J Neurol DOI 10.1007/s00415-015-7840-2
ORIGINAL COMMUNICATION
Renal dysfunction is associated with deep cerebral microbleeds but not white matter hyperintensities in patients with acute intracerebral hemorrhage Mona Laible1 • Solveig Horstmann1 • Markus Mo¨hlenbruch2 • Christian Wegele1 Timolaos Rizos1 • Svenja Schu¨ler3 • Markus Zorn4 • Roland Veltkamp1,5
•
Received: 5 May 2015 / Revised: 29 June 2015 / Accepted: 29 June 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Kidney disease is a risk factor for cerebral microangiopathy and spontaneous intracerebral hemorrhage (ICH). We aimed to determine the association of renal dysfunction (RD) with MRI correlates of different patterns of cerebral microangiopathies including cerebral microbleeds (CMB) and white matter lesions (WML) in patients with ICH. In a prospectively collected, singlecenter cohort of ICH patients, glomerular filtration rate (eGFR) was estimated using the Modification of Diet in Renal Disease equation. We classified the renal function in five categories: category 1 (eGFR C90 mL/min/1.73 m2), category 2 (eGFR 60–89), category 3 (eGFR 30–59), category 4 (eGFR 15–29), and category 5 (eGFR \15) and dichotomized at an eGFR of 60. Number, location, and extent of CMB and WML were measured on MRI. ICH and CMB locations were classified as lobar or deep. 97 ICH patients with MRI (mean age 65.9 ± 13.9 years) were included. Intracerebral hemorrhage was lobar in 52.6 %.
Electronic supplementary material The online version of this article (doi:10.1007/s00415-015-7840-2) contains supplementary material, which is available to authorized users. & Mona Laible [email protected] 1
Department of Neurology, University of Heidelberg, INF 400, 69120 Heidelberg, Germany
2
Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
3
Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
4
Department of Internal Medicine-I, University of Heidelberg, Heidelberg, Germany
5
Department of Stroke Medicine, Imperial College London, London, UK
Median eGFR was 85.8 mL/min/1.73 m2 (IQR 34.3). Renal dysfunction was present in 12.4 % of the patients. At least one CMB was present in 57.7 % of patients, WML were even more frequent (97.7 %). Age and impaired renal function were factors independently associated with the presence of CMB. The presence of CMB was independently associated with the number and extent of WML. RD is a frequent comorbidity in patients with ICH. Associations of RD with hypertension and with CMB in deep location suggest a predominant impact of RD on deep rather than on lobar microangiopathy. Keywords Renal failure Intracerebral hemorrhage Cerebral white matter lesions Cerebral microbleeds
Introduction Current concepts of the pathogenesis of spontaneous intracerebral hemorrhage (ICH) attribute the vast majority of intracerebral hematomas to underlying cerebral microangiopathies. Lipohyalinosis of penetrating arteries as caused by arterial hypertension is thought to be of major importance for deep ICH [1, 2] and cerebral amyloid angiopathy (CAA) for ICH in predominantly lobar location [3]. Magnetic resonance imaging (MRI) can provide important clues to the pathogenesis of ICH. Deep white matter hyperintensities on MRI may result from the ischemic processes associated with arteriolar lipohyalinosis [4]. Cerebral microbleeds (CMB) on susceptibility-weighted MRI reflect focal hemosiderin deposits from previous small bleeds [5]. CMBs in deep brain structures sparing the U-fibers are typically associated with hypertensive microangiopathies [5]. Lobar CMBs are a major imaging correlate of CAA [6].
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Renal dysfunction (RD) is a frequent comorbidity in patients with spontaneous ICH [7] and ischemic stroke [8, 9]. Furthermore, RD has been associated with the presence of white matter lesions (WML) [10, 11] and lacunar infarcts [12, 13]. One major assumption why RD is associated with ICH is the coexistence of small vessel microangiopathies in brain and kidney. Both are low-resistance end-organs which are exposed to high-volume blood flow throughout the cardiac cycle [10]. Moreover, there are similarities between the microvascular system of the kidney and the brain [10, 11, 13]; and endothelial dysfunction is evident in both [13, 14]. Renal dysfunction is also associated with the presence of CMBs in subjects without known cardiovascular disease [15] and with the presence of CMBs in patients with acute ischemic stroke [11, 16, 17]. There is little information so far on the potential association of RD on CMBs in ICH patients. In a recent ICH cohort study predominantly enrolling black patients, an association of RD with the number and particularly deep brain location of CMB was reported but this was not independent of other vascular risk factors such as hypertension and diabetes [18]. In the present study, we examined whether RD is associated with the presence of different patterns of cerebral microangiopathy and their anatomical distribution in ICH patients. Specifically, we investigated the association of RD with the location and severity of WML and CMBs.
Patients and methods Data for this single-center, observational cohort study were collected as part of a consecutive and prospective stroke registry in the Department of Neurology, University Heidelberg between August 2009 and September 2012. Our neurological emergency room and the stroke unit represent the primary care provider for all acute cerebrovascular events in this area. All study procedures were approved by the local independent ethics committee and have been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Informed consent for study participation was obtained from the patient or the patient’s legal representative. All patients aged C18 years with acute spontaneous ICH, diagnosed according to standard criteria [19], who were admitted to our stroke unit, were consecutively included in the study. Only patients who had received MRI including susceptibility-weighted imaging (SWI) were consecutively included into the present analysis. For each patient, demographic variables, cardiovascular risk factors, patients’ medication, and a history of stroke, TIA, or ICH were recorded. Stroke severity and the premorbid functional status were documented according to the National
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Institute of Health Stroke Scale Score (NIHSSS) and the modified Rankin Scale (mRS), respectively. Exclusion criteria were lack of consent, the detection of extra-axial intracranial bleedings, secondary ICH, and unavailability of creatinine values. ICH was classified as formerly described [1] as ‘‘lobar’’ (involving the cortex/gray-white junction or subcortical white matter) or ‘‘deep’’ hemorrhage. Renal function was assessed by estimating the glomerular filtration rate (eGFR) based on admission plasma creatinine values on admission, age, sex, and race, and was routinely calculated by the local central laboratory using the Modification of Diet in Renal Disease (MDRD) equation [20]. In case that MDRD had not been calculated automatically (e.g., external laboratory), we completed the missing data using the simplified MDRD equation: eGFR (mL/min/1.73 m2) 9 186 9 [serum creatinine (mg/dl)] - 1.154 9 (age) - 0.203 9 (0.742 if female) 9 (1.212 if black colored). We classified the renal function of our cohort according to the modified United States National Kidney Foundation classification of chronic kidney disease (CKD) [21] into five categories: eGFR C90 mL/min/ 1.73 m2; eGFR 60–89; eGFR 30–59; eGFR 15–29; and kidney failure, eGFR \15 with a dichotomization at an eGFR of 60 mL/min/1.73 m2 according to previous categorizations [22]. Imaging protocol All MRIs were acquired using a 3 Tesla MR system (Magnetom Tim Trio or Verio using identical technical parameters, Siemens Healthcare, Erlangen, Germany) with a 12-channel head-matrix coil. Susceptibility-weighted imaging (SWI) was performed with a 3D, fully flowcompensated gradient-echo (GRE) sequence. The GRE magnitude and phase images were converted into SW images by the MR-scanner software. For detection of white matter hyperintensities, T2 TSE and fluid-attenuated inversion recovery (FLAIR) sequences were studied. Assessment of cerebral microbleeds First, imaging data were transferred to a Centricity PACS Radiology RA 1000 workstation or to a Centricity Enterprise Web (GE Healthcare, Chalfont St Giles, Great Britain). Then, CMBs were rated following the protocol developed by an international consensus group [22]. Accordingly, CMBs were defined as rounded or circular foci with low signal intensity on SWI-sequences with a maximum diameter of 10 mm. Mimics of CMBs such as calcifications, cavernomas, small pial blood vessels, partial volume artifacts were always considered, and, whenever identified, excluded from analysis [23, 24]. CMBs were classified according to their localization as ‘‘lobar’’ (cortex/
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gray-white junction or subcortical white matter) and ‘‘deep’’ (basal ganglia, internal and external capsule, thalamus, brain stem and cerebellum) [25]. Assessment of white matter lesions White matter lesions were rated according to the rating scale suggested by Scheltens et al. [26]. This rating scale provides four sum scores in a semi-quantitative way with the following scores possible: periventricular hyperintensities (0–6), lobar white matter hyperintensities (0–24), basal ganglia hyperintensities (0–30), and infratentorial foci (0–30). Hyperintensities which could be identified as former ischemic stroke were excluded. WML rating was performed by two independent trained raters; in case of disagreement, final decision was determined by a third experienced neuroradiologist. Statistical analysis Statistical analysis was performed using SPSS (version 22.0 for Windows; SPSS Inc., Chicago, IL). This study is to be considered as purely explorative and consequently statistical tests and resulting p-values can only be interpreted descriptively and have no confirmatory value. Depending on the scale level of the variables, we used the Mann–Whitney-U test for continuous, but not normally distributed variables and the v2-test for categorical variables to explore differences between CMB, Non-CMB, WML, and Non-WML groups. The relations between the variable NIHSS and the level of the WML score were investigated using Spearman’s correlation, as the NIHSS was regarded as non-normally distributed within our cohort and therefore treated as a categorical variable. Univariate and multivariate logistic regression models were used to explore independent factors on the presence of CMB and WML and separately for patients with and without treatment with vitamin K antagonists (VKA). Due to the limited sample size, the multivariate model only included a limited number of selected possible predictors to avoid overfitting. Adjusted odds ratios and 95 % confidence intervals were calculated from the multivariable logistic regression analyses. Interrater variability was calculated using Cohen’s kappa.
