Prognostic Value of Inflammatory and Cardiovascular Biomarkers for Prediction of 90-Day All-Cause Mortality after Acute Ischemic Stroke—Results from the Linz Stroke Unit Study

Abstract

BACKGROUND

Early outcome prediction after acute ischemic stroke is of great interest. The aim of our study was to evaluate the prognostic value of blood biomarkers in patients with acute ischemic stroke.

METHODS

We measured interleukin-6 (IL-6), d-dimer, amino-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T, and soluble ST2 plasma concentrations within 24 h after admission to our stroke unit in 721 consecutive acute ischemic stroke patients. End point was 90-day all-cause mortality.

RESULTS

During follow-up 81 patients died (11%). In univariate Cox proportional hazards regression analyses with the biochemical markers dichotomized according to median values, all baseline blood biomarkers were strong prognostic markers. However, in the multivariate analysis after adjustment for several clinical variables and the NIH Stroke Scale (NIHSS), only NIHSS >3 [risk ratio (RR) 7.87, 95% CI, 3.61–17.16; P < 0.001], IL-6 > 7 pg/mL (RR 4.09, 95% CI, 2.02–8.29; P < 0.001), and NT-proBNP >447 ng/L (RR 4.88, 95% CI, 2.41–9.88; P < 0.001) remained independent predictors. Using a simple multimarker approach combining these 3 complementary markers, we demonstrated that patients with increased NIHSS, IL-6, and NT-proBNP had the poorest outcome with a mortality rate of 38%, whereas no patient with negative readings for all 3 markers died during follow-up.

CONCLUSIONS

In this large cohort of patients with acute ischemic stroke, IL-6 and NT-proBNP at admission were strong and independent prognostic markers for 90-day all-cause mortality, and provided complementary prognostic information to the routinely used stroke severity score NIHSS.

Ischemic stroke is a devastating disease, being a leading cause of disability and one of the major causes of death worldwide (1, 2). There is an urgent need of accurate prediction of outcome after acute ischemic stroke for physicians, patients, and their families to aid early and informed decision-making about acute therapies, palliative care, and/or rehabilitation.

In clinical routine, prognostication is based on clinical variables including the NIH Stroke Scale (NIHSS)³ (3). The NIHSS is a stroke severity score, which quantifies neurological deficits in acute stroke. Baseline NIHSS is predictive of disease progression and outcome in patients after ischemic acute stroke (3–5).

Blood biomarkers reflecting pathophysiological processes associated with acute ischemic stroke and/or other concomitant diseases might improve outcome prediction. Previous clinical studies have shown that increased plasma concentrations of several biomarkers are associated with adverse outcome after acute ischemic stroke (4–7).

Some promising prognostic candidate biomarkers so far were inflammatory markers [e.g., C-reactive protein (CRP), interleukins], coagulation markers (e.g., d-dimer, von Willebrand factor) as well as cardiac markers (e.g., cardiac troponins, natriuretic peptides) (4–7). However, none have shown a clinical relevant additive value to the routinely used NIHSS (4–6).

The aim of this study was to evaluate the prognostic value of established and novel inflammatory and cardiovascular biomarkers [including interleukin-6 (IL-6), d-dimer, amino-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and soluble ST2 (sST2)] in patients after acute ischemic stroke. We hypothesized that incorporating independent prognostic biomarkers into a simple multimarker model with the NIHSS would improve outcome prediction in patients after acute ischemic stroke.

Methods

STUDY POPULATION

The Linz Stroke Unit (LISU) study is a prospective single-center study designed to assess the prognostic value of biomarkers in patients after acute ischemic stroke. All patients admitted to our 6-bed stroke unit of the Konventhospital Barmherzige Brueder Linz were enrolled between March 21, 2011 and March 20, 2013. Eligible patients were all patients admitted to our stroke unit during the study period. Exclusion criteria were patients with transient ischemic attack, nonstroke, or hemorrhagic stroke. Further, patients with no baseline blood collection for biomarker measurement within 24 h after admission to the stroke unit and patients without informed consent were excluded. The study protocol of the Linz Stroke Unit (LISU) study was approved by the local ethics committee in accordance with the Declaration of Helsinki and all study participants or the legally authorized representative gave informed consent.

