Predictors of hospitalization and death among pre-dialysis patients: a retrospective cohort study

David C. Holland1, and Miu Lam2

1 Division of Nephrology and 2 Department of Community Health and Epidemiology, Queen's University, Kingston, Ontario, Canada



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Although there is abundant research describing predictors of patient morbidity and mortality among dialysis patients, predictors of adverse clinical outcomes among pre-dialysis patients are less well defined. The purpose of this study was to identify baseline predictors of first non-elective hospitalization among a retrospective cohort of 362 pre-dialysis patients.

Methods. Univariate and multivariate Cox proportional hazard models were used to identify predictors of hospitalization prior to dialysis initiation, adjusted for baseline creatinine level. Dialysis initiation, loss to follow-up, and study conclusion were censored events. Secondary outcomes included cause-specific hospitalization and death.

Results. Univariate analysis indicated that advanced age (RR 1.026, CI 1.016–1.037), number of prescribed anti-hypertensive medications (RR 1.149, CI 1.019–1.296), history of myocardial infarction (RR 1.979, CI 1.339–2.926), congestive heart failure (RR 2.299, CI 1.616–3.270), angina (RR 2.289, CI 1.695–3.091), peripheral vascular disease (RR 1.841, CI 1.282–2.644), renal failure secondary to nephrosclerosis (RR 1.413, CI 1.033–1.933) or renal artery stenosis (RR 1.587, CI 1.036–2.430), lower baseline haemoglobin level (RR 0.986, CI 0.979–0.992), and baseline creatinine greater than 300 µmol/l (RR 1.636, CI 1.233–2.171) were predictors of hospitalization. Gender, diabetes, diastolic blood pressure, mean arterial pressure, history of stroke, and hypoalbuminaemia did not predict outcome. Multivariate analysis, adjusted for baseline creatinine level, selected advanced age (RR 1.017, CI 1.006–1.027), angina (RR 1.893, CI 1.371–2.613), peripheral vascular disease (RR 1.545, CI 1.054–2.266), and haemoglobin level (RR 0.987, CI 0.944–0.979) as independent predictors of hospitalization.

Conclusion. Advanced age, co-morbid cardiovascular illness and anaemia are independent predictors of non-elective hospitalization prior to dialysis initiation. Further study is needed to determine the extent to which aggressive pre-dialysis management of anaemia and cardiovascular disease can improve patient outcomes.

Keywords: chronic renal insufficiency; morbidity; mortality; outcomes; pre-dialysis; progressive renal insufficiency



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The Canadian Organ Replacement Registry has reported that the prevalence of end-stage renal disease has increased approximately 10% annually from 1980 to 1994, primarily reflecting the demographic changes of an ageing Canadian population [1]. Pre-dialysis patients are also an important segment of the growing renal failure population. Using data from the Second National Health Nutrition Examination Survey, Strauss et al. estimated that the size of the US pre-dialysis population in 1990 was approximately 700 000 people [2]. The National Institutes of Health have recommended that patients with chronic renal failure should be referred to a multidisciplinary team for pre-dialysis management in order to minimize patient morbidity and ensure a smooth transition to dialysis therapy [3]. Several studies have reported that timely referral to a pre-dialysis programme can decrease the risk of adverse patient-oriented outcomes at the time of dialysis initiation [4–12]. Patients referred to a multidisciplinary pre-dialysis teams are better nourished, demonstrate better metabolic profiles, are less likely to require central venous catheter insertion, and require fewer urgent dialysis starts and hospital admission days at the time of dialysis initiation compared to patients who receive standard care. Nonetheless, despite the growing use of pre-dialysis programmes, patient morbidity and mortality remain excessive. Specific baseline risk factors identified at the time of dialysis initiation can predict increased patient morbidity and mortality [13–25]. These risk factors include advanced age [14,20,25], cardiovascular disease [14,20,21,25], and hypoalbuminaemia [14,22–25]. Unfortunately, the prognostic importance of similar variables among pre-dialysis patients is less clear. The purpose of the present study was to identify baseline demographic, clinical, and biochemical features predicting hospitalization among a retrospective cohort of pre-dialysis patients. Recognition of pre-dialysis risk factors will focus limited health care resources on high-risk individuals and encourage correction of modifiable risk factors.



