a b s t r a c t

Background: Neutrophil to lymphocyte ratio serves as a powerful inflammatory marker for predicting car- diovascular events. Here, we investigate whether admission NLR is associated with hematoma volume, stroke se- verity, and 3-month outcomes in patients with acute intracerebral hemorrhage (ICH).

Methods: 352 patients with acute ICH were prospectively identified in this study. Demographic characteristics, lifestyle risk factors, NIHSS score, hematoma volumes, and other clinical features were recorded for all partici- pants. Patients was divided into quartiles based on the admission NLR levels (Q1: b 2.78; Q2: 2.78-4.08; Q3: 4.08-7.85; Q4: >= 7.85). Multivariable Linear regression models and logistic regression models were used to eval- uate the association between NLR and hematoma volume, admission severity, or the outcomes after ICH. Results: Median NIHSS scores for each quartile (Q1 to Q4) were 6.0, 6.0, 6.0, and 11.0 (P = .001), and median he- matoma volumes were 9.5, 9.3, 9.1, and 15.0 ml (P = .005), respectively. After adjusting the age, sex, and other potential risk factors, the patients in Q4 had higher NIHSS scores (P = .042) and larger hematoma volume (P =

.014). After 3-month follow-up, 148 poor outcomes (mRS, 3-6) and 47 All-cause deaths were documented. There were more patients with poor outcomes in Q4 than Q1. However, compared with the patients in Q1, those in Q4 were not associated with poor outcomes (P-trend = 0.379), and all-cause mortality (P-trend = 0.843) after ad- just for other risk factors.

Conclusions: Higher admission NLR are associated with larger hematoma volume and more serious stroke, but not 3-month outcomes in patients with acute ICH.

(C) 2016

1. Introduction

Intracerebral hemorrhage (ICH) accounts for 10-15% of all strokes. Although better treatments and nursing quality are applied, ICH still represents the most serious type of stroke with high morbidity and mortality [1,2]. Previous studies have been reported that the severe neurological deficit at presentation, large hematoma volume, hemato- ma growth, hematoma location and the presence of intraventricular bleeding are associated with poor outcomes of ICH patients [3-5].

Recently, a growing body of evidence supports that the inflammato- ry mechanisms were involved in the brain injury after ICH [6]. There

* Correspondence to: Y.-J. Cao, Department of Neurology, Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, 215004, Suzhou, Jiangsu, China.

?? Correspondence to: C.-F. Liu, Department of Neurology, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, China.

E-mail addresses: [email protected] (Y. Cao), [email protected] (C.-F. Liu).

¹ These authors contributed equally to this work.

were robust inflammatory responses, including peripheral leukocytes infiltration, microglia activation and subsequent release of various cyto- kines in ICH animal models [7,8]. A few clinical studies have demon- strated that the inflammatory markers were association with bad outcomes of ICH patients [9,10]. Compared to other inflammatory markers, leukocyte number is simpler and commonly used marker. Some studies indicated that elevated leukocyte level is associated with the large hematoma volume and bad outcomes in ICH patients [9-11].

Neutrophils and lymphocyte are the main components of the leuko- cytes. According to recent studies, the Neutrophil to lymphocyte ratio serves as a more powerful inflammatory marker than neutrophils or lymphocyte itself [12,13]. NLR has already been used to predict sub- clinical inflammation in patients with cancer [12] and also vascular dis- ease (including coronary artery disease and stroke) [13,14]. And a study led by Wang et al. found that high NLR is associated with high 30-day mortality in ICH patients [15]. A study in a 177 ICH patients, reported that high NLR predictor the bad outcome in 3-month [16]. However, whether NLR is correlated with hematoma volume, baseline National

http://dx.doi.org/10.1016/j.ajem.2016.11.037

0735-6757/(C) 2016

Institute of Health Stroke Scale (NIHSS) remains unknown. And wheth- er the relationship between NLR and 3-month outcomes and mortality of patients with ICH in china remains unclear. Thus, the aim of this study was to examine the associations between the NLR and hematoma volume, baseline NIHSS and outcomes in patients with acute ICH.