97 patients received an MRI with SWI-sequences and were entered into final analysis (Fig. 1; for comparison of ICH patients with and without MRI compare supplementary Table 1). Mean age of included ICH patients was 65.7 ± 14.5 years, 55.7 % were male, and 14.4 % (n = 14) were using vitamin K antagonists at the time of ICH. Intracerebral hematoma was lobar in 52.6 % and deep in 47.4 %. Median hematoma volume was 10.5 mL, IQR 128.5 mL. Age and the presence of arterial hypertension differed significantly between groups: patients in the MRI group were significantly younger and suffered less frequently from arterial hypertension. Renal function Entire cohort Median eGFR of the entire cohort was 85.8 mL/min/ 1.73 m2 (IQR 70.1/104.4). Renal dysfunction was present in the entire cohort in 14.1 %. Of the entire cohort, 31 patients (14.1 %) had RD (eGFR \60). MRI cohort Median eGFR in the MRI cohort was 86.1 mL/min/ 1.73 m2 (IQR 72.9/104.8). The majority of patients (87.6 %) had a normal to only slightly reduced renal function (eGFR C60), 12.4 % had RD (eGFR \60). Table 1 compares the patients of the MRI cohort (n = 97) with an eGFR [60 and \60 mL/min. White matter lesions At least one WML was present in 97.7 %. The median Scheltens score of all ICH patients was 10 (IQR 6–16). Severity of WML according to the Scheltens score did not
Results In total, 237 patients with ICH were admitted to our stroke unit during the study period (patient characteristics supplementary table I). Seventeen patients were excluded from the analysis because they suffered from other defined (secondary) causes of ICH. Of the remaining 220 patients,
Fig. 1 Flow chart of the study population
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J Neurol Table 1 Patients’ characteristics depending on the presence of renal dysfunction (RD), n = 97 Baseline characteristics
All patients (n = 97)
eGFR \60 (n = 12)
eGFR [60 (n = 85)
p value
Age (years, mean; SD)
65.9 ± 13.9
69.8 ± 10.5
65.3 ± 14.3
0.405*
Male sex
54 (55.7 %)
7 (58.3 %)
47 (55.3 %)
0.843§
Arterial hypertension
74 (76.3 %)
10 (83.3 %)
64 (75.3 %)
0.540§
Diabetes mellitus
14 (14.4 %)
3 (25 %)
11 (12.9 %)
0.266§
Any history of stroke
10 (10.3 %)
2 (16.7 %)
8 (9.4 %)
0.439§
NIHSS at admission (median, IQR)
4; 8
4; 8
5; 4
0.425*
Oral anticoagulation
14 (14.4 %)
6 (50 %)
8 (9.4 %)
0.001§
Antiplatelet therapy
18 (18.6 %)
17 (20.0 %)
2 (16.7 %)
0.020§
12 (41.6 %)
46 (54.1 %)
0.419§
Cortical ICH Any CMB
56 (57.7 %)
11 (91.7 %)
45 (52.9 %)
0.011§
Only lobar CMB Only deep CMB
18 (18.6 %) 9 (16.1 %)
2 (11.1 %) 3 (33.3 %)
16 (88.8 %) 6 (66.6 %)
0.857§ 0.045§
n = 97 refers to all ICH patients who received an MRI RD renal dysfunction, IQR interquartile range, SD standard deviation, NIHSS National Institute of Health Stroke Scale * Mann–Whitney-U test §
Chi-square-test
differ between patients with RD (eGFR \60)(median score: 15; IQR 7–20) compared to patients without (median score: 9; IQR 6–15; p = 0.172). A higher Scheltens score was associated with higher age (p = 0.003), the presence of arterial hypertension (p \ 0.001), and the presence of deep CMBs (p = 0.001, Table 2). These results did not change when patients were stratified by the use of VKA (Table 2), except for the finding that only in patients with VKA use, the association of deep CMB with the Scheltens score was significant (p = 0.016). Location of white matter hyperintensities was predominantly periventricular (97.7 %) and subcortical (89.7 %), whereas basal ganglia hyperintensities and infratentorial foci of hyperintensity were less frequent (50.5 and 10.3 %, respectively). Cerebral microbleeds Interrater variability for assessment of CMB using Cohen’s kappa for the presence of C1 CMB was 0.73, indicating a substantial level of agreement [25]. The median number of CMB was 5 per patient (IQR 2–16). In total, at least one CMB was present in 56 (57.7 %) patients. Thereof, 32.1 % had only lobar CMBs, whereas 16.1 % had only CMBs in deep brain regions. 50.0 % had CMBs both in lobar and deep location. Patients with RD were more likely to have any CMB (OR 9.78, 95 % CI 1.21–79.13, p = 0.033, Tables 3 and 4). In univariate analysis, RD, age, and arterial hypertension were associated with the presence of CMBs in all patients. However, after adjustment for arterial hypertension and age, RD and age were the only
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independent predictors associated with the presence of any CMB in multivariate analysis (Table 4). A further analysis taking the location of CMBs into account revealed that only deep, but not lobar CMBs were significantly associated with arterial hypertension, age, and RD (univariable analysis, supplementary tables II and III). In the multivariate model, only RD remained associated to deep CMB. In patients with VKA intake, there was no correlation of age, male sex, arterial hypertension, RD, or NIHSS with the presence of CMB. In patients without VKA intake, we found a correlation of age and arterial hypertension with the presence of CMB (Table 4). Association of CMB and WML There was an association between the presence of CMB and the WML score as a continuous variable (p \ 0.001, Table 2). A higher Scheltens score was associated with deep CMBs (p = 0.004, Mann–Whitney-U-test and p \ 0.001, linear regression analysis), but not with lobar CMBs (p = 0.487). Table 5 shows the results of the univariable and multivariable linear regression analysis for factors associated with a higher level of the Scheltens score. In univariable analysis, these were age, arterial hypertension, and the presence of CMBs, but not RD. After correction CMBs for age, arterial hypertension, and the presence of CMBs, were independently associated with the Scheltens score. In the subgroup of patients without VKA treatment, arterial hypertension and CMBs were also correlated with the Scheltens score, in contrast to the patients
J Neurol Table 2 Patients’ characteristics at admission associated with the white matter lesion score (as a continuous variable) in univariable analysis and depending on vitamin K antagonist use
WML score (median; IQR)
p value
Male sex (n = 42)
10; 11
0.588§
Female sex (n = 55)
9; 9
Sex All (n = 97)
No VKA (n = 83) Male sex (n = 37)
9; 10
Female sex (n = 46)
9; 9
0.551§
VKA (n = 14) Male sex (n = 5)
21; 12
Female sex (n = 9)
11; 7
0.202§
Arterial hypertension All (n = 97) Arterial hypertension (n = 74)
12; 9
No arterial hypertension (n = 23)
7; 4
No VKA (n = 83) Arterial hypertension (n = 61) No arterial hypertension (n = 22)
\0.001§
11; 10 6.5; 11
0.006§
VKA (n = 14) Arterial hypertension (n = 13)
15; 11
No arterial hypertension (n = 1)
8
0.318§
CMB All (n = 97) CMB present (n = 56)
12; 11
No CMB (n = 41)
8; 8
\0.001§
No VKA (n = 83) CMB present (n = 46)
11; 10
No CMB (n = 37)
8; 8
0.002§
VKA (n = 14) CMB present (n = 10)
15; 12
No CMB (n = 4)
9.5; 6
0.750§
Only lobar CMB All (n = 97) Only lobar CMB (n = 18)
11; 9
Lobar/deep or only deep CMB (n = 79)
9; 9
0.487§
No VKA (n = 83) Only lobar CMB (n = 17)
9; 11
Lobar/deep or only deep CMB (n = 66)
9; 10
0.164§
VKA (n = 14) Only lobar CMB (n = 1)
7
Lobar/deep or only deep CMB (n = 13)