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Patient selection
The study cohort was selected from among all out-patients referred to the nephrology service at Kingston General Hospital, Ontario, Canada from 1 January 1990 to 1 July 1997. Kingston General Hospital is the only centre in the region of South-eastern Ontario offering pre-dialysis and dialysis care, serving a population of approximately 450 000 people. Referral patterns are therefore highly centralized, offering remarkably complete patient follow-up. Patient inclusion criteria were age >16 years, diagnosis of chronic irreversible renal failure, and attendance at a pre-dialysis clinic on at least one occasion. Chronic renal failure was a clinical diagnosis made by a nephrologist at the time of initial assessment prior to referral to the pre-dialysis clinic. The diagnosis of chronic renal failure and the decision to transfer a patient's care to the pre-dialysis clinic did not require a pre-specified creatinine level. Inclusion in the pre-dialysis clinic implied that, in the opinion of the nephrologist, patient life expectancy and quality of life would be benefited by future dialysis initiation.

The pre-dialysis clinic is staffed by a multi-disciplinary team, including nephrologists, pre-dialysis nurses, dieticians, and social workers. Components of the pre-dialysis programme include: attempts to delay renal failure progression through control of hypertension and hyperglycaemia; patient education regarding chronic renal insufficiency, dialysis modalities, and dietary interventions; correction of metabolic abnormalities; insertion of permanent dialysis access; and timely outpatient dialysis initiation. Hospitalization for dialysis initiation is not required at this centre and would therefore be considered a failure event according to the objectives of the pre-dialysis clinic, as well as this study protocol. Patients are allowed to choose dialysis modality according to personal preference, where medical considerations permit. The presence of a universally funded Canadian health care system would suggest that explicit economic factors should not influence referral pattern in the present study. Eligible patients were identified by manual chart review of hospital and office records. Previous kidney transplant recipients were excluded. A total of 362 pre-dialysis cohort patients were identified using the above criteria.

Independent variables
Baseline demographic, clinical, and biochemical data were recorded at the time of initial assessment by the nephrologist and later extracted from hospital records. Demographic data included the date of initial referral to the nephrologist, patient birthdate, and gender. Baseline clinical features included history of diabetes (yes/no), type of diabetes (non-diabetic, type I, type II), systolic and diastolic blood pressures, mean arterial pressure, number of prescribed anti-hypertensive medications, use of ACE inhibitors (yes/no), history of myocardial infarction (yes/no), congestive heart failure (yes/no), angina (yes/no), stroke (yes/no), and peripheral vascular disease (yes/no). Where there was a difference between supine and standing blood pressure values, the higher value was selected. Congestive heart failure was defined as any previous or present history of dyspnoea, orthopnoea, or paroxysmal nocturnal dyspnoea due to cardiac aetiologies and not solely attributable to renal failure and volume expansion alone. Echocardiographic documentation of systolic dysfunction was not required. Angina was defined as any previous or present history of typical chest discomfort (possibly but not necessarily requiring anti-anginal therapy) or any past history of coronary artery angioplasty or bypass surgery. Stroke was defined clinically and did not require radiographic confirmation. Peripheral vascular disease was defined as either a typical history of exertional leg discomfort or previous history of vascular angioplasty or bypass surgery. Baseline biochemical data included serum haemoglobin level and creatinine level recorded at the time of initial assessment by the nephrologist, and baseline serum albumin level recorded at the time of initial assessment or first visit to the pre-dialysis clinic.

Dependent variables
Primary outcome was first non-elective hospitalization occurring after the date of initial assessment by the nephrologist. Admission date and primary diagnosis for first non-elective hospitalization were identified using medical records and computer databases of all tertiary care and community-based hospitals located in south-eastern Ontario. Hospitalization for the purpose of emergent or unexpected dialysis initiation was considered an endpoint. However, hospitalization for radiographic tests and minor surgical procedures (such as insertion of haemodialysis fistulae and peritoneal dialysis catheters) was considered elective and not included as a primary endpoint. Cardiovascular causes for hospitalization included myocardial infarction, unstable angina, arrhythmia, congestive heart failure, and stroke. Hospitalization for cardiovascular reasons alone, pre-dialysis death, in-patient dialysis initiation, and dialysis initiation requiring venous catheter insertion were secondary outcomes. The vital status of all patients at the time of study conclusion (1 January 1998) was confirmed by review of hospital charts and office files. Date of death was obtained by review of hospital charts and out-patient office records.