1. Methods
   1. Study design and patients enrolment

From November 2011 to March 2014, we prospectively identified acute ICH patients from the Second Affiliated Hospital of Soochow Uni- versity in China. The methods for recruiting study participants have been described elsewhere [17,18]. Briefly, all patients with computed tomography (CT) confirmed ICH was recruited. Patients with trauma, brain tumor, Hemorrhagic transformation of IS, and vascular cerebral malformations were excluded, and a total of 413 potentially eligible pa- tients were enrolled. Additional exclusion criteria were as follows: 1) re- quirement for neurosurgical procedures (n = 10); 2) time from onset to admission over 7 days (n = 11); 3) no Modified Rankin scale (mRS) score at 3-month (n = 27); and 4) no neutrophil and lymphocyte count (n = 13) measurements. 352 patients with available data for NLR were finally included in the analyses for this study (flowchart of participants selection; Fig. 1).

Ethics statement

This study was approved by the Ethics Committee of the Second Af- filiated Hospital of Soochow University, and informed consent was ob- tained from all the patients participating in this study.

Data collection

Demographic characteristics, lifestyle risk factors, medical history, clinical laboratory tests, imaging data (CT and magnetic resonance im- aging) were collected at the time of enrollment. All information was ob- tained using a standard questionnaire that was administered by the trained staff blinded to the Study objectives. Trained neurologists assessed the baseline stroke severity using the National Institutes of Health Stroke Scale score. Cigarette smokers were defined as having smoked at least one cigarette per day for 1 year or more. Using a standard mercury sphygmomanometer, Blood pressure mea- surements were performed in the supine position for admission. Blood samples were collected within 24 h of hospital admission. The WBC

Fig. 1. Flowchart of participants’ selection.

and differential counts were determined by the mindray, the NLR was calculated as the Neutrophil counts over Lymphocyte counts. All serum Biochemical parameters were analyzed enzymatically on an Olympus Au5400 automatic biochemical analyzer (First Chemical Co., LTD, Japan) using the commercial reagents.

Outcomes assessment

Hematoma volumes were calculated by two neuroradiologists who were blinded to the clinical data and follow-up CT scans using the for- mula ABC/2 [19]. Stroke severity was assessed with the NIHSS by trained neurologists. Modified Rankin Scale (mRS) scores were evaluat- ed at day 90 after onset, and poor outcomes were defined as mRS scores

>= 3 or death. Deaths were reported by family members or work associ- ates and/or obtained from death certificates and medical records.

Statistical analysis

Patients was divided into quartiles based on admission NLR levels (Q1: b 2.78; Q2: 2.78-4.08; Q3: 4.08-7.85; Q4: >= 7.85). Continuous var-

iables are expressed as mean +- standard deviation or median (inter- quartile range), whereas categorical variables are expressed as frequency (percent). For group comparisons, variance analysis was per- formed for continuous variables with a normal distribution. Wilcoxon rank-sum test was used for those with skewed distributions, and chi- square tests were applied for categorical variables. A Spearman correla- tion analysis was used to assess the correlation between admission NLR with hematoma volume and NIHSS score, as well as other related vari- ables such as age, sex, time from onset to admission, cigarette smoking, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol, history of hypertension, history of diabetes mellitus, and history of stroke. In addition, multiple linear regression analyses were performed to evaluate the relationships between NLR and hema- toma volume or NIHSS scores. The potential covariates such as age, sex, time from onset to admission, current smoking status, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, history of hypertension, history of diabetes mellitus, history of stroke, total cho- lesterol, and hematoma volume or baseline NIHSS score, were included in the multivariable model. Furthermore, multivariable non-conditional logistic regression models were used to assess the associations between NLR with 3-month poor outcomes and all-cause death among acute ICH patients. Odds ratios (ORs) and 95% confidence intervals (CIs) were cal- culated for each group, and the lowest quartile was used as the refer- ence. Tests for linear trend in the ORs across admission NLR quartiles were performed with the modeling NLR category as an ordinal variable. All P values were 2-tailed, and a significance level of 0.05 was used. All analyses were conducted using the SAS statistical software (version 9.2, Cary, North Carolina, USA).

