NDT Advance Access originally published online on November 7, 2007
Nephrology Dialysis Transplantation 2008 23(7):2228-2234; doi:10.1093/ndt/gfm591
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The impact of acute kidney injury on short-term survival in an Eastern European population with stroke
1 Dialysis and Transplantation Center, Dr C.I. Parhon University Hospital, Ia
i
2 Nephrology Department
3 Neurology Department, Emergency Clinical Hospital, Timisoara, Romania
Correspondence to: Adrian Covic, MD, PhD, Professor of Nephrology, C.I. Parhon University Hospital, Blvd. Carol 1st No. 50, Iasi 700503, Romania. E-mail: acovic{at}xnet.ro
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Abstract |
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Background. Stroke is one of the leading causes of death and of serious disability with significant impact on patients’ long-term survival. The short-term evolution following stroke can associate acute kidney injury (AKI) as a possible complication, frequently overlooked and underestimated in clinical trials. We aimed to describe in an East European cohort (i) the incidence of AKI and its risk factors; (ii) the 30-day mortality and its risk factors and (iii) the relationship between mortality, pre-existent renal function and subsequent AKI.
Methods. A total of 1090 consecutive cases hospitalized—during a 12-month period—with a CT-confirmed diagnosis of stroke, from a distinct administrative region were included. Demographic details, comorbidities, laboratory and outcome data were retrieved from the electronic hospital database. All patients included in the study were followed for 30 days or until death.
Results. The mean age of this population was 66.1 ± 11.5 years, 49.3% were males, mean glomerular filtration rate (GFR) 68.9 ± 22.6 ml/min/1.73 m2. The 30-day mortality rate was 17.2%. One hundred and fifty-eight patients presented with haemorrhagic stroke and 932 patients had ischaemic stroke. Stroke mortality was—14% for ischaemic stroke and almost twice as high for haemorrhagic stroke—36.3%.
One hundred fifty-eight (14.5%) patients were classified as developing AKI. The AKI patients were older, had a higher baseline serum creatinine, lower GFR, higher serum glucose, higher prevalence of chronic heart failure and ischaemic heart disease, were more likely to have suffered a haemorrhagic stroke, and had a significantly higher 30-day mortality rate (43.1 vs 12.8%) (P < 0.05 for all). Independent predictors for AKI development in the logistic regression analysis were age, GFR, presence of comorbidities (ischaemic heart disease and chronic heart failure) and type of stroke (Cox and Snell R2 0.244; Nagelkerke R2 0.431; P < 0.05).
In our study, we demonstrated that the occurrence of AKI is not a rare finding in stroke patients. This is the first study to report the incidence of AKI in a distinct geographic population base, in patients with stroke. Baseline renal function emerged as both a significant independent marker for short-term survival after an acute stroke (even after adjustment for baseline comorbidities) and as a risk factor for subsequent AKI.
Keywords: acute kidney injury; renal function; stroke; survival
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Introduction |
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Stroke is the third leading cause of death in the United States and a leading cause of serious disability. The immediate evolution after stroke can significantly impact on patients’ long-term survival—as most recently demonstrated by the ACCESS study (Acute Candesartan Cilexetil Therapy in Stroke Survivors) [1]. Mortality after stroke has been described relative to various time intervals: in-hospital [2]/short-term (30 days), intermediate (3 months) [3], and long-term (usually >1 year) mortality [4]. Additionally, stroke is a highly heterogeneous disorder with distinct subtypes, each presenting with different incidence, risk factors and outcomes [5]. In the largest single-cohort study to date (>40 000 patients), Collins et al. [6] reported that short-term, intermediate and long-term mortality rates were: 7.4, 11.4 and 19.1% (respectively) for ischaemic stroke and 18.8, 24.6 and 31.8%, for haemorrhagic stroke. Currently demonstrated survival determinants for ischaemic stroke include age (over 65 years), presence of chronic heart failure (CHF), diabetes, history of peripheral vascular disease, various abnormalities on the baseline electrocardiogram (ECG) and the pre-stroke Rankin handicap score [6–8]. For haemorrhagic stroke, determinants of survival appear to be the following: older age (over 75 years), the initial Glasgow coma score, various abnormalities on the cerebral computer tomography scan, presence of CHF, malignancy, chronic obstructive pulmonary disease (COPD), chronic liver disease and use of anti-thrombotic drugs [2,6,9].