15; 11
0.170§
Only deep CMB All (n = 97) Only deep CMB (n = 36)
15; 12
Also lobar CMB (n = 61)
9; 9
0.004§
10; 10 9; 9
0.074§
No VKA (n = 83) Only deep CMB (n = 56) Also lobar CMB (n = 27) VKA (n = 14) Only deep CMB (n = 9)
15; 9
Also lobar CMB (n = 5)
8; 6
0.016§
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J Neurol Table 2 continued
WML score (median; IQR)
p value
Lobar ICH All (n = 97) Only lobar ICH (n = 51)
9; 9
Also deep ICH (n = 46)
12; 10
0.237§
No VKA (n = 83) Only lobar ICH (n = 46)
9; 8
Also deep ICH (n = 37)
12; 12
0.236§
VKA (n = 14) Only lobar ICH (n = 5)
15; 11
Also deep ICH (n = 9)
13; 11
0.421§
Deep ICH All (n = 97) Deep ICH (n = 46)
12; 10 0.237§
Lobar ICH (n = 51) No VKA (n = 83) Deep ICH (n = 37) Lobar ICH (n = 46)
12; 12 9; 8
0.236§
VKA (n = 14) Deep ICH (n = 9)
13; 11
Lobar ICH (n = 5)
15; 11
0.421§
Diabetes mellitus All (n = 97) Diabetes (n = 14)
14; 11
No diabetes (n = 83)
9; 9
0.073§
No VKA (n = 83) Diabetes (n = 10)
12.5; 9
No diabetes (n = 73)
9; 10
0.244§
VKA (n = 14) Diabetes (n = 4)
18; 11
No diabetes (n = 10)
12; 8
0.200§
History of stroke present All (n = 97) History of stroke (n = 10)
13; 9
No history of stroke (n = 87)
9; 10
0.290§
No VKA (n = 83) History of stroke (n = 5)
14; 10
No history of stroke (n = 78)
9; 10
0.388§
VKA (n = 14) History of stroke (n = 5)
11; 10
No history of stroke (n = 9)
15; 12
0.840§
Renal dysfunction All (n = 97) No renal dysfunction (n = 85)
9; 9
Renal dysfunction (n = 12)
15; 13
0.172§
9; 10 11; 17
0.537§
No VKA (n = 83) No renal dysfunction (n = 77) Renal dysfunction (n = 6) VKA (n = 14)
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No renal dysfunction (n = 8)
12; 12
Renal dysfunction (n = 6)
15; 10
0.516§
J Neurol Table 2 continued
WML score (median; IQR)
p value
Spearman correlation Age 9 WML score All (n = 97)
0.293
0.003*
No VKA (n = 83)
0.288
0.008*
VKA (n = 14)
0.099
0.737*
All (n = 97)
-0.008
0.938*
No VKA (n = 83)
-0.028
0.804*
VKA (n = 14)
-0.006
0.985*
NIHSS at admission (0–36) 9 WML score
WML white matter lesion, n indicates the number of patients affected, IQR interquartile range, CMB cerebral microbleed, ICH intracerebral hemorrhage, NIHSS National Institute of Health Stroke Scale, VKA vitamin K antagonist use §
Mann–Whitney-U test
* Spearman correlation
Table 3 Patients’ characteristics depending on the presence of cerebral microbleeds (CMB), n = 97 Baseline characteristics
All patients (n = 97)
CMB (n = 56)
No CMB (n = 41)
p value \0.001*
Age (years)
Mean; SD
65.9 ± 13.9
70.5 ± 9.5
59.5 ± 16
Sex
Male n (%)
54 (55.7 %)
32 (57.1 %)
22 (53.7 %)
0.733§
Renal dysfunction (eGFR \60)
Present n (%)
12 (12.4 %)
11 (19.6 %)
1 (2.4 %)
0.011§
Arterial hypertension
Present n (%)
74 (76.3 %)
50 (89.3 %)
24 (58.5 %)
Diabetes mellitus
Present n (%)
14 (14.4 %)
8 (14.3 %)
6 (14.6 %)
0.962§
Any history of stroke
Present n (%)
10 (10.3 %)
6 (10.7 %)
4 (9.8 %)
0.88§
\0.001§
NIHSS at admission
Median; IQR
4, 8
4, 8
4, 8
0.528*
Oral anticoagulation
Present n (%)
14 (14.4 %)
10 (17.9 %)
4 (9.8 %)
0.265§
Antiplatelet therapy
Present n (%)
18 (18.6 %)
13 (23.2 %)
6 (14.6 %)
0.281§
Lobar ICH
Present n (%)
51 (52.6 %)
33 (64.7 %)
18 (35.3 %)
0.143§
Deep ICH
Present n (%)
46 (47.4 %)
23 (50 %)
23 (50 %)
0.143§
n = 97 refers to all ICH patients who received an MRI IQR interquartile range, SD standard deviation, NIHSS National Institute of Health Stroke Scale * Mann–Whitney-U test §
Chi-square test
on VKA treatment, in which we did not observe any correlation of age, arterial hypertension, and the presence of CMBs with the Scheltens score (Table 5).
Discussion The major findings of our study are (1) mild to moderate RD is a common comorbidity in ICH patients. (2) RD is an independent predictor for the presence of CMBs but not for WML in ICH patients. (3) RD is associated with deep, but not with lobar CMB. There are several reports on the prevalence of RD in ICH patients. In our cohort of Caucasian ICH patients,
prevalence of RD was 12.4 % in the MRI group and 14.1 % in the entire group of ICH patients. Ovbiagele and coworkers reported that 26 % of their predominantly black ICH patients had an eGFR \60 mL/min/1.73 m2 [18], although their patients were younger with a mean age of 59 years compared to 65.9 years in our cohort. The distribution of lobar and deep macrohemorrhages was similar in both studies. In a Japanese cohort of ICH patients, the prevalence RD was higher with 39 % [27]. In the cohort described by Ovbiagele et al., the prevalence of comorbidity of arterial hypertension was 100 % in patients with renal disease and 83.3 % in patients without renal disease. In our study, the prevalence of arterial hypertension was slightly lower with 78.6 % in patients who had a RD and
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J Neurol Table 4 Association of patients’ characteristics with the presence of any CMB in univariable and multivariable logistic regression [depending on the use of vitamin K antagonists (VKA)] n = 97
Univariable analysis OR
95 % CI
Multivariable analysis p value
OR
95 % CI
p value
Demographic data Age All (n = 97)
1.07
1.03–1.11
0.001
1.05
1.00–1.10
0.017
No VKA (n = 83)
1.08
1.03–1.12
\0.001
1.06
1.01–1.11
0.010
VKA (n = 14)
0.90
0.71–1.15
0.404
–
–
–
Sex (male) All (n = 42)
0.96
0.43–2.16
0.918
–
–
–
No VKA (n = 37)
1.10
0.46–2.64
0.826
–
–
–
VKA (n = 5)
0.43
0.04–4.64
0.486
–
–
–
Arterial hypertension All (n = 74)
5.90
2.07–16.87
0.001
3.38
0.98–11.69
0.054
No VKA(n = 61)
6.97
2.25–21.61
0.001
3.65
1.06–12.55
0.040
VKA (n = 13)*
–
–
–
–
–
–
Cardiovascular risk factors
Diabetes mellitus All (n = 14)
0.97
0.31–3.05
0.962
–
–
–
No VKA (n = 10)
0.492
0.128–1.894
0.302
–
–
–
VKA (n = 4)*
–
–
–
–
–
–
0.046
Renal function Renal dysfunction (eGFR \60) All (n = 12)
9.78
1.21–79.13
0.033
8.75
1.04–74
No VKA (n = 6)
4.39
0.49–39.35
0.186
–
–
–
VKA (n = 6)*
–
–
–
–
–
–
Clinical and functional status NIHSS All (n = 97)
0.96
0.90–1.03
0.232
–
–
–
No VKA (n = 83) VKA (n = 14)
1.055
0.984–1.132
0.134
–
–
–
0.940
0.729–1.212
0.633
–
–
–
CI confidence interval, eGFR estimated glomerular filtration rate, CMB cerebral microbleed, ICH intracerebral hemorrhage, NIHSS National Institute of Health Stroke Scale, VKA vitamin K antagonist use * Logistic regression model does not converge
75.3 % in those who did not. This suggests that ethnicity and associated risk factors influence the prevalence of RD differently. In patients with acute ischemic stroke, the reports on the prevalence of RD were similar to those in hemorrhagic stroke and vary from 24 to 45 % [28, 29]. In general population studies from the USA, 12 % of subjects have mild to moderate RD [30, 31]. On the other hand, population-based screening for RD in Japan yielded a higher prevalence of RD of 15.9–42.6 % [27, 32]. Factors underlying these differences may be higher rates of low birth weight with a lower number of nephrons [33], primary hypertension [34], and higher age of the population [35] in the Japanese population. Our study confirms previous findings of a relatively high prevalence of CMBs in ICH patients with 57.7 % compared to 54 and 58 % in previous reports [18, 36].
