distinguish patients who had had diabetes since the start of dialysis treatment from those with later onset.
In retrospective studies, particularly those involving many centres, it is likely that there will have been variations between physicians in the diagnosis and recording of comorbid conditions. More valid data on comorbidity are available from prospective studies, such as Barrett et al. [13] in which the presence and severity of heart disease could be assessed at the time of onset of dialysis treatment. The variations in classification of comorbidity between and within studies make the validity of pooling relative risks based on different definitions questionable, but is also an argument for the use of random effects models. Hence, the pooled estimates of risk according to comorbidity should be interpreted with caution. In addition, comorbid conditions were assessed at the start of dialysis and none of the studies took into account changes in the prevalence or severity of comorbidity over time. The use of risk classification schemes meant that the influences of age, diabetes, and comorbidity could not be independently assessed in some studies [8,9].
Patient outcome misclassification bias
The main source of error in dealing with outcome of patients was the censoring of patients who received a transplant. Seventeen of the studies stated that patients were censored at transplantation, but none stated whether patients returned to the cohort if their graft failed. Similarly, it was not stated if the patient was followed up until death with a functioning graft. Two of the studies [11,26] did not censor patients at transplantation, and five [4,8,9,13,29] did not state how transplantation was managed in the analysis. This lack of clarity may have led to misclassification of the treatment modality: whether survival on both dialysis and transplantation or purely on dialysis was being assessed.
Loss to follow-up may also have introduced bias. Follow-up was near complete in 11 studies, likely to be near complete in three [810], incomplete in five [15,19,20,24,26], and the number lost to follow-up was not stated in six [5,6,13,16,22,28].
Confounding
The observed association between age, diabetes or other comorbidity, and the risk of death may have been confounded by the presence of another variable which was associated with both the patient characteristics of interest and the risk of death on dialysis. Potential confounders may have been introduced due to the characteristics of different dialysis centres, such as the frequency of dialysis given, the dialysis modality preferred (haemodialysis or continuous ambulatory peritoneal dialysis), the time at which dialysis treatment was typically initiated in a patient, or the rate of transplantation at a particular centre. The influence of such confounders would be expected to be greater if the time period over which patients were enrolled spanned many years [7,10], as there might be systematic changes in the treatment regimens offered. Confounding may also have been introduced by differences in unmeasured patient characteristics, such as social class or race.
The studies examined demonstrate many attempts to control for confounding. Some samples were restricted to one dialysis centre [6,10,20], or a short period of time, such as 3 years [15]. Others controlled for centre differences by analysing data separately for different dialysis centres [8,9]. Change in treatment regimens over time was controlled by stratifying for time of entry into the study [7,10]. In the main, however, confounding was controlled by way of multifactorial analysis. In particular, two centres controlled for a centre effect in the multifactorial analysis [11,29]. We drew only on estimates of risk derived from multifactorial analyses for the purposes of obtaining pooled estimates of risk.
There is some concern that studies using a logistic regression analysis instead of Cox proportional hazards regression did not fully control for the fact that patients had spent different amounts of time in the study cohort [12,13]. In addition, the proportional hazards assumption was tested in only four of the studies that used this method of analysis [4,9,15,24]. If the assumption was found not to hold in the remaining studies, confounding may have been inadequately controlled: there may have been a decay in the effect of baseline prognostic factors on relative risk over time, or there may have been non-proportionality in times to peak hazard. In addition, it is possible that variation in the methods by which multifactorial models were constructed (e.g. by a stepwise method, or by including known confounders even if not statistically significant) may have led to confounding not having been completely controlled in some studies.
Refutation of unmeasurable biases and confounders
We cannot entirely refute the possible role of publication bias, selection bias, and confounding and, to some extent, misclassification bias in producing the observed effect of age, diabetes, and other comorbidity on the relative risk of death on dialysis. However, these associations are biologically plausible, there is a consistency in the evidence from the studies, and the strength of the associations is clear.
It would be of interest to investigate the relationship between severity of comorbidity and death; however, this was not possible from the papers identified. Similarly, none of the studies addressed whether there was interaction between comorbid conditions, that is, whether the presence of two comorbid conditions had a synergistic effect on the risk of death.
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Conclusion |
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However, our systematic overview of the literature demonstrates that comparisons of survival of dialysis patients in different centres will always be difficult if data collection is not standardized. But we recognize that the recommendation of standards is always a perilous and subjective matter. None can claim the right to set them. In this context, some might suggest the collection of a complex data set, whereas those of a more pragmatic nature (such as ourselves) would favour something relatively simple. Whilst not wishing to dissuade those of a more ambitious disposition, we would suggest that all dialysis units be encouraged to keep records of the following characteristics of all incident chronic dialysis patients: date of first dialysis, age, sex, diabetes mellitus as a cause of renal failure (yes/no), clinical evidence of heart disease on history, examination or simple testing (yes/no), clinical evidence of peripheral vascular disease on history, examination or simple testing (yes/no), date of transplantation or death.
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Appendix |
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(1) Renal-Replacement-Therapy OR (2) Kidney-Failure, -Chronic
AND (3) Dialysis
or (4) Hemodialysis
or (5) Peritoneal-Dialysis
or (6) Peritoneal-Dialysis,-Continous-Ambulatory
not (7) Transplantation
AND (8) Survival
or (9) Survival-rate
or (10) Survival-analysis
or (11) Mortality
AND (12) Diagnosis-related-groups
or (13) Risk-factors
or (14) Comorbidity
Similarly, on EMBASE the following search terms were used:
(1) Chronic Kidney failure
AND (2) Continuous ambulatory peritoneal dialysis
or (3) Haemodialysis/hemodialysis
AND (4) Survival/or survival rate/ survival time
or (5) Mortality
or (6) Case Mix
or (7) Risk factor
or (8) Comorbidity
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Acknowledgments |
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References |
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