Statistical analysis
The date of initial assessment by the nephrologist—rather than the date of first pre-dialysis clinic visit—was chosen as the point of study inception in order to minimize lead-time bias. Survival estimates for primary and secondary end-points were estimated using the product limit method of Kaplan and Meier. In this context, ‘survival’ refers to failure-free survival, where failures were the dates of all-cause hospitalization, hospitalization for non-cardiovascular reasons, hospitalization due to cardiovascular reasons alone, and death. Statistically significant relationships between baseline explanatory variables and primary outcome were identified by the log-rank test (P<0.05). For the purpose of generating survival estimates, continuous variables were categorized using clinically relevant cut-points chosen a priori: age <=65 years or >65 years; systolic blood pressure <=140 mmHg or >140 mmHg; diastolic blood pressure <=90 mmHg or >90 mmHg; mean arterial pressure <=100 mmHg or >100 mmHg; haemoglobin <=9.5 g/dl or >9.5 g/dl; albumin <=3.5 g/dl or >3.5 g/dl; and serum creatinine <=300 µmol/l or >300 µmol/l. Dialysis initiation, patient relocation or loss to follow-up, and study conclusion were censored events. Dummy variables were created in the case of categorical independent variables with more than one stratum. Statistically significant relationships between baseline explanatory variables and primary outcome were identified using univariate and multivariate Cox proportional hazard models. Age, blood pressure, haemoglobin, and albumin levels were treated as continuous variables in the Cox models. All explanatory variables were entered into the multivariate analysis simultaneously. Independent predictors were identified by a backward selection process using the maximum likelihood method (P<0.05). Where multivariate analysis selected a dummy variable as an independent predictor of outcome, all dummy variables for that predictor were included in the final model. Serum creatinine level at the time of first assessment (creatinine <=300 µmol/l or >300 µmol/l) was included in the final multivariate models in order to adjust for baseline renal function. Ties were handled by the Breslow method.

Parameter estimates, P values, risk ratios, and corresponding 95% confidence intervals were calculated. The final multivariate model was examined for possible interactions and adherence to the linear assumptions of the Cox model. Simple comparisons between explanatory variables according baseline renal function (creatinine <=300 µmol/l or >300 µmol/l) were made by {chi}2 analysis. Statistical analyses were performed using the SAS statistical software program for personal computers.

The study protocol was approved by the Queen's University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board. Patient care was not affected by this retrospective study. Standard safeguards concerning patient confidentiality were respected.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Baseline characteristics of the pre-dialysis cohort are shown in Table 1Go. Over 50% of patients were over 65 years of age at the time of initial referral. Diabetes was identified in 43% of pre-dialysis patients. Over 75% of patients had a systolic blood pressure greater than 140 mmHg at the time of initial assessment, with over 25% of patients having a diastolic pressure greater than 90 mmHg. A positive history of any cardiovascular disorder was seen in 46% of patients. A history of myocardial infarction, congestive heart failure, angina, stroke, and peripheral vascular disease was identified in 10.5, 15.2, 25.4, 11.1, and 14.1% of patients respectively. Over 45% of patients had a baseline albumin level below 3.5 g/dl. A haemoglobin value less than 9.5 g/dl was reported in 16.3% of patients at the time of first assessment.


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Table 1. Frequency distribution of explanatory variables among a cohort of pre-dialysis patients and comparison of explanatory variables between low and high baseline creatinine levels using chi-square analysis

 
Comparisons between explanatory variables according to baseline creatinine level are shown in Table 1Go. Patients with less advanced renal insufficiency at the time of initial assessment were younger, more frequently prescribed ACE inhibitors, and demonstrated higher mean baseline haemoglobin values. Diabetes was more commonly reported among patients with less advanced baseline renal insufficiency, while a history of congestive heart failure was more commonly identified among patients with more advanced disease. Serum albumin levels were similar between the two groups.

Of a total 362 pre-dialysis patients, 201 individuals (55.5%) progressed to dialysis dependency. Two hundred and eight patients required hospitalization on or before date of dialysis initiation. Thirty-seven patients died prior to dialysis initiation. Four patients were lost to follow-up prior to dialysis initiation due to re-location. Among the 201 patients starting dialysis, 91 patients (45.3%) received their first dialysis therapy in hospital, while 68 patients (33.8%) required insertion of a central venous catheter. Among the group of patients who started dialysis, 25% were referred within 4 months of dialysis initiation.