For each patient 2 experienced stroke neurologists (C. Bocksrucker and C. Eggers) agreed on a final diagnosis of confirmed ischemic stroke, transient ischemic attack, nonstroke, or hemorrhagic stroke, after considering the presentation, neuroimaging (computer tomography and/or MRI), and clinical course, blinded to the results of the baseline biomarker measurements. We defined an ischemic stroke as a clinically definite stroke in a patient whose symptoms lasted >24 h, and whose brain imaging showed either evidence of a relevant ischemic lesion or was normal, and excluded intracranial hemorrhage and stroke mimics. Stroke severity was assessed using the NIHSS at admission to our stroke unit (3). Etiologic subtypes of ischemic stroke were classified on the basis of the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification (8). The clinical stroke syndrome was determined by applying the criteria of the Oxfordshire Community Stroke Project (OCSP) (9). Demographic characteristics and cardiovascular risk factors at baseline were obtained from medical records.

BIOCHEMICAL ANALYSES

Using Vacuette polyethylene terephthalate glycol blood collection tubes (Greiner Bio-One), EDTA, citrate, and lithium-heparin anticoagulated blood was collected within 24 h after admission to the stroke unit. CRP, total cholesterol, albumin, and creatinine (enzymatic method) were analyzed with standard assays on an Architect c16000 analyzer (Abbott Diagnostics). Estimated glomerular filtration rate (eGFR) was calculated according to the Modification of Diet in Renal Disease (MDRD) equation (10). Renal dysfunction was defined as an eGFR <60 mL/min/1.73 m². White blood cell count was performed on an Advia 2120 analyzer (Siemens Diagnostics). d-Dimer was measured with the Innovance d-dimer assay on a Sysmex CA-7000 analyzer (Siemens Diagnostics) analyzer and total CVs were 7.9% and 2.6% at mean values of 0.2 and 3.6 mg/L. CRP, white cell blood count, total cholesterol, albumin, creatinine, and d-dimer were quantified within 2 h of blood collection in all study participants.

EDTA plasma aliquots were stored at −80 °C for further analysis. One of these plasma aliquots was used for determination of IL-6, NT-proBNP, hs-cTnT, and sST2. All patient samples were measured in 1 batch approximately 1 month after the recruitment period. IL-6, NT-proBNP, and hs-cTnT were determined with chemiluminescent microparticle immunoassay on a Cobas e411 Hitachi (Roche Diagnostics) and CVs were 3.1% and 1.1% at mean values of 12 and 1966 pg/mL for IL-6, 4.2% and 1.6% at mean values of 44 and 2410 ng/L for NT-proBNP, and 5.2% and 1.3% at 14 and 300 ng/L for hs-cTnT. sST2 was measured on a BEP 2000 instrument (Siemens Diagnostics) with the Presage ST2 sandwich immunoassay (Critical Diagnostics), and the CVs were <4.0%, as previously reported by our group (11).

OUTCOME MEASURES

All patients with acute ischemic stroke received follow-up at 3 months (90 days). The primary end point was all-cause mortality at 90 days. The secondary end point was functional outcome according to the modified Ranking Scale (mRS) score at 90 days; “good” outcome was defined as mRS of 0–2, and “poor” outcome was defined as mRS of 3–5 or dead (12). All patients with acute ischemic stroke underwent a structured telephone interview by a stroke neurologist or a trained study nurse to identify all-cause mortality and stroke related functional outcome after 90 days. If patients could not be contacted directly, their relatives, caregivers, practitioners, or the staff of rehabilitation and nursing homes were interviewed. No patient was lost through follow-up.