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Concordant to the findings of Ovbiagele and colleagues [18], RD was associated with the presence of CMB, their number, and a deep location of CMB. These findings are also consistent with imaging results of 285 patients without history of ischemic stroke or ICH by Umemura and colleagues who found an association of microalbuminuria with a higher prevalence of deep CMB [37]. Altogether, this supports the concept of RD as a pathogenic cofactor of ICH associated to lipohyalinosis rather than intracerebral hemorrhages caused by CAA. There are controversies regarding the effect of renal disease on white matter disease and differences appear to depend on which population—general population, ischemic stroke, or ICH patients—is studied. Unlike previous studies in patients without acute cerebrovascular disease [11, 38], the comorbidity of RD was not associated
J Neurol Table 5 Association of patients’ characteristics, CMBs, and ICH localization with the Scheltens WML score as a continuous variable in linear regression (depending on the use of vitamin K antagonists) Univariable analysis
Multivariable analysis
Regression coefficient B
95 % CI
p value
Regression coefficient B
95 % CI
p value
All
0.13
0.02–0.23
0.019
0.03
-0.09 to 0.14
0.643
No VKA
0.12
0.02–0.23
0.022
0.24
-0.255 to 0.74
0.305
VKA
0.03
-0.05 to 0.58
0.905
–
–
–
Demographic data Age
Sex All
0.98
-1.41 to 4.16
0.328
–
–
–
No VKA
0.99
-2.12 to 4.09
0.529
–
–
–
VKA
4.82
-1.48 to 11.13
0.121
–
–
–
Cardiovascular risk factors Hypertension All
5.02
1.91–8.14
0.002
2.95
-0.51 to 6.41
0.093
No VKA
4.74
1.40–8.09
0.006
9.34
-2.19 to 20.96
0.101
0.321
–
–
–
VKA Diabetes mellitus
5.92
-6.55 to 18.39
All
3.31
-0.59 to 7.22
0.960
–
–
–
No VKA
2.57
-2.15 to 7.29
0.282
–
–
–
VKA
4.20
-2.73 to 11.13
0.211
–
–
– –
History of stroke All
-2.33
-6.89 to 2.23
0.313
–
–
No VKA
-3.26
-9.72 to 3.21
0.319
–
–
–
1.09
-5.87 to 8.05
0.739
–
–
–
All
4.93
2.29 to 7.57
\0.001
3.84
1.00 to 6.68
0.009
No VKA
4.67
1.73 to 7.61
0.002
7.28
0.66 to 13.90
0.034
VKA
5.60
-0.93 to 12.13
0.086
–
–
–
VKA Presence of CMB
Renal function Renal dysfunction (eGFR \60) All No VKA
3.07 2.94
-1.12 to 7.26 -3.00 to 8.88
0.149 0.328
– –
– –
– –
VKA
1.46
-5.25 to 8.17
0.644
–
–
–
All
-2.27
-5.02 to 0.49
0.106
–
–
–
No VKA
-2.69
-5.75 to 0.36
0.083
–
–
–
2.02
-4.86 to 8.90
0.534
–
–
–
ICH Only lobar ICH
VKA
Clinical and functional status NIHSS All
-0.006
-0.21 to 2.0
0.958
–
–
–
No VKA
-0.001
-0.22 to 0.22
0.996
–
–
–
0.006
-0.76 to 0.78
0.987
–
–
–
VKA
CI confidence interval, eGFR estimated glomerular filtration rate, CMB cerebral microbleed, ICH intracerebral hemorrhage, NIHSS National Institute of Health Stroke Scale, VKA use of vitamin K antagonists
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with a higher WML score in our ICH cohort. In the study by Smith et al. [39] who included 133 patients with primary supratentorial ICH, patients with lobar and deep ICH had a similar burden of WML. This is matching with our results as we did not see any significant difference between the WML score in lobar and deep ICH. In multivariable analysis, the WML score was only associated with the presence of CMB. In a population-based study in 625 elderly subjects, low eGFR levels were related to the presence of lacunar infarcts and higher grades of WML [40]. In the Rotterdam scan study examining a total of 1077 elderly subjects from the general population [41], MRI showed one or more WML in 92 % which is similar to our cohort (97.7 %). This supports the concept that small vessel disease underlies CMB and WML. Population-based studies showed a higher WML burden in persons with RD (eGFR \60 mL/min/1.73 m2) [38] and in elderly people [41]. In our cohort of ICH patients, we did not find any significant differences of the WML score in relation to RD. An explanation for this finding could be the relatively low number of twelve patients with RD in ICH patients with MRI. Interestingly, in our ICH patients, a higher Scheltens score was associated with deep CMBs, but not with lobar CMBs. We found no significant association between lobar or deep macrohemorrhage and the localization of CMB, unlike Smith et al. who described an association of lobar ICH with lobar CMB, and deep ICH was more commonly associated with deep CMBs in a subset of 133 patients with primary supratentorial ICH [39]. In summary, our results suggest that the presence of RD and deep CMBs are strongly associated in ICH patients. Our study has strengths and limitations. An advantage of the study is the use of well-established imaging measures and protocols for evaluation of CMBs and WML. Data were prospectively collected and rated by various readers with a good interrater variability. A limitation is the relatively small sample size and a possible selection bias as patients with ICH who did not receive an MRI and therefore could not enter further analyses were significantly older and suffered more frequently from arterial hypertension. We are aware that the proportion of patients who did not receive an MRI in the course of ICH was rather high in our study (51.8 %). This was due to the observational character of the study. The decision to perform an MRI in acute ICH was taken by the treating physician. More extensive diagnostic evaluation of ICH is currently performed in patients who are young, do not have preexisting hypertension, or present with lobar hemorrhage [42]. Thus, our findings may not be representative of spontaneous ICH in general. Another limitation is that RD was diagnosed based on eGFR alone because we did not measure albuminuria. Finally, due to the cross-sectional
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character of the study with the analysis of the MRI at the index ICH event, there may be de novo formation of CMB related to the acute ICH. Therefore, the number of CMB related to renal function impairment may be overestimated. We cannot rule out that eGFR values at admission in the stroke unit overestimate the frequency of kidney dysfunction in our cohort. As eGFR values were retrospectively calculated, we do not dispose of follow-up values to draw conclusions about the presence of a chronic renal impairment. To our knowledge, this is the first study to describe both, CMB and WML in patients with ICH with special respect to concomitant RD. Conflicts of interest RV has received consulting honoraria, research support, travel grants, and speakers’ honoraria from Bayer HealthCare, BoehringerIngelheim, BMS Pfizer, Daiichi Sankyo, Roche Diagnostics, St. Jude Medical, and Sanofi Aventis. TR received consulting honoraria and speakers’ honoraria from BoehringerIngelheim and BMS Pfizer. CW, MM, ML, SH, and SS report no disclosures. Ethical standards This study was approved by the appropriate ethic committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.