1. Results
   1. Baseline characteristics of study participants

Complete data on conventional risk factors were available for 352 acute ICH patients (234 men and 118 women). The mean age of the par- ticipants was 64.2 +- 13.8 years (range: 21-96 years). The baseline char- acteristics were presented in Table 1. Patients with higher NLR have higher high density lipoprotein cholesterol, fasting plasma glucose, he- matoma volume, and baseline NIHSS score, but have lower triglyceride levels. There were no significant differences in other baseline demo- graphic and clinical parameters among the quartiles.

Correlation between NLR and hematoma volume or NIHSS scores

The spearman correlation analysis showed that the admission NLR positively correlated with the baseline NIHSS score (r = 0.125) and

Table 1

Characteristics of the study participants according to neutrophil lymphocyte ratio quartiles

Neutrophil lymphocyte ratio (NLR)

Characteristics	Q1, 87	Q2, 89	Q3, 88	Q4, 88	P-value
Range	b 2.78	2.78-4.08	4.08-7.85	>= 7.85
Median	2.17	3.35	5.46	13.55
Age, mean +- SD, y	62.40 +- 12.61	65.51 +- 14.16	62.63 +- 14.17	66.42 +- 14.10	0.129
Time from onset to admission, h	4.0 (2.0-18.0)	5.0 (3.0-24.0)	6.0 (3.0-24.0)	6.0 (4.0-15.0)	0.161
Male, No (%)	52 (59.77)	55 (61.80)	63 (71.59)	64 (72.73)	0.157
Current smoking, No (%)	17 (19.54)	10 (11.24)	24 (27.27)	20 (22.73)	0.056
SBP, mean +- SD, mm Hg	162.2 +- 26.1	169.1 +- 27.0	168.9 +- 26.4	170.7 +- 36.8	0.236
DBP, mean +- SD, mm Hg	92.7 +- 13.1	93.8 +- 17.4	95.9 +- 17.2	95.5 +- 17.7	0.546
TG, mmol/L	1.14(0.81-1.72)	1.11(0.80-1.57)	1.09(0.81-1.44)	0.90(0.71-1.21)	0.018
TC, mmol/L	4.63(3.96-5.34)	4.63(4.00-5.23)	4.67(4.13-5.53)	4.65(3.98-5.26)	0.802
LDL-C, mmol/L	2.68(2.18-3.40)	2.90(2.38-3.27)	2.90(2.33-3.63)	2.59(2.19-3.33)	0.091
HDL-C, mmol/L	1.19(0.97-1.49)	1.15(0.94-1.45)	1.29(1.05-1.48)	1.47(1.21-1.76)	b 0.001
FPG, mol/L	6.44 +- 1.99	6.08 +- 1.84	6.61 +- 3.13	7.59 +- 3.44	0.004
History of hypertension, no (%)	70 (80.46)	74 (83.15)	73 (82.95)	73 (82.95)	0.961
History of diabetes mellitus, no (%)	14 (16.09)	9 (10.11)	11 (12.50)	9 (10.23)	0.590
History of stroke, no (%)	15 (17.24)	20 (22.47)	16 (18.18)	13 (14.77)	0.606
Hematoma volume (ml)	9.5 (4.1-18.3)	9.3 (5.0-18.8)	9.1 (4.7-19.7)	15.0 (6.7-34.2)	0.005
Hematoma location, no (%)					0.053
Lobar	8 (9.20)	15 (16.85)	16 (18.18)	11 (12.50)
Basal Ganglia	49 (56.32)	44 (49.44)	37 (42.05)	36 (40.91)
Thalamus	10 (11.49)	8 (8.99)	5 (5.68)	4 (4.55)
Cerebellum	2 (2.30)	3 (3.37)	4 (4.55)	5 (5.68)
Brain stem	8 (9.20)	0 (0.0)	8 (9.09)	11 (12.50)
Intraventricular extension Baseline NIHSS score	10 (11.49) 6.0 (3.0-13.0)	19 (21.35) 6.0 (3.0-11.0)	18 (20.45) 6.0 (3.0-9.5)	21 (23.86) 11.0 (5.0-16.5)	0.001

Results are expressed with median (interquartile range) unless otherwise noted.