Only recently, several reports have indicated chronic kidney disease (CKD) to be an additional independent and powerful predictor for stroke outcome. In a cohort study of 2042 patients admitted for stroke, renal indexes (serum urea and creatinine, creatinine clearance at admission) remained significant predictors of mortality even after adjustment for a variety of ‘classical’ risk factors (see above): age, neurological presentation and comorbidities (CHF, ischaemic heart disease—(IHD), hypertension and smoking) [10]. In a typical middle-aged, community-based western population, the atherosclerosis risk in communities (ARIC) population, CKD was associated with an increase in stroke risk after adjustment for other factors [hazards ratio (HR) 1.81; 95% confidence interval (CI) = 1.26–2.02] [11]. Similarly, in a prospective study of a community-based Japanese population (The Hisayama Study) [12], CKD emerged as an independent predictor of ischaemic stroke in women. For the moment, no data exists on stroke and CKD in Eastern European populations (geographic area with distinct genetic, demographic, socioeconomic and comorbid population characteristics, that impact on disease epidemiology and outcome [13].
In the immediate period following a stroke, acute kidney injury (AKI) may develop as a possible complication. However, this association is frequently overlooked and underestimated in clinical trials. AKI presence can be explained by the particular characteristics of the stroke-prone population: elderly individuals (typically over 60 years), associating multiple cardiovascular comorbidities frequently treated with multiple drug associations, and usually with impaired renal function [14,15]. There are still few studies to describe the incidence of AKI (as defined by the RIFLE criteria) in a distinct geographic base [16], particularly in selected populations and to our knowledge none in a single cohort of patients with acute stroke.
In our study, we aimed to describe for the first time in an East European cohort, including all patients with stroke from a distinct region (i) the incidence of AKI and its risk factors; (ii) the 30 days mortality and its risk factors and (iii) the relationship between mortality, pre-existent renal function and subsequent AKI.
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Study population
We conducted a retrospective cohort study in the Timisoara County region of Romania (population in 2004—659 512). The study included all consecutive cases hospitalized—during a 12-month period between 1 January 2005 and 1 January 2006—with a CT-confirmed diagnosis of stroke, at the Neurology Department, Timisoara County Hospital (a tertiary care academic medical centre treating all cases of stroke for the entire administrative region). A total of 1090 patients were eligible for the study.
Data collection and follow-up
Data including demographic details, comorbidities, laboratory data and outcome were retrieved from the electronic hospital database. After merging data from the different sources, we performed automated and manual data verification. All patients included in the study were followed for 30 days or at least until death.
Inclusion criteria and stroke definition
In this study, we included all patients that presented in the Emergency Department with first ever stroke. Exclusion criteria were diagnosis of stroke not confirmed by CT scan and transient ischaemic attacks (TIA). The study was approved by the Hospital Ethical Committee.
Stroke was defined as rapidly developing clinical signs of focal disturbance of cerebral function, lasting more than 24 h or leading to death with no apparent cause other than vascular origin [17]. The information on each stroke case was extracted from the medical records of each patient and from CT scan. Ascertainment of each case was based on the medical history, clinical neurological examination by a neurologist, CT scan to confirm the diagnosis and to determine the type of stroke. As per hospital protocol, in all cases considered necessary, an izo-osmolar, non-nephrotoxic radiocontrast media (iodixanol - Visipaque®) was used.