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ORIGINAL COMMUNICATION
Renal dysfunction is associated with deep cerebral microbleeds but not white matter hyperintensities in patients with acute intracerebral hemorrhage Mona Laible1 • Solveig Horstmann1 • Markus Mo¨hlenbruch2 • Christian Wegele1 Timolaos Rizos1 • Svenja Schu¨ler3 • Markus Zorn4 • Roland Veltkamp1,5
•
Received: 5 May 2015 / Revised: 29 June 2015 / Accepted: 29 June 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Kidney disease is a risk factor for cerebral microangiopathy and spontaneous intracerebral hemorrhage (ICH). We aimed to determine the association of renal dysfunction (RD) with MRI correlates of different patterns of cerebral microangiopathies including cerebral microbleeds (CMB) and white matter lesions (WML) in patients with ICH. In a prospectively collected, singlecenter cohort of ICH patients, glomerular filtration rate (eGFR) was estimated using the Modification of Diet in Renal Disease equation. We classified the renal function in five categories: category 1 (eGFR C90 mL/min/1.73 m2), category 2 (eGFR 60–89), category 3 (eGFR 30–59), category 4 (eGFR 15–29), and category 5 (eGFR \15) and dichotomized at an eGFR of 60. Number, location, and extent of CMB and WML were measured on MRI. ICH and CMB locations were classified as lobar or deep. 97 ICH patients with MRI (mean age 65.9 ± 13.9 years) were included. Intracerebral hemorrhage was lobar in 52.6 %.
Electronic supplementary material The online version of this article (doi:10.1007/s00415-015-7840-2) contains supplementary material, which is available to authorized users. & Mona Laible [email protected] 1
Department of Neurology, University of Heidelberg, INF 400, 69120 Heidelberg, Germany
2
Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
3
Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
4
Department of Internal Medicine-I, University of Heidelberg, Heidelberg, Germany
5
Department of Stroke Medicine, Imperial College London, London, UK
Median eGFR was 85.8 mL/min/1.73 m2 (IQR 34.3). Renal dysfunction was present in 12.4 % of the patients. At least one CMB was present in 57.7 % of patients, WML were even more frequent (97.7 %). Age and impaired renal function were factors independently associated with the presence of CMB. The presence of CMB was independently associated with the number and extent of WML. RD is a frequent comorbidity in patients with ICH. Associations of RD with hypertension and with CMB in deep location suggest a predominant impact of RD on deep rather than on lobar microangiopathy. Keywords Renal failure Intracerebral hemorrhage Cerebral white matter lesions Cerebral microbleeds
Introduction Current concepts of the pathogenesis of spontaneous intracerebral hemorrhage (ICH) attribute the vast majority of intracerebral hematomas to underlying cerebral microangiopathies. Lipohyalinosis of penetrating arteries as caused by arterial hypertension is thought to be of major importance for deep ICH [1, 2] and cerebral amyloid angiopathy (CAA) for ICH in predominantly lobar location [3]. Magnetic resonance imaging (MRI) can provide important clues to the pathogenesis of ICH. Deep white matter hyperintensities on MRI may result from the ischemic processes associated with arteriolar lipohyalinosis [4]. Cerebral microbleeds (CMB) on susceptibility-weighted MRI reflect focal hemosiderin deposits from previous small bleeds [5]. CMBs in deep brain structures sparing the U-fibers are typically associated with hypertensive microangiopathies [5]. Lobar CMBs are a major imaging correlate of CAA [6].
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Renal dysfunction (RD) is a frequent comorbidity in patients with spontaneous ICH [7] and ischemic stroke [8, 9]. Furthermore, RD has been associated with the presence of white matter lesions (WML) [10, 11] and lacunar infarcts [12, 13]. One major assumption why RD is associated with ICH is the coexistence of small vessel microangiopathies in brain and kidney. Both are low-resistance end-organs which are exposed to high-volume blood flow throughout the cardiac cycle [10]. Moreover, there are similarities between the microvascular system of the kidney and the brain [10, 11, 13]; and endothelial dysfunction is evident in both [13, 14]. Renal dysfunction is also associated with the presence of CMBs in subjects without known cardiovascular disease [15] and with the presence of CMBs in patients with acute ischemic stroke [11, 16, 17]. There is little information so far on the potential association of RD on CMBs in ICH patients. In a recent ICH cohort study predominantly enrolling black patients, an association of RD with the number and particularly deep brain location of CMB was reported but this was not independent of other vascular risk factors such as hypertension and diabetes [18]. In the present study, we examined whether RD is associated with the presence of different patterns of cerebral microangiopathy and their anatomical distribution in ICH patients. Specifically, we investigated the association of RD with the location and severity of WML and CMBs.
Patients and methods Data for this single-center, observational cohort study were collected as part of a consecutive and prospective stroke registry in the Department of Neurology, University Heidelberg between August 2009 and September 2012. Our neurological emergency room and the stroke unit represent the primary care provider for all acute cerebrovascular events in this area. All study procedures were approved by the local independent ethics committee and have been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Informed consent for study participation was obtained from the patient or the patient’s legal representative. All patients aged C18 years with acute spontaneous ICH, diagnosed according to standard criteria [19], who were admitted to our stroke unit, were consecutively included in the study. Only patients who had received MRI including susceptibility-weighted imaging (SWI) were consecutively included into the present analysis. For each patient, demographic variables, cardiovascular risk factors, patients’ medication, and a history of stroke, TIA, or ICH were recorded. Stroke severity and the premorbid functional status were documented according to the National
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Institute of Health Stroke Scale Score (NIHSSS) and the modified Rankin Scale (mRS), respectively. Exclusion criteria were lack of consent, the detection of extra-axial intracranial bleedings, secondary ICH, and unavailability of creatinine values. ICH was classified as formerly described [1] as ‘‘lobar’’ (involving the cortex/gray-white junction or subcortical white matter) or ‘‘deep’’ hemorrhage. Renal function was assessed by estimating the glomerular filtration rate (eGFR) based on admission plasma creatinine values on admission, age, sex, and race, and was routinely calculated by the local central laboratory using the Modification of Diet in Renal Disease (MDRD) equation [20]. In case that MDRD had not been calculated automatically (e.g., external laboratory), we completed the missing data using the simplified MDRD equation: eGFR (mL/min/1.73 m2) 9 186 9 [serum creatinine (mg/dl)] - 1.154 9 (age) - 0.203 9 (0.742 if female) 9 (1.212 if black colored). We classified the renal function of our cohort according to the modified United States National Kidney Foundation classification of chronic kidney disease (CKD) [21] into five categories: eGFR C90 mL/min/ 1.73 m2; eGFR 60–89; eGFR 30–59; eGFR 15–29; and kidney failure, eGFR \15 with a dichotomization at an eGFR of 60 mL/min/1.73 m2 according to previous categorizations [22]. Imaging protocol All MRIs were acquired using a 3 Tesla MR system (Magnetom Tim Trio or Verio using identical technical parameters, Siemens Healthcare, Erlangen, Germany) with a 12-channel head-matrix coil. Susceptibility-weighted imaging (SWI) was performed with a 3D, fully flowcompensated gradient-echo (GRE) sequence. The GRE magnitude and phase images were converted into SW images by the MR-scanner software. For detection of white matter hyperintensities, T2 TSE and fluid-attenuated inversion recovery (FLAIR) sequences were studied. Assessment of cerebral microbleeds First, imaging data were transferred to a Centricity PACS Radiology RA 1000 workstation or to a Centricity Enterprise Web (GE Healthcare, Chalfont St Giles, Great Britain). Then, CMBs were rated following the protocol developed by an international consensus group [22]. Accordingly, CMBs were defined as rounded or circular foci with low signal intensity on SWI-sequences with a maximum diameter of 10 mm. Mimics of CMBs such as calcifications, cavernomas, small pial blood vessels, partial volume artifacts were always considered, and, whenever identified, excluded from analysis [23, 24]. CMBs were classified according to their localization as ‘‘lobar’’ (cortex/
J Neurol
gray-white junction or subcortical white matter) and ‘‘deep’’ (basal ganglia, internal and external capsule, thalamus, brain stem and cerebellum) [25]. Assessment of white matter lesions White matter lesions were rated according to the rating scale suggested by Scheltens et al. [26]. This rating scale provides four sum scores in a semi-quantitative way with the following scores possible: periventricular hyperintensities (0–6), lobar white matter hyperintensities (0–24), basal ganglia hyperintensities (0–30), and infratentorial foci (0–30). Hyperintensities which could be identified as former ischemic stroke were excluded. WML rating was performed by two independent trained raters; in case of disagreement, final decision was determined by a third experienced neuroradiologist. Statistical analysis Statistical analysis was performed using SPSS (version 22.0 for Windows; SPSS Inc., Chicago, IL). This study is to be considered as purely explorative and consequently statistical tests and resulting p-values can only be interpreted descriptively and have no confirmatory value. Depending on the scale level of the variables, we used the Mann–Whitney-U test for continuous, but not normally distributed variables and the v2-test for categorical variables to explore differences between CMB, Non-CMB, WML, and Non-WML groups. The relations between the variable NIHSS and the level of the WML score were investigated using Spearman’s correlation, as the NIHSS was regarded as non-normally distributed within our cohort and therefore treated as a categorical variable. Univariate and multivariate logistic regression models were used to explore independent factors on the presence of CMB and WML and separately for patients with and without treatment with vitamin K antagonists (VKA). Due to the limited sample size, the multivariate model only included a limited number of selected possible predictors to avoid overfitting. Adjusted odds ratios and 95 % confidence intervals were calculated from the multivariable logistic regression analyses. Interrater variability was calculated using Cohen’s kappa.
