High neutrophil counts before endovascular treatment for acute basilar artery occlusion predict worse outcomes

Abstract

Background and purpose:

Ischemic stroke is related to inflammation. We investigated leukocyte counts, neutrophil counts, and NLR (neutrophil-to-lymphocyte ratio) to explore their prognostic potential and determine if high neutrophil counts before endovascular treatment (EVT) in patients with acute basilar artery occlusion (BAO) are associated with worse outcomes at 90 days post-EVT.

Methods:

Leukocyte and neutrophil counts and NLR were determined in eligible patients from the Acute Basilar Artery Occlusion Study (BASILAR). Patients were divided into four groups according to leukocyte and neutrophil counts and NLR quartiles. The primary outcome was a favorable outcome based on the modified Rankin Scale (mRS: 0–3). The secondary outcome was functional independence (mRS 0–2). The safety outcome was mortality, and an unfavorable outcome was mRS 4–6. Successful reperfusion was mTICI (modified Thrombolysis in Cerebral Infarction) of 2b or 3. All the data were collected within 90 days after EVT.

Results:

We enrolled 586 patients in the study. The leukocyte and neutrophil counts and NLR were significantly associated with clinical outcomes in all patients though no effects were seen in some intervals. Of these three parameters, the neutrophil count had the most significant impact, negatively affecting the outcome. The findings were similar in patients who were successfully recanalized.

Conclusion:

Higher neutrophil counts predicted worse clinical outcomes 90 days after EVT. This finding supports the deleterious role of inflammation in patients with acute BAO despite EVT or successful recanalization.

Introduction

Stroke leads to disability and death, causing substantial economic and social burdens (Di Carlo, 2009; Wang et al., 2016). Strokes can be ischemic or hemorrhagic, with the former accounting for 85% of strokes (Rammal and Almekhlafi, 2016). The inflammatory response has been increasingly considered a crucial factor in the events leading to an acute ischemic stroke (AIS) (Iadecola and Anrather, 2011; Kim et al., 2016). Infiltration of leukocytes, especially neutrophils, into the ischemic areas is vital for inflammation (Ishikawa et al., 2004).

After a cerebral infarction, neutrophils are recruited into the ischemic areas representing the early stage of inflammation (Herz et al., 2015). Inflammatory factors such as cytokines, chemokines, and matrix metalloproteinase-9 (MMP-9) released by neutrophils cause oxidative stress, platelet accumulation, and damage to the blood-brain barrier, accelerating the devastation of the brain (Montaner et al., 2001; Castellanos et al., 2003; Shichita et al., 2012; Jickling et al., 2015). Neutrophils are also involved in angiogenesis, neuroplasticity, and neurogenesis, impacting recovery (Perez-de-Puig et al., 2015). Studies using radiolabeled neutrophils have demonstrated their accumulation in the injured zone as the infarct expands. The increasing neutrophil counts are related to the severity and mortality of stroke (Price et al., 2004; Kim et al., 2012; Mo et al., 2013). A dynamic increase in post-stroke neutrophils significantly predicted death or severe disability in patients with AIS treated with recombinant tissue plasminogen activator (r-tPA) for 3 months (Shi et al., 2018). Higher neutrophil counts had a deleterious effect on both the recovery (despite successful recanalization) and recurrence of cerebral infarction in patients with a high-risk TIA or mild ischemic stroke (Zhu et al., 2018b; Boisseau et al., 2019).

In contrast to neutrophils, lymphocyte counts are relatively reduced in AIS patients due to stress-induced corticosteroid response affecting the resolution of inflammation (Jickling et al., 2015). Therefore, the neutrophil to lymphocyte ratio (NLR) might be a promising inflammation biomarker (Zahorec, 2001; Walsh et al., 2005). An elevated NLR is detrimental to the three-month outcome in AIS patients (Brooks et al., 2014; Shi et al., 2018). It is also related to clinical outcomes in stroke patients (Gökhan et al., 2013; Tokgoz et al., 2013; Celikbilek et al., 2014).