Definition of acute kidney injury
In this study, patient baseline creatinine was considered to be the value recorded at admission. Subsequently, serum creatinine was measured daily. All patients with a rise in serum creatinine value or fall in GFR (as per RIFLE, whichever was greater) were included in the AKI group and were assigned to a category in the RIFLE classification [17]. Since this study relies on an electronic database without a mandatory registration of the urinary output, we did not use the urinary output criteria for the inclusion in the RIFLE classes. GFR was estimated using the abbreviated modification of diet in renal disease (MDRD) equation. Since a small number of patients had documentation of the renal function previous to admission and met the criteria for acute or chronic renal failure (defined by the ADQI group as an increase in serum creatinine to 4 mg/dl), we included them in the group with acute renal injury.
For more accurate analyses of the influence of baseline renal function on AKI development, we also used in our analysis the NKF–KDOQI classification of CKD stages [18].
Statistical analysis
All values are expressed as mean ± SD unless stated otherwise in the text. Continuous variables were compared using the t-test. Chi-squared test was used to test differences in frequency distributions. All potential (physiologically meaningful) determinants of AKI and type of stroke were investigated in a binary logistic regression model. A P < 0.05 for the final model was considered as statistically significant. The goodness-of-fit of the logistic model was assessed by the Hosmer–Lemeshow test and colinearity checked using the colinearity matrix. Factors highly correlated were excluded from the analysis. Survival curves were estimated by the Kaplan–Meier product-limit method and compared by the Mantel (log-rank) test. Prognostic factors of survival were identified by the use of the Cox proportional hazards regression model. Variables were considered to be prognostic when they were found to be statistically significant (P < 0.05) in the Cox proportional hazards regression model of over all survival. The adjusted relative risk of experiencing an outcome event during follow-up was estimated as the odds ratio (OR). Adjusted OR was calculated as the anti-logarithm of the β-coefficient of the independent variable in the Cox survival analysis. Data were analysed using the SSPS 12.0 for Windows software (SPSS® Inc. Chicago IL, USA).
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Results |
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The study group included 1090 consecutive patients treated for CT-confirmed stroke, during 2005–2006, resulting in an annual incidence of 1653 cases p.m.p. The demographic characteristics of the entire group are shown in Table 1. The mean age of this population was 66.1 ± 11.5 years, 49.3% were males and the mean GFR was 68.9 ± 22.6 ml/ min/1.73 m2. In our group, 38.9% of the patients were on ACE inhibitors and 19.7% were on angiotensin (AT)-II receptor blockers (Table 1); no patient received AINS. The 30 day mortality rate was 17.2%.
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One hundred fifty-eight patients presented with a haemorrhagic stroke and 932 patients had an ischaemic stroke. Patients with haemorrhagic stroke were younger, had higher systolic blood pressure (SBP), developed more frequently AKI (see subsequently) and had higher 30-day mortality rates, compared with patients with ischaemic stroke.
AKI vs non – AKI patients
One hundred and fifty-eight (14.5%) patients were classified as developing AKI after hospital admission, as defined by the RIFLE criteria. Therefore, the combination stroke—AKI developed with an incidence of 240 cases p.m.p/year. The AKI patients were older, had a higher initial serum creatinine and lower GFR, and had higher serum glucose, compared with the rest of the study group (P < 0.05 for all) (Table 2). There was a higher prevalence of IHD, CHF and diabetes mellitus in the subgroup of AKI patients. Also, patients who developed AKI were more likely to have suffered a haemorrhagic stroke and had a significantly higher 30-day mortality rate (Table 2). Cardiac comorbidities and diabetes prevalence were higher in the failure (F) category compared to the injury (I) and the risk (R) categories of the AKI group (Table 3).