STATISTICAL ANALYSIS

Data were analyzed with SPSS 13.0.0 software (SPSS Inc.) and the MedCalc 13.1.2.0 package (MedCalc Software). Dichotomous data are given as absolute numbers (percentage), and continuous variables are presented as median [interquartile range (IQR)] if not otherwise indicated. Univariate comparisons between groups were performed with the χ² test for categorical variables and with the nonparametric Mann–Whitney U-test for continuous variables (respective P values were not adjusted for multiple comparisons and are therefore descriptive only). ROC plots were constructed for NIHSS, IL-6, NT-proBNP, d-dimer, hs-cTnT, as well as sST2, and the areas under the curve (AUCs) for the prediction of death were calculated.

Univariate and multivariate Cox proportional hazards regression analyses were used to assess the prognostic value of several clinical and biochemical biomarkers. To report robust information in the multivariate models, we used a forward stepwise approach offering variables that were significant in univariate analyses. Owing to a cross-correlation between inflammatory markers, we used IL-6, being the strongest predictor, for multivariate analyses. As the primary approach for calculating univariate and multivariate risk ratios (RRs), we arbitrarily decided to dichotomize the relevant continuous variables according to the median values of the entire cohort. Furthermore, we used a secondary approach, where the NIHSS score was entered by point increment and all other continuous variables were log transformed and the RRs refer to a 1-SD increase in the log-transformed units. To report robust information in the multivariate analyses, we limited the number of baseline variables included in the multivariate models to established clinical and biochemical markers.

Kaplan–Meier estimates of the distribution of times from baseline to death were computed according to the median marker values and log-rank tests were performed to compare the survival curves between the groups.

To examine a possible combined effect of independent prognostics markers (i.e., NIHSS, IL-6, and NT-proBNP) we computed mortality rates in which study participants were stratified into quartiles of marker values. Further, we assessed a combined logistic regression model of NIHSS, IL-6, and NT-proBNP in ROC curve analysis and compared the AUC of the combined model with the AUCs to each marker alone according to the method of Hanley and McNeil 1983 (13).

Finally, we performed a simple combined multimarker approach stratifying the entire cohort according to median values of NIHSS, IL-6, and NT-proBNP. Using these cutoffs, we reported Kaplan–Meier curves of survival according to the presence of none, 1, 2, or 3 increased markers.

Results

TRIAL PROFILE AND PATIENT CHARACTERISTICS

During the recruitment period of 2 years 1264 patients were admitted to our stroke unit. Of these patients, 530 were excluded because of a final diagnosis of transient ischemic attack (n = 269), of nonstroke (n = 152), and of hemorrhagic stroke (n = 109). Further, 3 patients did not give an informed consent, and 10 patients did not have a baseline study blood sample available. Therefore, 721 patients with acute ischemic stroke were included in the present study.

Baseline patient characteristics of the 721 patients with acute ischemic stroke are shown in Table 1. The study comprised 374 (52%) men and 347 (48%) women with a median age of 76 years and median baseline NIHSS of 3 (IQR 2–7). The median time of stroke onset to time of blood draw was 13 h (IQR 4–19 h).

Table 1.

Baseline patient characteristics of all acute ischemic stroke patients and according to all-cause mortality.^a