One hundred and sixty-four patients (45.3%) had a baseline creatinine already above 300 µmol/l at the time of initial referral. Among those patients with a creatinine below 300 µmol/l at initial assessment, 32.8% had been prescribed an ACE inhibitor by their family physician. Among diabetic patients with a baseline creatinine level below 300 µmol/l, 43.8% had already been prescribed an ACE inhibitor prior to initial nephrological assessment.

Primary outcome was first non-elective pre-dialysis hospital admission. Hospitalization-free ‘survival’ estimates are shown in Table 2Go. Overall hospital-free median survival for the entire cohort was 18.9 months. Patients with baseline creatinine greater than 300 µmol/l at initial assessment had a shorter hospital-free median survival compared to patients with a baseline creatinine below 300 µmol/l (11.2 vs 23.9 months P=0.0006). Pre-dialysis patients over age 65 at the time of initial assessment demonstrated shorter failure-free median survival compared to individuals less than 65 years (11.5 vs 31.1 months P=0.0001). Patients whose baseline systolic blood pressure was greater than 140 mmHg demonstrated a median survival of only 15.8 months compared to a median survival of 30.5 months for those patients with lower baseline systolic blood pressures (P=0.0443). Positive history of myocardial infarction, congestive heart failure, angina, and peripheral vascular disease were associated with statistically significant reductions in hospital-free median survival estimates. Hospital-free survival estimates were improved among pre-dialysis patients whose renal insufficiency was attributed to causes other than diabetic nephropathy, nephrosclerosis, or renal artery stenosis. Patients with low baseline haemoglobin and albumin levels had shorter median survival estimates compared to individuals with higher baseline values.


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Table 2. Median hospital-free survival estimates among a retrospective cohort of pre-dialysis patients

 
The relationship between independent explanatory variables and the primary outcome, first non-elective hospitalization, was examined using univariate and multivariate Cox proportional hazard models. Univariate analysis identified advanced age, number of prescribed anti-hypertensive medications, history of myocardial infarction, congestive heart failure, angina, peripheral vascular disease, renal insufficiency secondary to nephrosclerosis and renal artery stenosis, lower baseline haemoglobin concentration and baseline creatinine greater than 300 µmol/l as significant predictors of all-cause hospitalization (Table 3Go). Gender, diabetes, diastolic blood pressure, mean arterial pressure, history of stroke, and baseline albumin level did not predict hospitalization. Multivariate analysis selected advanced age, history of myocardial infarction, angina, peripheral vascular disease, and lower baseline haemoglobin level as independent predictors of pre-dialysis hospitalization. Surprisingly, baseline serum creatinine level was not an independent predictor of hospitalization. Serum creatinine level was subsequently forced into the multivariate model in order to adjust for baseline renal function. After adjustment for baseline renal function, history of myocardial infarction was no longer a statistically significant independent predictor of hospitalization (Table 4Go). The final multivariate model was examined for possible interaction between independent explanatory variables as well as adherence to model assumptions. No statistically significant interactions between independent variables were identified in the final multivariate model.


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Table 3. Univariate analysis identifying predictors of hospitalization among a cohort of pre-dialysis patients

 

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Table 4. Multivariate analysis identifying independent predictors of hospitalization among a retrospective cohort of pre-dialysis patients

 
Further analyses were performed in order to identify independent predictors of first non-elective hospitalization for non-cardiovascular reasons. One hundred and thirty-eight admissions were for non-cardiovascular reasons. Non-cardiovascular reasons for hospitalization included infection (8.4%), gastrointestinal aetiologies (6.4%), previously unrecognized cancer (4.5%), dialysis initiation (22.3%), and other miscellaneous causes (23.8%). Univariate analysis identified advanced age (RR 1.016, CI 1.004–1.028), baseline haemoglobin level (RR 0.982, CI 0.974–0.990), and albumin level (RR 0.965, CI 0.939–0.990), and initial serum creatinine above 300 µmol/l (RR 1.791, CI 1.261–2.544) as predictors of non-cardiovascular hospitalization. Not surprisingly, positive history of cardiovascular disorders did not predict hospitalization for non-cardiovascular reasons. Multivariate analysis identified renal insufficiency due to nephrosclerosis (RR 1.640, CI 1.003–2.682), lower baseline haemoglobin level (RR 0.987, CI 0.978–0.996) and lower baseline albumin level (RR 0.957, CI 0.930–0.986), but not baseline renal function, as independent predictors of non-cardiovascular hospitalization.