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All clinically significant determinants of AKI (age, gender, blood pressure, serum creatinine at admission and GFR, serum cholesterol and triglycerides, serum glucose, presence of comorbidities and anti hypertensive treatment) were introduced in a logistic regression model. When all factors were introduced in the model, the goodness-of-fit analysis by the Hosmer–Lemeshow test indicated a poor adequacy of the model (P = 0.025) in predicting the occurrence of AKI. The correlation matrix showed a high correlation indexes between indicators of renal function (serum creatinine and GFR), blood pressure (SBP and DBP), lipid abnormalities (serum cholesterol and triglycerides), respectively glycaemic status (diabetes and serum glucose). In the next step, serum creatinine, serum cholesterol, diabetes status and blood pressure were excluded from the analysis. The goodness-of-fit analysis by the Hosmer–Lemeshow test indicated improved adequacy of the model in predicting the occurrence of AKI (P = 0.554). Independent predictors for AKI development in the logistic regression analysis were age, renal function at admission expressed by the GFR, presence of comorbidities (IHD and CHF) and type of stroke (Cox and Snell R2 0.244; Nagelkerke R2 0.431; P < 0.05) (Table 4). In the logistic analysis, the use of angiotensin-converting enzyme (ACE) inhibitors was only marginally correlated to AKI presence.
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To better assess the influence of the baseline renal function, we divided the group by baseline GFR categories: >90, 60–90, 30–60 and <30 ml/min. The mean age increased across GFR categories (higher for the lower GFR, P < 0.05 for trend). Patients with GFR 30–60 and <30 ml/min had a significantly higher prevalence of cardiac comorbidities (IHD and CHF) and a significantly higher proportion of cases developed AKI. The distribution of stroke types was similar across all GFR subgroups. The 30-day mortality was similar for patients with a baseline GFR >90, 60–90, 30–60, but was significantly higher in the lowest GFR subgroup.
Patient 30-day survival
During the 30 days following the acute event—stroke, 187 deaths were recorded (17.2% of the entire population). Time to death was significantly shorter in the F category of the AKI group (Table 3). In patients with AKI, mortality was 30.4% in the R group, 72.7% in the I group and 90% in the F group (Table 3).
Kaplan–Meier survival curves showed significant survival differences when divided by presence of AKI (worse survival in the AKI group—Figure 1), by RIFLE category (worse survival in the failure group—Figure 2) and by baseline GFR category (worse survival in the <30 ml/ min group—Figure 3). All clinically significant parameters for survival were introduced in a Cox survival model (Table 5). In the first step, age, gender, GFR category, blood pressure, serum cholesterol and serum glucose at admission were analysed. Baseline GFR was associated with an OR of 1.51 (95% CI = 1.19–1.93). When adjusted for the presence of comorbidities (CHF, IHD and diabetes) and anti-hypertensive treatment, in the second step, the only factor retained in this second model was CHF presence at admission, while the impact of baseline GFR diminished (OR = 1.41, 95% CI = 1.1–1.81). In the third step, presence of AKI and type of stroke were added to the model; the importance of baseline GFR as a predictor for mortality was no longer significant (OR = 1.05; 95% CI = 0.8–1.37) and the most important predictor for survival became the type of stroke (OR = 2.22; 95% CI = 1.54–3.21).
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Discussions |
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This is the first study to report the incidence of AKI, in a distinct geographic population base, in patients with stroke. Our study shows that the occurrence of AKI, defined by the RIFLE criteria, is not a rare finding in stroke patients: in this 1 year, over 1000 patient cohort, the prevalence of AKI was 14.5% and the incidence of AKI and stroke 240 p.m.p./year. As expected, AKI developed more frequently in older persons, with a higher creatinine/lower GFR at baseline and with pre-existent IHD or CHF. At the same time, the baseline renal function emerged as a significant independent marker for short-term survival after an acute stroke (even after adjustment for baseline comorbidities) and as a risk factor for subsequent AKI.
In our study, the stroke mortality was quite high—14% for ischaemic stroke and almost twice as high for haemorrhagic stroke—36.3%. Comparatively, rates are lower in the study of Collins et al. [6] among American veterans: the adjusted 30 days mortality was 7.4% for ischaemic stroke and 18.8% for haemorrhagic stroke. In the ARIC study the unadjusted 30-day mortality rate for combined incident and recurrent hospitalized ischaemic strokes was 7.6% [18]. Finally, in another American prospective, population-based study—the Northern Manhattan Stroke Study, cumulative mortality risk was 5% [19]. However, in a smaller prospective European study from Spain [20], in patients with first ever stroke, the 30-day cumulative mortality was 16%, similar to ours. All these differences are not explained by differences in age, gender distribution or case mix.