	All (n = 721)	Survivors (n = 640)	Decedents (n = 81)	P valueb
Male sex, n (%)	374 (52%)	338 (53%)	36 (44%)	0.156
Age, years	76 (66–84)	75 (64–83)	82 (76–88)	<0.001
Body mass index, kg/m2	26 (24–28)	26 (24–28)	27 (22–28)	0.766
Stroke severity, NIHSS	3 (2–7)	3 (1–6)	14 (7–21)	<0.001
Thromobolysis (on admission)	177 (25%)	145 (24%)	32 (40%)	0.001
Arterial hypertension, n (%)	625 (87%)	558 (87%)	67 (83%)	0.264
Dyslipidemia, n (%)	379 (53%)	351 (55%)	28 (35%)	0.001
Diabetes mellitus, n (%)	172 (24%)	149 (23%)	23 (28%)	0.309
Current smoking, n (%)	76 (11%)	73 (12%)	3 (4%)	0.103
Coronary heart disease, n (%)	68 (9%)	59 (9%)	9 (11%)	0.787
Peripheral artery disease, n (%)	82 (11%)	67 (10%)	15 (19%)	0.084
Prior stroke, n (%)	176 (24%)	149 (23%)	27 (33%)	0.047
Arterial fibrillation, n (%)	226 (31%)	176 (28%)	50 (62%)	<0.001
Heart failure, n (%)	106 (15%)	90 (14%)	16 (20%)	0.352
Renal dysfunction, n (%)	165 (23%)	136 (21%)	29 (36%)	0.003
Stroke syndrome, OCSPc classification				<0.001
TACS, n (%)	58 (8%)	22 (3%)	36 (44%)
PACS, n (%)	372 (52%)	337 (53%)	35 (43)
LACS, n (%)	188 (26%)	182 (28%)	6 (7%)
POCS, n (%)	103 (14%)	99 (16%)	4 (5%)
Stroke etiology, TOAST subtype				0.001
Large-vessel occlusive	143 (20%)	130 (20%)	13 (16%)
Small-vessel occlusive	176 (24%)	167 (26%)	9 (11%)
Cardioembolic	268 (37%)	214 (33%)	54 (67%)
Other	16 (2%)	16 (3%)	0 (0%)
Unknown	118 (16%)	113 (18%)	5 (6%)
Stroke unit length of stay, days	2.4 (1.2–3.8)	2.6 (1.5–3.9)	1.7 (0.8–2.9)	<0.001
Secondary prevention
Antiplatelet use	486 (67%)	459 (72%)	27 (33%)	<0.001
Anticoagulant use	231 (32%)	192 (30%)	39 (48%)	0.001
Biochemical markers
CRP, mg/dL	0.4 (0.2–0.9)	0.3 (0.1–0.8)	1.0 (0.5–3.4)	<0.001
IL-6, pg/mL	7.0 (3.9–13.7)	6.2 (3.7–11.8)	24.2 (11.6–49.6)	<0.001
White blood cell count, ×109/L	7.9 (6.3–9.6)	7.8 (6.3–9.4)	9.3 (7.3–10.8)	<0.001
Total cholesterol, mg/dL	191 (160–221)	193 (161–225)	170 (147–199)	<0.001
Albumin, g/dL	4.01 (3.74–4.25)	4.05 (3.80–4.27)	3.73 (3.38–3.97)	<0.001
Creatinine, mg/dL	0.9 (0.8–1.1)	0.9 (0.8–1.1)	0.9 (0.7–1.2)	0.343
eGFR, mL/min/1.73 m2	76 (62–93)	77 (63–93)	70 (53–89)	0.013
d-Dimer, mg/L	0.95 (0.46–3.05)	0.86 (0.43–2.35)	3.84 (1.27–8.11)	<0.001
NT-proBNP, ng/L	447 (152–1563)	374 (132–1208)	2480 (910–4784)	<0.001
hs-cTnT, ng/L	14 (8–25)	13 (8–22)	27 (16–86)	<0.001
sST2, ng/mL	35 (28–50)	34 (27–46)	55 (36–120)	<0.001

a

Dichotomous data are given as absolute numbers (percentage), and continuous variables are presented as median (IQR). To convert concentrations in mg/dL to mmol/L multiply by 0.0884 for creatinine and by 0.02586 for cholesterol.

b

Univariate comparisons between survivors and decedents were performed with the χ² test for categorical variables and with the nonparametric Mann–Whitney U-test for continuous variables.

c

OCSP, Oxfordshire Community Stroke Project; TACS, total anterior circulation syndrome; PACS, partial anterior circulation syndrome; LACS, lacunar syndrome; POCS, posterior circulation syndrome; TOAST, Trial of Org 10172 in Acute Stroke Treatment.