Predictors of hospitalization for cardiovascular reasons alone were examined by univariate and multivariate analysis. About one-third of hospital admissions (70 patients) were due to cardiovascular reasons. Univariate analysis identified advanced age (RR 1.035, CI 1.017–1.054), number of prescribed anti-hypertensive medications (RR 1.284 CI 1.041–1.584), history of myocardial infarction (RR 4.284, CI 2.511–7.309), congestive heart failure (RR 3.952, CI 2.348–6.665), angina (RR 4.998, CI 3.090–8.085), stroke (RR 2.114, CI 1.134–3.941), and peripheral vascular disease (RR 2.718, CI 1.585–4.659) as predictors of hospitalization for cardiovascular reasons. Multivariate analysis selected advanced age (RR 1.023 CI 1.003–1.043), history of myocardial infarction (RR 2.157, CI 1.194–3.898), congestive heart failure (RR 2.143, CI 1.224–3.752) and angina (RR 3.061, CI 1.796–5.215), but not baseline creatinine level, as independent predictors of hospitalization for cardiovascular reasons.

Only 37 deaths occurred prior to dialysis initiation. Univariate analysis identified advanced age (RR 1.116, CI 1.074–1.159), renal failure due to nephrosclerosis (RR 3.542, CI 1.846–6.796), diastolic hypertension (RR 0.972, CI 0.948–0.997), history of myocardial infarction (RR 3.123 CI 1.461–6.674), congestive heart failure (RR 3.095, CI 1.501–6.383), angina (RR 3.692, CI 1.953–6.979), lower baseline haemoglobin level (RR 0.972, CI 0.957–0.988), and baseline creatinine level greater than 300 µmol/l (RR 2.591, CI 1.354–4.961) as predictors of pre-dialysis death. Multivariate analysis selected advanced age (RR 1.107, CI 1.066–1.151), female gender (RR 2.012, CI 1.056–3.834), and history of angina (RR 2.611, CI 1.377–4.951), as independent predictors of death prior to dialysis initiation. Female gender remained an independent predictor of pre-dialysis death even after adjustment for baseline renal insufficiency.



   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Risk factors predicting either increased morbidity or mortality among dialysis patients have been well described by earlier research, but are poorly defined among the pre-dialysis population. The present study has identified baseline demographic, clinical, and biochemical predictors of adverse outcomes among a retrospective cohort of pre-dialysis patients. Univariate analysis indicated that advanced age, number of prescribed anti-hypertensive medications, a history of myocardial infarction, congestive heart failure, angina, peripheral vascular disease, renal failure secondary to nephrosclerosis or renal artery stenosis, anaemia, and baseline renal function at time of assessment were predictors of hospitalization. Independent predictors of first non-elective hospitalization, adjusted for baseline renal function, were advanced age, history of angina and peripheral vascular disease, and baseline haemoglobin concentration. Hospital-free ‘survival’ estimates indicate that the influence of these predictors on patient outcome is both clinically and statistically significant. These results suggest that many of the well-recognized predictors of adverse outcome among dialysis patients are also useful predictors of pre-dialysis patient morbidity.

Subsequent analyses were performed in order to identify predictors of secondary outcomes (hospitalization for non-cardiovascular reasons, hospitalization for cardiovascular reasons alone, and death). Renal insufficiency secondary to nephrosclerosis and baseline haemoglobin and albumin concentrations were independent predictors of non-cardiovascular hospitalization. The exclusion of cardiovascular disorders as independent predictors of non-cardiovascular hospitalization, as well as the inclusion of hypoalbuminaemia as an independent predictor of non-cardiovascular hospitalization, is not unexpected.

Advanced age, history of myocardial infarction, congestive heart failure, and angina were independent predictors of cardiovascular hospitalization. Although multivariate analysis identified advanced age, female gender, and history of angina as independent predictors of pre-dialysis death, the small number of outcome events and diminished statistical power suggest cautious interpretation of this result.