97 patients received an MRI with SWI-sequences and were entered into final analysis (Fig. 1; for comparison of ICH patients with and without MRI compare supplementary Table 1). Mean age of included ICH patients was 65.7 ± 14.5 years, 55.7 % were male, and 14.4 % (n = 14) were using vitamin K antagonists at the time of ICH. Intracerebral hematoma was lobar in 52.6 % and deep in 47.4 %. Median hematoma volume was 10.5 mL, IQR 128.5 mL. Age and the presence of arterial hypertension differed significantly between groups: patients in the MRI group were significantly younger and suffered less frequently from arterial hypertension. Renal function Entire cohort Median eGFR of the entire cohort was 85.8 mL/min/ 1.73 m2 (IQR 70.1/104.4). Renal dysfunction was present in the entire cohort in 14.1 %. Of the entire cohort, 31 patients (14.1 %) had RD (eGFR \60). MRI cohort Median eGFR in the MRI cohort was 86.1 mL/min/ 1.73 m2 (IQR 72.9/104.8). The majority of patients (87.6 %) had a normal to only slightly reduced renal function (eGFR C60), 12.4 % had RD (eGFR \60). Table 1 compares the patients of the MRI cohort (n = 97) with an eGFR [60 and \60 mL/min. White matter lesions At least one WML was present in 97.7 %. The median Scheltens score of all ICH patients was 10 (IQR 6–16). Severity of WML according to the Scheltens score did not
Results In total, 237 patients with ICH were admitted to our stroke unit during the study period (patient characteristics supplementary table I). Seventeen patients were excluded from the analysis because they suffered from other defined (secondary) causes of ICH. Of the remaining 220 patients,
Fig. 1 Flow chart of the study population
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J Neurol Table 1 Patients’ characteristics depending on the presence of renal dysfunction (RD), n = 97 Baseline characteristics
All patients (n = 97)
eGFR \60 (n = 12)
eGFR [60 (n = 85)
p value
Age (years, mean; SD)
65.9 ± 13.9
69.8 ± 10.5
65.3 ± 14.3
0.405*
Male sex
54 (55.7 %)
7 (58.3 %)
47 (55.3 %)
0.843§
Arterial hypertension
74 (76.3 %)
10 (83.3 %)
64 (75.3 %)
0.540§
Diabetes mellitus
14 (14.4 %)
3 (25 %)
11 (12.9 %)
0.266§
Any history of stroke
10 (10.3 %)
2 (16.7 %)
8 (9.4 %)
0.439§
NIHSS at admission (median, IQR)
4; 8
4; 8
5; 4
0.425*
Oral anticoagulation
14 (14.4 %)
6 (50 %)
8 (9.4 %)
0.001§
Antiplatelet therapy
18 (18.6 %)
17 (20.0 %)
2 (16.7 %)
0.020§
12 (41.6 %)
46 (54.1 %)
0.419§
Cortical ICH Any CMB
56 (57.7 %)
11 (91.7 %)
45 (52.9 %)
0.011§
Only lobar CMB Only deep CMB
18 (18.6 %) 9 (16.1 %)
2 (11.1 %) 3 (33.3 %)
16 (88.8 %) 6 (66.6 %)
0.857§ 0.045§
n = 97 refers to all ICH patients who received an MRI RD renal dysfunction, IQR interquartile range, SD standard deviation, NIHSS National Institute of Health Stroke Scale * Mann–Whitney-U test §
Chi-square-test
differ between patients with RD (eGFR \60)(median score: 15; IQR 7–20) compared to patients without (median score: 9; IQR 6–15; p = 0.172). A higher Scheltens score was associated with higher age (p = 0.003), the presence of arterial hypertension (p \ 0.001), and the presence of deep CMBs (p = 0.001, Table 2). These results did not change when patients were stratified by the use of VKA (Table 2), except for the finding that only in patients with VKA use, the association of deep CMB with the Scheltens score was significant (p = 0.016). Location of white matter hyperintensities was predominantly periventricular (97.7 %) and subcortical (89.7 %), whereas basal ganglia hyperintensities and infratentorial foci of hyperintensity were less frequent (50.5 and 10.3 %, respectively). Cerebral microbleeds Interrater variability for assessment of CMB using Cohen’s kappa for the presence of C1 CMB was 0.73, indicating a substantial level of agreement [25]. The median number of CMB was 5 per patient (IQR 2–16). In total, at least one CMB was present in 56 (57.7 %) patients. Thereof, 32.1 % had only lobar CMBs, whereas 16.1 % had only CMBs in deep brain regions. 50.0 % had CMBs both in lobar and deep location. Patients with RD were more likely to have any CMB (OR 9.78, 95 % CI 1.21–79.13, p = 0.033, Tables 3 and 4). In univariate analysis, RD, age, and arterial hypertension were associated with the presence of CMBs in all patients. However, after adjustment for arterial hypertension and age, RD and age were the only
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independent predictors associated with the presence of any CMB in multivariate analysis (Table 4). A further analysis taking the location of CMBs into account revealed that only deep, but not lobar CMBs were significantly associated with arterial hypertension, age, and RD (univariable analysis, supplementary tables II and III). In the multivariate model, only RD remained associated to deep CMB. In patients with VKA intake, there was no correlation of age, male sex, arterial hypertension, RD, or NIHSS with the presence of CMB. In patients without VKA intake, we found a correlation of age and arterial hypertension with the presence of CMB (Table 4). Association of CMB and WML There was an association between the presence of CMB and the WML score as a continuous variable (p \ 0.001, Table 2). A higher Scheltens score was associated with deep CMBs (p = 0.004, Mann–Whitney-U-test and p \ 0.001, linear regression analysis), but not with lobar CMBs (p = 0.487). Table 5 shows the results of the univariable and multivariable linear regression analysis for factors associated with a higher level of the Scheltens score. In univariable analysis, these were age, arterial hypertension, and the presence of CMBs, but not RD. After correction CMBs for age, arterial hypertension, and the presence of CMBs, were independently associated with the Scheltens score. In the subgroup of patients without VKA treatment, arterial hypertension and CMBs were also correlated with the Scheltens score, in contrast to the patients
J Neurol Table 2 Patients’ characteristics at admission associated with the white matter lesion score (as a continuous variable) in univariable analysis and depending on vitamin K antagonist use
WML score (median; IQR)
p value
Male sex (n = 42)
10; 11
0.588§
Female sex (n = 55)
9; 9
Sex All (n = 97)
No VKA (n = 83) Male sex (n = 37)
9; 10
Female sex (n = 46)
9; 9
0.551§
VKA (n = 14) Male sex (n = 5)
21; 12
Female sex (n = 9)
11; 7
0.202§
Arterial hypertension All (n = 97) Arterial hypertension (n = 74)
12; 9
No arterial hypertension (n = 23)
7; 4
No VKA (n = 83) Arterial hypertension (n = 61) No arterial hypertension (n = 22)
\0.001§
11; 10 6.5; 11
0.006§
VKA (n = 14) Arterial hypertension (n = 13)
15; 11
No arterial hypertension (n = 1)
8
0.318§
CMB All (n = 97) CMB present (n = 56)
12; 11
No CMB (n = 41)
8; 8
\0.001§
No VKA (n = 83) CMB present (n = 46)
11; 10
No CMB (n = 37)
8; 8
0.002§
VKA (n = 14) CMB present (n = 10)
15; 12
No CMB (n = 4)
9.5; 6
0.750§
Only lobar CMB All (n = 97) Only lobar CMB (n = 18)
11; 9
Lobar/deep or only deep CMB (n = 79)
9; 9
0.487§
No VKA (n = 83) Only lobar CMB (n = 17)
9; 11
Lobar/deep or only deep CMB (n = 66)
9; 10
0.164§
VKA (n = 14) Only lobar CMB (n = 1)