SBP: systolic blood pressure; DBP: diastolic blood pressure; TG: triglyceride; TC: total cholesterol; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprotein cholesterol; FPG, fasting plasma glucose; NIHSS, National Institute of Health Stroke Scale.

hematoma volume (r = 0.169) in acute ICH patients (P b .05 for both) (Table 2). In addition, admission NLR is also positively correlated to age, time from onset to admission, and fasting plasma glucose, but in- versely correlated to sex (P b .05 for all). Multivariable linear regression analyses of the associations between NLR and baseline NIHSS or hema- toma volumes scores are presented in Table 3. The patients in the highest NLR quartile had higher NIHSS scores (P = .042) and larger he- matoma volume (P = .014), after adjusted age, sex, time from onset to admission and other potential risk factors (Table 3).

Association between NLR with 3-month poor outcomes and all-cause death

After 3-month follow-up, 148 (42.0%) participants experienced poor functional outcomes (mRS, 3-6), and 47 (13.4%) patients died from all causes. As shown in Table 4, compared to the patients in the lowest quartile of admission NLR, the patients in the highest quartile were as- sociated with poor outcome (OR, 2.60; 95% CI, 1.41-4.79; P-trend =

0.008) and all-cause death (OR, 2.89; 95% CI, 1.25-6.71; P-trend = 0.005). However, these associations were no longer significant after adjusting for potential covariates including hematoma volume and baseline NIHSS score.

1. Discussion

This is the first study that demonstrating the relationships between NLR at admission and hematoma volume, baseline NIHSS and 3-month outcomes in patients with acute ICH. We found that patients with higher admission NLR had larger hematoma volumes and higher NIHSS scores. However, no signification correlation was observed be- tween NLR and 3-month all-cause mortality or disability.

Increasing evidence suggests that inflammatory mechanisms were involved in ICH-induced brain injury, including leukocytes, microglia and molecular (cytokines, proteases, and Reactive oxygen species ) components [8,20-23]. And leukocytes number was the most important inflammatory marker than others. Clinical studies also

Table 2

Spearman Correlation coefficients of neutrophil lymphocyte ratio with covariates among acute intracerebral hemorrhage patients.

Variables

Age

Sex

TFOA

Smoking

SBP

DBP

FPG

TC

HOH

HODM

HOS

NIHSS

Hematoma volume

Sex

NS

TFOA

NS

NS

Smoking

NS

-0.342

NS

SBP

NS

NS

-0.188

NS

DBP

-0.284

-0.164

-0.113

NS

0.635

FPG

NS

NS

-0.160

NS

0.202

0.109

TC

NS

0.149

NS

NS

0.155

0.120

0.179

HOH

NS

NS

NS

NS

0.210

0.215

0.188

NS

HODM

NS

NS

NS

NS

NS

NS

0.439

NS

NS

HOS

0.153

NS

NS

-0.108

NS

NS

NS

NS

NS

NS

Baseline NIHSS

0.233

NS

-0.362

NS

0.130

NS

0.280

NS

NS

NS

0.136

Hematoma volume

0.133

NS

-0.111

NS

NS

NS

0.227

NS

NS

NS

NS

0.524

NLR

0.110

-0.126

0.139

NS

NS

NS

0.192

NS

NS

NS

NS

0.125

0.169

NS, not significant; TFOA, time from onset to admission; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HOH, history of hy- pertension; HODM, history of diabetes mellitus; HOS, history of stroke; NIHSS, National Institutes of Health Stroke Scale; NLR, neutrophil lymphocyte ratio.