A baseline haemoglobin concentration less than 9.5 g/dl was identified in over 15% of pre-dialysis patients but was understandably more common among patients with more advanced renal insufficiency at the time of initial assessment. Adjusted for baseline creatinine level, multivariate analysis suggested that low baseline haemoglobin concentration was an independent predictor of all-cause hospitalization. Previous research has demonstrated that pre-dialysis erythropoietin administration can effectively improve haemoglobin concentration [25–31]. The present study therefore suggests that early correction of anaemia using erythropoietin and iron supplements can lead to improved patient-oriented outcomes. Theoretically, the inclusion of patients who received erythropoietin prior to dialysis initiation may have potentially underestimated the association between higher baseline haemoglobin concentration and improved clinical outcome (due to the possibility of a type II statistical error). Nonetheless, only 27 patients actually received erythropoietin 6 months or more prior to dialysis initiation, and the results of a repeat analysis after exclusion of these patients were unchanged.

Earlier research among dialysis patients has demonstrated that anaemia is associated with left ventricular hypertrophy and left ventricular dilatation, which themselves are predictors of adverse outcome [32]. Although the mechanisms by which anaemia influences pre-dialysis outcomes are unclear, an association between pre-dialysis anaemia and left ventricular dysfunction should be considered. Nonetheless, the effect of anaemia upon pre-dialysis outcome is probably multi-factorial. Notably, the present study has demonstrated that pre-dialysis anaemia, adjusted for baseline renal function, was an independent predictor of both all-cause hospitalization and hospitalization for non-cardiovascular reasons.

Earlier research among dialysis patients has also demonstrated that attempts to achieve regression of established left ventricular hypertrophy and dilatation through correction of anaemia are usually unsuccessful, suggesting that earlier correction of anaemia is necessary in order to prevent left ventricular dysfunction [33]. This explanation may account for the finding that a history of congestive heart failure, but not anaemia, was as an independent predictor of hospitalization for cardiovascular reasons. The inability of the present study to identify anaemia as a predictor of cardiovascular admission might also be attributed to diagnostic misclassification. Due to the limitations of a retrospective study design, a significant number of admitting diagnoses could only be classified as ‘dialysis initiation’. In some cases, emergent dialysis initiation requiring hospitalization may have been precipitated by worsening pulmonary oedema and congestive heart failure. The use of a single baseline haemoglobin concentration in model construction, rather than repeated time-dependent covariates, is another limitation of the retrospective study design. A prospective study using a repeated-measures design and time-dependent covariates is clearly warranted.

Neither univariate nor multivariate analysis suggested an association between diabetes and adverse clinical outcome. Failure to include duration of diabetic history as an independent variable may be one possible explanation accounting for this negative finding. The inability to demonstrate an association between diabetes and clinical outcome contradicts the conclusions of some investigators [15,17,18,22,25], but not all [14,20]. It has been suggested that the failure of the Canadian Hemodialysis Morbidity Study [14] to demonstrate a significant association between diabetes and adverse clinical outcome was due to a short follow-up period in which the long-term complications of diabetes could not be realized. The longer follow-up period of the present study would preclude a similar explanation. It is possible that the present study failed to detect a statistically significant association between diabetic history and clinical outcome because of small sample size and the possibility of a type II statistical error. However, using a standard P value of 0.05 and power of 0.8, the present sample size would be at least capable of detecting a minimum hazard ratio of 1.4, should such an association exist.

Hypertension was remarkably common even though patients had already been assessed by primary care physicians prior to nephrology referral. Although over 75% of patients had a systolic blood pressure greater than 140 mmHg at the time of initial referral, fewer than 50% of patients were prescribed more than one anti-hypertensive agent, suggesting that the importance of aggressive control of hypertension in the setting of chronic renal failure has been undervalued by some primary care physicians. Not surprisingly, ACE inhibitors were less frequently prescribed among patients with higher serum creatinine levels at the time of initial assessment. However, only 44% of diabetic patients with a baseline creatinine level below 300 µmol/l were already prescribed an ACE inhibitor at the time of nephrology referral, indicating that many primary care physicians appear unaware that ACE inhibitors can retard the progression of early diabetic nephropathy [34]. Multivariate analysis demonstrated that blood pressure was not an independent predictor of outcome. This finding is consistent with the conclusions of other investigators who have also reported no significant independent relationship between baseline hypertension and clinical outcome among dialysis patients [19,35]. However, it can be argued that a single baseline blood pressure measurement is an insensitive marker of long-term blood pressure control. The absence of a statistically significant association between blood pressure and hospitalization or death should not diminish the importance of pre-dialysis blood pressure control, since research has convincingly demonstrated that aggressive control of hypertension can retard the progression of chronic renal failure and delay dialysis initiation [36].