PROGNOSTIC VALUE OF BASELINE MARKERS TO PREDICT ALL-CAUSE MORTALITY

Outcome evaluation of the acute ischemic stroke patients at 90 days showed an all-cause mortality rate of 11% (n = 81). IL-6, NT-proBNP, d-dimer, hs-cTnT, and sST2 plasma concentrations at baseline were significantly higher among decedents from all-causes than in survivors (Table 1).

ROC curves showing the ability of baseline NIHSS, IL-6, NT-proBNP, d-dimer, hs-cTnT, and sST2 to predict 90-day all-cause mortality in patients with acute ischemic stroke are depicted in Fig. 1. The highest AUC was found for the NIHSS (AUC, 0.88; 95% CI, 0.85–0.90), followed by IL-6 (AUC, 0.83; 95% CI, 0.80–0.86), NT-proBNP (AUC, 0.80; 95% CI, 0.77–0.83), d-dimer (AUC, 0.77; 95% CI, 0.74–0.80), hs-cTnT (AUC, 0.75; 95% CI, 0.72–0.78), and sST2 (AUC, 0.73; 95% CI, 0.69–0.76).

ROC plots demonstrating the ability of NIHSS and biomarkers at baseline to predict 90-day all-cause mortality in patients with acute ischemic stroke.

Fig. 1.

Cox proportional hazards regression analyses evaluating the prognostic value of clinical variables, NIHSS, and biomarkers are shown in Table 2. In univariate analyses with the markers dichotomized according to median values we showed that patients with NIHSS >3, IL-6 >7 pg/mL, NT-proBNP >447 ng/L, d-dimer >0.95 mg/L, hs-cTnT >14 ng/L, and sST2> 35 ng/mL at baseline had an increased risk of dying during follow-up (Table 2). In the multivariate model only increased NIHSS (RR 7.87; 95% CI, 3.61–17.16; P < 0.001), IL-6 (RR 4.09; 95% CI, 2.02–8.29; P < 0.001), and NT-proBNP (RR 4.88; 95% CI, 2.41–9.88; P < 0.001) remained independent mortality predictors (Table 2).

Table 2.

Results of Cox proportional hazards regression analyzing the effect of baseline variables on 90-day all-cause mortality patients with ischemic stroke using a dichotomized approach.

Variablea	Univariate analyses, RR (95% CI)b	P values	Multivariate model, RR (95% CI)c	P values
Sex	1.39 (0.90–2.15)	0.143
Age (>76 years)	3.34 (2.03–5.49)	<0.001
Body mass index (>26 kg/m2)	1.09 (0.70–1.68)	0.707
NIHSS (>3)	12.16 (5.60–26.41)	<0.001	7.87 (3.61–17.16)	<0.001
Thromobolysis (on admission)	2.13 (1.34–3.33)	0.001
Arterial hypertension	0.71 (0.40–1.26)	0.243
Dyslipidemia	0.46 (0.29–0.72)	0.001
Diabetes mellitus	1.28 (0.79–2.07)	0.322
Current smoking	0.31 (0.10–0.99)	0.047
Coronary heart disease	1.44 (0.84–2.47)	0.183
Peripheral artery disease	1.78 (1.03–3.07)	0.039
Prior stroke	1.57 (0.99–2.48)	0.057
Arterial fibrillation	3.82 (2.44–5.97)	<0.001
Heart failure	1.44 (0.84–2.67)	0.183
Renal dysfunction	1.99 (1.27–3.14)	0.003
IL-6 (>7 pg/mL)	8.81 (4.41–17.62)	<0.001	4.09 (2.02–8.29)	<0.001
Total cholesterol (>191 mg/dL)d	0.50 (0.32–0.79)	0.003
d-Dimer (>0.95 mg/L)	5.39 (3.03–9.59)	<0.001
NT-proBNP (>447 ng/L)	8.80 (4.40–17.60)	<0.001	4.88 (2.41–9.88)	<0.001
hs-cTnT (>14 ng/L)	4.64 (2.65–8.13)	<0.001
sST2 (>35 ng/mL)	3.77 (2.33–6.57)	<0.001

a

Age, body mass index, NIHSS, and the biochemical markers were dichotomized according to median values.

b

Univariate analyses.

c

Multivariate model using a forward stepwise approach (using an entry limit of P < 0.05).

d

To convert cholesterol concentrations in mg/dL to mmol/L multiply by 0.02586.