Table 3

Multivariate linear regression analyses for baseline NIHSS score and hematoma volume according to quartiles of NLR

Median value

(Interquartile ratio) ? SE P value

Baseline NIHSS score⁺

Q1	6.0 (3.0-13.0)	Reference
Q2	6.0 (3.0-11.0)	-1.11	0.91	0.225
Q3	6.0 (3.0-9.5)	-1.25	0.91	0.170
Q4 Hematoma volume?	11.0 (5.0-16.5)	1.92	0.94	0.042
Q1	9.5 (4.1-18.3)	Reference
Q2	9.3 (5.0-18.8)	1.50	4.48	0.737
Q3	9.1 (4.7-19.7)	3.86	4.47	0.401
Q4	15.0 (6.7-34.2)	11.51	4.67	0.014

⁺ Adjusted for age, sex, time from onset to admission, current smoking, SBP, fasting plasma glucose, history of hypertension, history of diabetes mellitus, history of stroke, to- tal cholesterol, location of hematoma, and hematoma volume (>=30 vs. b 30 ml).

^? Adjusted for age, sex, time from onset to admission, current smoking, SBP, fasting plasma glucose, history of hypertension, history of diabetes mellitus, history of stroke, to- tal cholesterol, location of hematoma, and baseline NIHSS score.

support that leukocytes play an important role in ICH. Early studies found that leukocyte counts in cerebrospinal fluid were frequently ele- vated after ICH. Two previous studies found that peripheral leukocyte counts were elevated in ICH patients and that the increased leukocyte level was associated with larger hematoma volume [9,10]. Some other studies also show that high leukocyte count was an independent predic- tor of early Neurologic deterioration and 3-month bad outcome in pri- mary ICH [11,22,24].

Animal studies also provide the evidence that leukocytes are in- volved in ICH-induced brain injury. Moreover, the researchers found that neutrophil infiltrated in and around the hematoma within less than 1 day after ICH, which reached the peak at 2-3 days in a rat autol- ogous blood model [21,25]. Activated neutrophils secrete and release a variety of proteolytic enzymes and pro-inflammatory proteases, which can damage brain tissue directly [26]. Some studies also found the in- volvement of lymphocyte in the ICH models [27]. In rat ICH model, Xue et al. study found that the lymphocytes in the rat’s brain increased significantly as early as 24 h after ICH-induction, with a maximum reaching at 3-7 days [28]. Other studies also found that CD4⁺ T lympho- cytes were the predominant brain leukocyte population in the ICH model [29]. Taken together, these findings indicate that leukocytes, es- pecially neutrophils and lymphocytes, may play important roles in ICH-induced Early brain injury.

The neutrophil to lymphocyte ratio, combined with neutrophils and lymphocyte counts, emerged as an independent and powerful

prognostic parameter in cancer [12,30]. Recent studies found that NLR, as a new marker of inflammation, had great significance in the progno- sis of cardiovascular diseases including stroke [13,14]. Tokgoz et al. re- ported that NLR, at the time of hospital admission, may act as a predictor of short-term mortality, independent of infarct volume in acute ischemic patients [31]. Hyun et al. showed that NLR can be a clin- ically significant predictor of the degree of carotid stenosis in male pa- tients with ischemic stroke [32]. Another study supported that NLR can predict the prognosis in acute ischemic stroke patients for endovascular therapies [33]. Taken together, most of the studies focus on ischemic stroke. However, few studies examined the relationship be- tween NLR and ICH. To the best of our knowledge, two studies demon- strated that higher NLR was associated with higher 30-day mortality in ICH patients [15,34]. Recently a interesting study from Italy demonstrat- ed that higher NLR predicted worse 3-month outcome in ICH patients [16]. However, whether NLR is correlated with hematoma volume, baseline NIHSS remains unknown. And whether there was a relation- ship between NLR and 3-month outcomes and mortality of patients with ICH in china remains unclear. In this study, we investigate the rela- tionship between NLR and hematoma volume, baseline NIHSS score, and outcome at 3 months. The present study found an NLR of 7.85 or higher at the time of admission were significantly associated with larger hematoma volume and higher NIHSS in patients with ICH. However, there were no significant association between NLR and the 3-months outcomes, different from Lattanzi et al.’s [16] study. These controversy results of NLR and the 3-month prognosis of ICH patients may be par- tially caused by different population, different inclusion criteria and sample size. Further long-term follow-up studies are required to verify these findings and clarify the potential biological mechanisms.