Hypoalbuminaemia, a surrogate marker of nutritional inadequacy, is an independent predictor of both hospitalization and death among haemodialysis and peritoneal dialysis patients [14,22–25]. In the present study, baseline albumin level was an independent predictor of hospitalization for non-cardiovascular reasons. Baseline albumin level did not predict all-cause hospitalization, hospitalization for cardiovascular reasons alone, or death, perhaps due to the limitations of small sample size. It is also likely that baseline serum albumin levels in this population are insensitive markers of long-term nutritional adequacy, particularly among patients in whom advancing azotaemia can lead to worsening nutritional intake immediately prior to dialysis initiation. Moreover, causes of hypoalbuminaemia other than nutritional inadequacy could confound the relationship between surrogate markers of nutritional adequacy and clinical outcome. The presence of comparable baseline albumin levels between patients with an initial serum creatinine concentration less than 300 µmol/l and those with an initial creatinine level greater than 300 µmol/l would support these explanations.

It is disappointing that 45% of patients who started dialysis received their first dialysis therapy in hospital despite the fact that these patients were actively followed in a pre-dialysis clinic. In comparison, other pre-dialysis programmes have reported that urgent in-patient dialysis starts occur in 13–56% of pre-dialysis patients [10]. Ironically, the pre-dialysis programme in Toronto, Canada reported that due to a lack of haemodialysis resources, the proportion of elective out-patient dialysis starts actually decreased over time. Similar limitations in dialysis resources have not affected our centre, or the results of the present study. However, in the present study, 34% of patients who started dialysis required insertion of a central venous catheter because a permanent access had not been created. By comparison, only 14% of pre-dialysis patients in the Toronto programme required temporary dialysis access.

Surprisingly, over 60% of cohort patients reached an adverse clinical end-point—either hospitalization or death—prior to dialysis initiation, underscoring the significant morbidity and mortality incurred by pre-dialysis patients. Not unexpectedly, cardiovascular causes accounted for 33.7% of pre-dialysis hospital admissions. Whether the high cardiovascular risk among patients with chronic renal failure is related to a high prevalence of established risk factors or is the result of other factors specific to renal disease is a source of ongoing debate [37].

The present study is notable for its complete patient follow-up and well-defined catchment area in which referral patterns are extremely centralized. However, by using only baseline clinical and biochemical explanatory variables, the prognostic importance of risk factors that change over time (such as blood pressure, serum albumin, and haemoglobin concentrations) has probably been underestimated by a retrospective study design. Further investigation using a prospective repeated-measures design and time-dependent covariates is necessary. While failure to detect statistically significant associations between some explanatory variables and clinical outcome might be explained by small sample size, this study is capable of detecting a minimum hazard ratio of at least 1.4 using a standard power of 0.8.

Notwithstanding these limitations, the present study has demonstrated that several important predictors of adverse outcomes in the dialysis population are also operative in pre-dialysis patients. Advanced age, co-morbid cardiovascular disease and anaemia are independent predictors of non-elective hospitalization among pre-dialysis patients. These results underscore the importance of early referral to a pre-dialysis programme in order to allow sufficient time to correct modifiable risks. For example, the results of this study support the suggestion that early pre-dialysis erythropoietin administration may improve patient outcomes. Moreover, identifying risk factors that predict imminent hospitalization or death can signal the need to initiate dialysis earlier among high-risk individuals, perhaps avoiding the need for hospitalization altogether [38]. Further investigation is necessary in order to determine the extent to which modification of pre-dialysis risk factors can alter patient morbidity and mortality.



   Acknowledgments
 
The authors gratefully acknowledge the assistance of the medical record staff of the following hospitals: Kingston General Hospital, Hotel Dieu Hospital (Kingston), Lennox and Addington County General Hospital (Napanee), St Vincent de Paul Hospital (Brockville), Brockville General Hospital, Belleville General Hospital, Trenton Memorial Hospital, Prince Edward County Memorial Hospital (Picton), Smith Falls Rideau Regional Hospital, and Smith Falls–Perth Hospital. The authors are also grateful to Dr Joseph Pater and Dr Michael Singer for review of the study protocol and manuscript.



   Notes
 
Correspondence and offprint requests to: David C. Holland, Suite 2058, Etherington Hall, Queen's University, Kingston, Ontario, Canada K7L 3N6. Back



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

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Received for publication: 13. 4.99
Revision received 10.12.99.