When using a continuous approach, we found similar result. In multivariate analysis NIHSS (RR 1.13; 95% CI, 1.10–1.17; P < 0.001) per point increment, IL-6 (RR 1.34; 95% CI, 1.09–1.63; P = 0.005) per 1-SD increase in the log-transformed units, and NT-proBNP (RR 1.48; 95% CI, 1.04–2.09; P = 0.028) per 1-SD increase in the log-transformed units remained independent prognostic markers.

Fig. 2 shows Kaplan–Meier curves according to median values of NIHSS (>3), IL-6 (>7 pg/mL), and NT-proBNP (>447 ng/L). Mortality was significantly higher in patients with increased NIHSS, IL-6, and NT-proBNP (log-rank test, P < 0.001, for each).

Kaplan–Meier plots showing survival according to increased NHISS (>3) (A), IL-6 (>7 pg/mL) (B), and NT-proBNP (>447 ng/L) (C) in acute ischemic stroke patients.

Fig. 2.

To illustrate how IL-6 and NT-proBNP add to the prognostic value of NIHSS, we computed mortality rates with the study participants stratified into quartiles of NIHSS and IL-6 as well as quartiles of NIHSS and NT-proBNP. As shown in Fig. 3, the prognostic impact of IL-6 and NT-proBNP was additive to NIHSS. Mortality rates were highest among patients in the highest quartiles of both NIHSS and IL-6 as well in the highest quartiles of both NIHSS and NT-proBNP. Further, ROC curve analyses combining NIHSS, IL-6, and NT-proBNP revealed that the combined AUC of 0.90 (95% CI, 0.88–0.92) was significantly higher when compared to each marker alone [vs NIHSS AUC of 0.88 (95% CI, 0.85–0.90), P = 0.004; vs IL-6 AUC of 0.83 (95% CI, 0.80–0.86), P = 0.001; vs NT-proBNP AUC of 0.80 (95% CI, 0.77–0.83), P < 0.001], respectively.

Rates of 90-day all-cause mortality in patients with acute ischemic stroke combining (A) NIHSS and IL-6, and (B) NIHSS and NT-proBNP.

Fig. 3.

NIHSS quartiles (Qs): Q1 < 2, Q2 2–3, Q3 > 3–6, Q4 > 6; IL-6 quartiles: Q1 < 4 pg/mL, Q2 4–7 pg/mL, Q3 > 7–14 pg/mL, Q4 > 14 pg/mL; NT-proBNP Qs: Q1 < 152 ng/L, Q2 152–447 ng/L, Q3 > 447–1550 ng/L, Q4 > 1550 ng/L.

PROGNOSTIC VALUE OF A MULTIMARKER APPROACH OF NIHSS, IL-6, AND NT-proBNP TO PREDICT ALL-CAUSE MORTALITY

Further, we performed a simple multimarker approach combining NIHSS, IL-6, and NT-proBNP. We stratified the entire cohort according to median values of NIHSS (>3), IL-6 (>7 pg/mL), and NT-proBNP (>447 ng/L). Fig. 4 displays the Kaplan–Meier curves of survival according to the presence of none, 1, 2, or 3 markers above the cutoff for the prediction of all-cause mortality (log-rank tests for trend, P < 0.001). Of the 721 acute ischemic stroke patients, 159 presented with no increased marker, 208 with 1, 192 with 2, and 162 with 3 increased markers at baseline. At 90 days, no patient with negative readings for all 3 markers had died. In patients with 1 marker increased death rates of 2% (n = 5) were observed. The patients with 2 markers increased displayed death rates of 8% (n = 15), and highest death rates of 38% (n = 61) were observed in patients with 3 markers increased.