7
Lobar/deep or only deep CMB (n = 13)
15; 11
0.170§
Only deep CMB All (n = 97) Only deep CMB (n = 36)
15; 12
Also lobar CMB (n = 61)
9; 9
0.004§
10; 10 9; 9
0.074§
No VKA (n = 83) Only deep CMB (n = 56) Also lobar CMB (n = 27) VKA (n = 14) Only deep CMB (n = 9)
15; 9
Also lobar CMB (n = 5)
8; 6
0.016§
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J Neurol Table 2 continued
WML score (median; IQR)
p value
Lobar ICH All (n = 97) Only lobar ICH (n = 51)
9; 9
Also deep ICH (n = 46)
12; 10
0.237§
No VKA (n = 83) Only lobar ICH (n = 46)
9; 8
Also deep ICH (n = 37)
12; 12
0.236§
VKA (n = 14) Only lobar ICH (n = 5)
15; 11
Also deep ICH (n = 9)
13; 11
0.421§
Deep ICH All (n = 97) Deep ICH (n = 46)
12; 10 0.237§
Lobar ICH (n = 51) No VKA (n = 83) Deep ICH (n = 37) Lobar ICH (n = 46)
12; 12 9; 8
0.236§
VKA (n = 14) Deep ICH (n = 9)
13; 11
Lobar ICH (n = 5)
15; 11
0.421§
Diabetes mellitus All (n = 97) Diabetes (n = 14)
14; 11
No diabetes (n = 83)
9; 9
0.073§
No VKA (n = 83) Diabetes (n = 10)
12.5; 9
No diabetes (n = 73)
9; 10
0.244§
VKA (n = 14) Diabetes (n = 4)
18; 11
No diabetes (n = 10)
12; 8
0.200§
History of stroke present All (n = 97) History of stroke (n = 10)
13; 9
No history of stroke (n = 87)
9; 10
0.290§
No VKA (n = 83) History of stroke (n = 5)
14; 10
No history of stroke (n = 78)
9; 10
0.388§
VKA (n = 14) History of stroke (n = 5)
11; 10
No history of stroke (n = 9)
15; 12
0.840§
Renal dysfunction All (n = 97) No renal dysfunction (n = 85)
9; 9
Renal dysfunction (n = 12)
15; 13
0.172§
9; 10 11; 17
0.537§
No VKA (n = 83) No renal dysfunction (n = 77) Renal dysfunction (n = 6) VKA (n = 14)
123
No renal dysfunction (n = 8)
12; 12
Renal dysfunction (n = 6)
15; 10
0.516§
J Neurol Table 2 continued
WML score (median; IQR)
p value
Spearman correlation Age 9 WML score All (n = 97)
0.293
0.003*
No VKA (n = 83)
0.288
0.008*
VKA (n = 14)
0.099
0.737*
All (n = 97)
-0.008
0.938*
No VKA (n = 83)
-0.028
0.804*
VKA (n = 14)
-0.006
0.985*
NIHSS at admission (0–36) 9 WML score
WML white matter lesion, n indicates the number of patients affected, IQR interquartile range, CMB cerebral microbleed, ICH intracerebral hemorrhage, NIHSS National Institute of Health Stroke Scale, VKA vitamin K antagonist use §
Mann–Whitney-U test
* Spearman correlation
Table 3 Patients’ characteristics depending on the presence of cerebral microbleeds (CMB), n = 97 Baseline characteristics
All patients (n = 97)
CMB (n = 56)
No CMB (n = 41)
p value \0.001*
Age (years)
Mean; SD
65.9 ± 13.9
70.5 ± 9.5
59.5 ± 16
Sex
Male n (%)
54 (55.7 %)
32 (57.1 %)
22 (53.7 %)
0.733§
Renal dysfunction (eGFR \60)
Present n (%)
12 (12.4 %)
11 (19.6 %)
1 (2.4 %)
0.011§
Arterial hypertension
Present n (%)
74 (76.3 %)
50 (89.3 %)
24 (58.5 %)
Diabetes mellitus
Present n (%)
14 (14.4 %)
8 (14.3 %)
6 (14.6 %)
0.962§
Any history of stroke
Present n (%)
10 (10.3 %)
6 (10.7 %)
4 (9.8 %)
0.88§
\0.001§
NIHSS at admission
Median; IQR
4, 8
4, 8
4, 8
0.528*
Oral anticoagulation
Present n (%)
14 (14.4 %)
10 (17.9 %)
4 (9.8 %)
0.265§
Antiplatelet therapy
Present n (%)
18 (18.6 %)
13 (23.2 %)
6 (14.6 %)
0.281§
Lobar ICH
Present n (%)
51 (52.6 %)
33 (64.7 %)
18 (35.3 %)
0.143§
Deep ICH
Present n (%)
46 (47.4 %)
23 (50 %)
23 (50 %)
0.143§
n = 97 refers to all ICH patients who received an MRI IQR interquartile range, SD standard deviation, NIHSS National Institute of Health Stroke Scale * Mann–Whitney-U test §
Chi-square test
on VKA treatment, in which we did not observe any correlation of age, arterial hypertension, and the presence of CMBs with the Scheltens score (Table 5).
Discussion The major findings of our study are (1) mild to moderate RD is a common comorbidity in ICH patients. (2) RD is an independent predictor for the presence of CMBs but not for WML in ICH patients. (3) RD is associated with deep, but not with lobar CMB. There are several reports on the prevalence of RD in ICH patients. In our cohort of Caucasian ICH patients,
prevalence of RD was 12.4 % in the MRI group and 14.1 % in the entire group of ICH patients. Ovbiagele and coworkers reported that 26 % of their predominantly black ICH patients had an eGFR \60 mL/min/1.73 m2 [18], although their patients were younger with a mean age of 59 years compared to 65.9 years in our cohort. The distribution of lobar and deep macrohemorrhages was similar in both studies. In a Japanese cohort of ICH patients, the prevalence RD was higher with 39 % [27]. In the cohort described by Ovbiagele et al., the prevalence of comorbidity of arterial hypertension was 100 % in patients with renal disease and 83.3 % in patients without renal disease. In our study, the prevalence of arterial hypertension was slightly lower with 78.6 % in patients who had a RD and
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J Neurol Table 4 Association of patients’ characteristics with the presence of any CMB in univariable and multivariable logistic regression [depending on the use of vitamin K antagonists (VKA)] n = 97
Univariable analysis OR
95 % CI
Multivariable analysis p value
OR
95 % CI
p value
Demographic data Age All (n = 97)
1.07
1.03–1.11
0.001
1.05
1.00–1.10
0.017
No VKA (n = 83)
1.08
1.03–1.12
\0.001
1.06
1.01–1.11
0.010
VKA (n = 14)
0.90
0.71–1.15
0.404
–
–
–
Sex (male) All (n = 42)
0.96
0.43–2.16
0.918
–
–
–
No VKA (n = 37)
1.10
0.46–2.64
0.826
–
–
–
VKA (n = 5)
0.43
0.04–4.64
0.486
–
–
–
Arterial hypertension All (n = 74)
5.90
2.07–16.87
0.001
3.38
0.98–11.69
0.054
No VKA(n = 61)
6.97
2.25–21.61
0.001
3.65
1.06–12.55
0.040
VKA (n = 13)*
–
–
–
–
–
–
Cardiovascular risk factors
Diabetes mellitus All (n = 14)
0.97
0.31–3.05
0.962
–
–
–
No VKA (n = 10)
0.492
0.128–1.894
0.302
–
–
–
VKA (n = 4)*
–
–
–
–
–
–
0.046
Renal function Renal dysfunction (eGFR \60) All (n = 12)
9.78
1.21–79.13
0.033
8.75
1.04–74
No VKA (n = 6)
4.39
0.49–39.35
0.186
–
–
–
VKA (n = 6)*
–
–
–
–
–
–
Clinical and functional status NIHSS All (n = 97)
0.96
0.90–1.03
0.232
–
–
–
No VKA (n = 83) VKA (n = 14)
1.055
0.984–1.132
0.134
–
–
–
0.940
0.729–1.212
0.633
–
–
–
CI confidence interval, eGFR estimated glomerular filtration rate, CMB cerebral microbleed, ICH intracerebral hemorrhage, NIHSS National Institute of Health Stroke Scale, VKA vitamin K antagonist use * Logistic regression model does not converge
75.3 % in those who did not. This suggests that ethnicity and associated risk factors influence the prevalence of RD differently. In patients with acute ischemic stroke, the reports on the prevalence of RD were similar to those in hemorrhagic stroke and vary from 24 to 45 % [28, 29]. In general population studies from the USA, 12 % of subjects have mild to moderate RD [30, 31]. On the other hand, population-based screening for RD in Japan yielded a higher prevalence of RD of 15.9–42.6 % [27, 32]. Factors underlying these differences may be higher rates of low birth weight with a lower number of nephrons [33], primary hypertension [34], and higher age of the population [35] in the Japanese population. Our study confirms previous findings of a relatively high prevalence of CMBs in ICH patients with 57.7 % compared to 54 and 58 % in previous reports [18, 36].