To our knowledge, the incidence of AKI following an acute cerebrovascular event and its prognostic importance has not been systematically investigated in previous studies performed in stroke populations. In populations with different pathologies the incidence of AKI varies quite significantly, from 209 p.m.p./year in a Spanish study [21], 288 p.m.p. in US [22], to 620 pmp in England [23] or even 1811 pmp in Scotland [16]. However, in this case, these important differences are explained by the different populations included/case-mix, as well as by differences in baseline serum creatinine/GFR. In a recent large retrospective study in Medicare beneficiaries (more than 5 403 015 hospital discharges) [24], AKI occurrence was strongly associated with older age, male gender and black race. We confirm literature data: the AKI patients were older, had a higher initial serum creatinine and lower GFR and had higher serum glucose, compared with the rest of the study group. Also, in the AKI group there was a higher prevalence of IHD, CHF and diabetes mellitus.
Most importantly, AKI carries an important negative prognostic message: unadjusted mortality was at least three times higher in patients with vs without AKI (42% compared to 12%, respectively). In the same Medicare population, Xue et al. [24] also demonstrated the survival impact of acute renal failure (ARF): the overall in-hospital death rate was 15.2% for discharges with ARF coded as the principal diagnosis, 32.6% for discharges with ARF as a secondary diagnosis. Death within 90 days after hospital admission was 13.1% in discharges without ARF, 34.5% in discharges with ARF.
Prior investigations have reported that CKD is associated with a higher stroke risk in different populations, particularly in patients with isolated systolic hypertension [25,26]. The negative long-term impact of GFR on survival in stroke populations was also demonstrated: in the Kaplan–Meier analysis, patients with stroke and lower calculated creatinine clearance (GFR < 38.8 mm/min) at admission had significantly worse 5-year survival compared to patients with a creatinine clearance above that limit [10]. In our Cox survival analysis, the baseline GFR remained predictive for 30-day mortality even after introduction in the model of comorbidities and type of stroke. Although baseline GFR predicts subsequent AKI, when AKI is forced in the Cox model, the significance of baseline GFR disappears; since many more factors contribute to the acute deterioration of the renal function, acute renal failure is probably a more comprehensive statistical risk factor.
This study has a number of limitations. First, it is observational, based on a retrospective analysis of electronic medical records data. Second, we had no data related to the presence of atrial fibrillation and other cardiovascular cardio-embolic condition. Moreover, no differentiation was possible between the different subtypes of ischaemic stroke. It is demonstrated in the literature that the localization and extension of ischaemic stroke bears prognostic significance: the total anterior circulation infarction, partial anterior circulation infarction, total posterior circulation infarction and lacunar infarction have different risk factors, with consequences on pathogenesis and prognosis [8].
The strengths of the present study are first, it includes a large population, from a well-defined administrative area, representative for the East European countries; second, all cases had a good documentation of the stroke diagnosis—the ascertainment of stroke was realized in each case by CT imaging in a single Neurology Department. Third, for the first time, AKI is recognized and included as a risk factor in the survival analysis.
In conclusion, our study accurately describes, in a large east European community based population, the incidence of stroke and its subtypes, the incidence of a potential deleterious complications, acute kidney injury and its determinants, and confirms the importance of baseline renal function as a predictor of mortality for stroke patients.
Conflict of interest statement. None declared.
(See related article by H. Schiffl and R. Fischer. Five-year outcomes of severe acute kidney injury requiring renal replacement therapy. Nephrol Dial Transplant 2008; 23: 2235–2241.)
(See related article by S. M. Bagshaw. Short- and long-term survival after acute kidney injury. Nephrol Dial Transplant 2008; 23: 2126– 2128.)
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Accepted in revised form: 2. 8.07
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