Kaplan–Meier plot showing survival according to a combined marker approach in patients with acute ischemic stroke, where the entire cohort was stratified according to the number of increased markers (NIHSS >3, IL-6 >7 pg/mL, and NT-proBNP >447 ng/L).

Fig. 4.

PROGNOSTIC VALUE OF BASELINE MARKERS TO PREDICT FUNCTIONAL OUTCOME

Finally, the assessment of our secondary end point, revealed a poor functional outcome in 46% (n = 334) of the patients with acute ischemic stroke after 90 days. Median NIHSS, IL-6, and NT-proBNP at baseline were significantly higher among patients with poor functional outcome compared to patients with good outcome [NIHSS 6 (IQR 3–12) vs 2 (IQR 1–4); IL-6 12 pg/mL (IQR 6–25 pg/mL) vs 5 pg/mL (IQR 3–8 pg/mL); and NT-proBNP 1102 ng/L (IQR 378–3021 ng/L) vs 230 ng/L (IQR 90–716 ng/L); P < 0.001 for each]. In multivariate Cox proportional hazards regression analyses, NIHSS >3 (RR 2.19; 95% CI, 1.72–2.77; P < 0.001), IL-6 > 7 pg/mL (RR 1.74; 95% CI, 1.36–2.24; P < 0.001), and NT-proBNP >447 ng/L (RR 1.46; 95% CI, 1.11–1.92; P = 0.007) remained independent predictors of poor functional outcome.

Discussion

In the present study, we assessed the prognostic value of IL-6, d-dimer, NT-proBNP, hs-cTnT, and sST2 after acute ischemic stroke. In this large cohort of acute ischemic stroke patients, only IL-6, and NT-proBNP at admission were strong and independent prognostic biomarkers for 90-day all-cause mortality, and provided complementary prognostic information to the routinely used stroke severity score NIHSS. Using a simple multimarker approach combing NIHSS, IL-6, and NT-proBNP, we identified patients with no, intermediate, and very high risk for all-cause mortality at 90 days.

We rigorously examined a combination of inflammatory and cardiovascular biomarkers besides the routinely used NIHSS for mortality prediction in consecutive acute ischemic stroke patients admitted to our stroke unit. In doing so, we modeled each marker individually and collectively, using a dichotomized and a continuous approach as well as integrating results over time.

In our cohort of patients after acute ischemic stroke, all biomarkers were strong mortality predictors in univariate analyses. Only, IL-6 and NT-proBNP added independent prognostic value to the NIHSS in our multivariate analyses. Our findings are in line with previous data of Whiteley and colleagues demonstrating that IL-6 and NT-proBNP were the only independent prognostic markers in an evaluation of several blood biomarkers in patients after acute ischemic cerebrovascular events (6). Two metaanalyses have recently confirmed the independent prognostic value of IL-6 and NT-proBNP in patients after acute ischemic stroke (14, 15). However, both metaanalysis also reported a rather modest additional predictive value of IL-6 or NT-proBNP to the routinely used NIHSS (14, 15).

In the present study, we showed for the first time that the prognostic impact of IL-6 and NT-proBNP was additive and clinically relevant to the NIHSS. Using thresholds at median marker values (i.e., NIHSS >3, IL-6 >7 pg/mL, and NT-proBNP >447 ng/L), we developed a simple multimarker approach combining these 3 markers, and stratified patients according to the number of increased markers. None of the patients with all 3 markers below the threshold died during follow-up, whereas patients with all 3 markers above the threshold had the poorest outcome with a mortality rate of 38%. Thus, a combination of these 3 complementary markers might provide a valuable tool for risk stratification in patients with acute ischemic stroke.

At first glance, our findings seem somewhat in contrast to the data of the 2 recent metaanalyses (14, 15). Possible reason for the discrepancies between our study results and the data from the 2 metaanalyses are differences in stroke cohort (ischemic stroke vs transient ischemic attack, ischemic stroke and/or hemorrhagic stroke), different outcome measure (all-cause mortality vs functional outcome) as well as different marker approach (multimarker vs single-marker).