1. Limitations

This study has several limitations. First, this was an observational study and was performed in a single center, a causal relationship could not be established. Although several potential covariates were ad- justed in the multivariable models, we still could not rule out the possi- bility that other unmeasured or inadequately measured factors could confound the true associations. Also, this study was performed in a rel- atively small Chinese population, and the findings should be extrapolat- ed cautiously to other populations with different genetic backgrounds. Second, the cohort included some patients whose time from onset to admission exceeded 24 h. Therefore, the levels of neutrophil and lym- phocyte at admission might not accurately reflect the levels at stroke onset. Third, in our hospital, if the hematoma volume is larger than 30 ml, most patients will receive neurosurgical operations, which

Table 4

Odds ratios (ORs) and 95% confidence intervals (CIs) of 3-month outcome after acute intracerebral hemorrhage

Neutrophil lymphocyte ratio (NLR)

	Q1	Q2	Q3	Q4	Ptrend
Death or major disability
No. of cases (%)	32 (36.78)	35 (39.33)	28 (31.82)	53 (60.23)
Crude	1.00	1.11 (0.61-2.05)	0.80 (0.43-1.50)	2.60 (1.41-4.79)	0.008
Model 1	1.00	0.95 (0.47-1.93)	0.72 (0.34-1.49)	1.93 (0.92-4.03)	0.186
Model 2	1.00	1.07 (0.40-2.92)	0.93 (0.34-2.57)	1.83 (0.62-5.39)	0.379
Death No. of cases (%)	9 (10.34)	7 (7.87)	9 (10.23)	22 (25.00)
Crude	1.00	0.74 (0.26-2.08)	0.99 (0.37-2.62)	2.89 (1.25-6.71)	0.005
Model 1	1.00	0.55 (0.16-1.87)	0.89 (0.27-2.88)	1.34 (0.45-4.04)	0.418
Model 2	1.00	0.52 (0.12-2.33)	1.51 (0.34-6.61)	0.64 (0.15-2.76)	0.843
Major disability No. of cases (%)	23 (26.44)	28 (31.46)	19 (21.59)	31 (35.23)
Crude	1.00	1.28 (0.66-2.46)	0.77 (0.38-1.54)	1.51 (0.79-2.89)	0.446
Model 1	1.00	1.10 (0.53-2.28)	0.70 (0.32-1.53)	1.62 (0.76-3.45)	0.434
Model 2	1.00	1.20 (0.55-2.59)	0.83 (0.36-1.91)	1.46 (0.65-3.28)	0.565

Model 1: adjusted for age, sex, time from onset to admission, current smoking, SBP, fasting plasma glucose, history of hypertension, history of diabetes mellitus, history of stroke, and total cholesterol.

Model 2: further adjusted for location of hematoma, hematoma volume (>=30 vs. b30 ml), and baseline NIHSS score.

were excluded in this study. Fourth, the period of follow-up is relatively short, and the incidence of death is too low, this may limit our power to detect significant association between NLR and all-cause death. Further large-scale prospective studies with longer follow-up period are needed to validate our findings. Finally, a significant proportion of patients were excluded for a lack of neutrophil and lymphocyte count measurements, which may also contribute to selection bias.

1. Conclusions

NLR is a simple, inexpensive, and immediately obtainable biomark- er. If our observations that higher admission NLR is associated with larg- er hematoma volume and higher baseline NIHSS score could be confirmed, NLR may have an additional predictive value for the hemato- ma volume and neurological deficit in ICH patients.

Sources of funding

This work was supported in part by grants from the National Natural Science Foundation of China (81471195), Suzhou Clinical Research Cen- ter of neurological disease (Szzx201503), the Second Affiliated Hospital of Soochow University Preponderant Clinic Discipline Group Project Funding (XKQ2015002). This work was also partly supported by the Pri- ority academic program development of Jiangsu Higher Education In- stitutions (PAPD).

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

We thank the study participants and their relatives and the clinical staff for their support and contribution to this study.

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