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Concordant to the findings of Ovbiagele and colleagues [18], RD was associated with the presence of CMB, their number, and a deep location of CMB. These findings are also consistent with imaging results of 285 patients without history of ischemic stroke or ICH by Umemura and colleagues who found an association of microalbuminuria with a higher prevalence of deep CMB [37]. Altogether, this supports the concept of RD as a pathogenic cofactor of ICH associated to lipohyalinosis rather than intracerebral hemorrhages caused by CAA. There are controversies regarding the effect of renal disease on white matter disease and differences appear to depend on which population—general population, ischemic stroke, or ICH patients—is studied. Unlike previous studies in patients without acute cerebrovascular disease [11, 38], the comorbidity of RD was not associated
J Neurol Table 5 Association of patients’ characteristics, CMBs, and ICH localization with the Scheltens WML score as a continuous variable in linear regression (depending on the use of vitamin K antagonists) Univariable analysis
Multivariable analysis
Regression coefficient B
95 % CI
p value
Regression coefficient B
95 % CI
p value
All
0.13
0.02–0.23
0.019
0.03
-0.09 to 0.14
0.643
No VKA
0.12
0.02–0.23
0.022
0.24
-0.255 to 0.74
0.305
VKA
0.03
-0.05 to 0.58
0.905
–
–
–
Demographic data Age
Sex All
0.98
-1.41 to 4.16
0.328
–
–
–
No VKA
0.99
-2.12 to 4.09
0.529
–
–
–
VKA
4.82
-1.48 to 11.13
0.121
–
–
–
Cardiovascular risk factors Hypertension All
5.02
1.91–8.14
0.002
2.95
-0.51 to 6.41
0.093
No VKA
4.74
1.40–8.09
0.006
9.34
-2.19 to 20.96
0.101
0.321
–
–
–
VKA Diabetes mellitus
5.92
-6.55 to 18.39
All
3.31
-0.59 to 7.22
0.960
–
–
–
No VKA
2.57
-2.15 to 7.29
0.282
–
–
–
VKA
4.20
-2.73 to 11.13
0.211
–
–
– –
History of stroke All
-2.33
-6.89 to 2.23
0.313
–
–
No VKA
-3.26
-9.72 to 3.21
0.319
–
–
–
1.09
-5.87 to 8.05
0.739
–
–
–
All
4.93
2.29 to 7.57
\0.001
3.84
1.00 to 6.68
0.009
No VKA
4.67
1.73 to 7.61
0.002
7.28
0.66 to 13.90
0.034
VKA
5.60
-0.93 to 12.13
0.086
–
–
–
VKA Presence of CMB
Renal function Renal dysfunction (eGFR \60) All No VKA
3.07 2.94
-1.12 to 7.26 -3.00 to 8.88
0.149 0.328
– –
– –
– –
VKA
1.46
-5.25 to 8.17
0.644
–
–
–
All
-2.27
-5.02 to 0.49
0.106
–
–
–
No VKA
-2.69
-5.75 to 0.36
0.083
–
–
–
2.02
-4.86 to 8.90
0.534
–
–
–
ICH Only lobar ICH
VKA
Clinical and functional status NIHSS All
-0.006
-0.21 to 2.0
0.958
–
–
–
No VKA
-0.001
-0.22 to 0.22
0.996
–
–
–
0.006
-0.76 to 0.78
0.987
–
–
–
VKA
CI confidence interval, eGFR estimated glomerular filtration rate, CMB cerebral microbleed, ICH intracerebral hemorrhage, NIHSS National Institute of Health Stroke Scale, VKA use of vitamin K antagonists
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J Neurol
with a higher WML score in our ICH cohort. In the study by Smith et al. [39] who included 133 patients with primary supratentorial ICH, patients with lobar and deep ICH had a similar burden of WML. This is matching with our results as we did not see any significant difference between the WML score in lobar and deep ICH. In multivariable analysis, the WML score was only associated with the presence of CMB. In a population-based study in 625 elderly subjects, low eGFR levels were related to the presence of lacunar infarcts and higher grades of WML [40]. In the Rotterdam scan study examining a total of 1077 elderly subjects from the general population [41], MRI showed one or more WML in 92 % which is similar to our cohort (97.7 %). This supports the concept that small vessel disease underlies CMB and WML. Population-based studies showed a higher WML burden in persons with RD (eGFR \60 mL/min/1.73 m2) [38] and in elderly people [41]. In our cohort of ICH patients, we did not find any significant differences of the WML score in relation to RD. An explanation for this finding could be the relatively low number of twelve patients with RD in ICH patients with MRI. Interestingly, in our ICH patients, a higher Scheltens score was associated with deep CMBs, but not with lobar CMBs. We found no significant association between lobar or deep macrohemorrhage and the localization of CMB, unlike Smith et al. who described an association of lobar ICH with lobar CMB, and deep ICH was more commonly associated with deep CMBs in a subset of 133 patients with primary supratentorial ICH [39]. In summary, our results suggest that the presence of RD and deep CMBs are strongly associated in ICH patients. Our study has strengths and limitations. An advantage of the study is the use of well-established imaging measures and protocols for evaluation of CMBs and WML. Data were prospectively collected and rated by various readers with a good interrater variability. A limitation is the relatively small sample size and a possible selection bias as patients with ICH who did not receive an MRI and therefore could not enter further analyses were significantly older and suffered more frequently from arterial hypertension. We are aware that the proportion of patients who did not receive an MRI in the course of ICH was rather high in our study (51.8 %). This was due to the observational character of the study. The decision to perform an MRI in acute ICH was taken by the treating physician. More extensive diagnostic evaluation of ICH is currently performed in patients who are young, do not have preexisting hypertension, or present with lobar hemorrhage [42]. Thus, our findings may not be representative of spontaneous ICH in general. Another limitation is that RD was diagnosed based on eGFR alone because we did not measure albuminuria. Finally, due to the cross-sectional
123
character of the study with the analysis of the MRI at the index ICH event, there may be de novo formation of CMB related to the acute ICH. Therefore, the number of CMB related to renal function impairment may be overestimated. We cannot rule out that eGFR values at admission in the stroke unit overestimate the frequency of kidney dysfunction in our cohort. As eGFR values were retrospectively calculated, we do not dispose of follow-up values to draw conclusions about the presence of a chronic renal impairment. To our knowledge, this is the first study to describe both, CMB and WML in patients with ICH with special respect to concomitant RD. Conflicts of interest RV has received consulting honoraria, research support, travel grants, and speakers’ honoraria from Bayer HealthCare, BoehringerIngelheim, BMS Pfizer, Daiichi Sankyo, Roche Diagnostics, St. Jude Medical, and Sanofi Aventis. TR received consulting honoraria and speakers’ honoraria from BoehringerIngelheim and BMS Pfizer. CW, MM, ML, SH, and SS report no disclosures. Ethical standards This study was approved by the appropriate ethic committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.
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