There are several reviews and expert opinions on prognostic stroke biomarkers that unanimously state that there currently is not a single candidate stroke biomarker that adds clinically relevant information to the stroke severity score NIHSS. However, they all speculate about the potential use of multimarker scores, incorporating biomarkers and clinical variables (4, 5, 16, 17).

Owing to the complexity of acute ischemic stroke, combining biomarkers reflecting different pathophysiological processes and/or other concomitant diseases might be a strategy to improve outcome prediction. We demonstrated that incorporating the 2 biomarkers IL-6 (reflecting inflammation) and NT-proBNP (reflecting cardiac dysfunction) together with the NIHSS (reflecting neurologic impairment) into a simple multimarker model showed improved risk stratification of patients after acute ischemic stroke.

Inflammation plays a key role in acute ischemic stroke (18). Cerebral ischemia triggers an inflammatory response characterized by the upregulation of IL-6 within the brain as well as in the peripheral blood (19), suggesting a plausible role of IL-6 as a biomarker of acute brain injury (6). In addition, IL-6 is also related to other factors such as poststroke infections (20). In contrast, the underlying mechanism of increased NT-proBNP in acute ischemic stroke remains less clear, and the ability of NT-proBNP to predict mortality is probably more related to concomitant cardiovascular disease, rather than ischemic stroke itself. The trigger for the release of NT-proBNP is cardiac overload and increased cardiac wall stretch (21). NT-proBNP is a strong prognostic biomarker in various cardiovascular diseases (heart failure, coronary artery disease, peripheral arterial disease, arterial fibrillation, stroke, etc.) (15, 21, 22). Increased natriuretic peptides have been associated with cardioembolic etiology as well as with infarct volume in ischemic stroke patients reinforcing the brain–heart link and the suggestion that cardiac factors, in particular heart failure, adversely influence stroke outcome (23, 24). In this context natriuretic peptides have also been shown to be useful to detect paroxysomal atrial fibrillation in patients with acute ischemic stroke (25).

We acknowledge, however, that we do not present detailed information on cardiac function, concomitant inflammatory diseases, or poststroke infections.

As a secondary end point we looked at a functional outcome according to mRS at 90 days. We found similar results compared with the primary end point of all-cause mortality, with NIHSS, IL-6, and NT-proBNP also being independent predictors for functional outcome. However, the RR we found were lower. In contrast to survival, functional outcome is probably more influenced by other factors such as brain plasticity. Further, being aware of the moderate reliability of the mRS our primary end point was the most objective and hardest end point all-cause mortality (26).

Interestingly, increased total cholesterol at baseline was associated with lower 90-day all-cause mortality in univariate analysis. A possible protective effect of increased total cholesterol in patients with acute ischemic stroke has been reported previously (27). However, in our study a reverse association of total cholesterol with outcome was not evident in multivariate analysis.

In conclusion, there is an unmet need of accurate prediction of outcome after acute ischemic stroke to guide early decision-making. In the present large cohort of well-characterized patients with acute ischemic stroke, we developed a novel multimarker model combining NIHSS, IL-6, and NT-proBNP, which allowed simple and accurate risk stratification. Our prototype multimarker approach needs further validation in independent cohorts of patients with acute ischemic stroke, and should ultimately also be implemented and tested in a decision-making process.

3 Nonstandard abbreviations

 
• NIHSS

  NIH Stroke Scale

 
• CRP

  C-reactive protein

 
• IL-6

  interleukin-6

 
• NT-proBNP

  amino-terminal pro–B-type natriuretic peptide

 
• hs-cTnT

  high-sensitivity cardiac troponin T

 
• sST2

  soluble ST2

 
• eGFR

  estimated glomerular filtration rate

 
• mRS

  modified Ranking Scale

 
• IQR

  interquartile range

 
• AUC

  area under the curve

 
• RR

  risk ratio.

Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: B. Dieplinger, Roche Diagnostics; T. Mueller, Roche Diagnostics.

Research Funding: None declared.

Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: No sponsor was declared.