However, the published clinical studies have mainly focused on acute anterior circulation ischemic stroke. Hence we sought to evaluate the predictive ability of admission leukocyte count, including neutrophils and NLR, in patients with acute basilar artery occlusion (BAO) treated with endovascular therapy.

Materials and methods

Study design and patients selection

Acute Basilar Artery Occlusion Study (BASILAR) was a multicenter prospective clinical registry in China that enrolled patients aged 18 years or older with acute symptomatic and radiological basilar artery occlusion within 24 h of the estimated onset time from January 2014 to May 2019 (Zi et al., 2020). Detailed information on the inclusion and exclusion criteria has been previously published (Zi et al., 2020). In this study, we only included patients who underwent standard medical treatment (SMT) plus endovascular treatment (EVT) (defined as the EVT group). SMT included intravenous thrombolysis, antiplatelet therapy, anticoagulation, or combinations of these treatments. EVT included mechanical thrombectomy, balloon angioplasty, thrombus aspiration, stenting, or a combination of these methods. The therapies each patient underwent relied on the discretion of local interventionists. The research protocol was approved by the ethics committees at all the involved study centers. And informed consent to participate in this study was obtained from the patients.

Variables and data collection

The data collected included demographic features, stroke risk factors, stroke severity at admission, mechanisms underlying ischemia, occlusion sites, crucial time metrics, and clinical outcomes. The neurological deficit at admission was measured by the National Institutes of Health Stroke Scale (NIHSS) (Brott et al., 1989). The posterior circulation Alberta Stroke Program Early Computed Tomography Score (pc-ASPECTS) indicated the ischemic change at baseline (Puetz et al., 2008). The posterior circulation collateral score (PC-CS) derived from the presence of potential collateral pathways on computed tomography angiography was used to calculate the collateral circulation status (van der Hoeven et al., 2016). The Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification stratified the stroke etiology (Adams et al., 1993). The modified Thrombolysis in Cerebral Infarction (mTICI) scale was used to grade reperfusion (Zaidat et al., 2013). The included patients (n = 586) were divided into four subgroups based on the leukocyte and neutrophil counts and NLR quartiles.

Outcome measures

We assessed the clinical outcomes at 90 days after EVT using the modified Rankin Scale (mRS) calculated by local neurologists blinded to the research procedures. The primary outcome was mRS 0–3, while the secondary outcome was functional independence with mRS 0–2 at 90 days post-EVT. The safety outcome was the incidence of death within 90 days. At 90 days, mRS 4–6 was considered an unfavorable outcome. Successful reperfusion was measured as mTICI of 2b or 3.

Statistical analysis

The leukocyte and neutrophil counts and NLR were divided into quartiles as categorical variables. They also were analyzed as continuous variables. The categorical and continuous variables with non-normal distributions were analyzed using the Chi-square and Kruskal–Wallis tests, respectively. The categorical variables were expressed as frequencies and percentages, while the continuous variables were expressed as the median and interquartile range (IQRs). We performed univariable and multivariable logistic regression analyses to assess the associations between essential parameters (as categorical and continuous variables) and clinical outcomes. The following confounding factors were adjusted: age, sex, hypertension, diabetes mellitus, baseline NIHSS score, baseline pc-ASPECTS, PC-CS score, stroke etiology, occlusion sites, and pass. We also examined the relationship between essential parameters and outcomes in a subgroup with successful reperfusion for sensitivity purposes. The same confounders were included.

Generalized linear models were used for subgroup analyses of the neutrophil quartiles to explore the heterogeneity in the associations between clinical characteristics and mRS as a continuous variable at 90 days after EVT in all patients. Models were adjusted for sex, age, baseline NIHSS, baseline pc-ASPECTS, PC-CS score, stroke etiology, occlusion sites, onset to treatment time. The median values were chosen for the threshold age, baseline NIHSS, baseline pc-ASPECTS, PC-CS score, and OTT.

We used Medcalc 20.0.22 to calculate the receiver operating characteristic curve (ROC curve) and establish optimal cutoff points to identify the most effective predictors of clinical outcomes at 90 days post EVT and successful recanalization. The distribution of the mRS scores at 90 days in patients with EVT and successful reperfusion based on the neutrophil quartiles were presented using Microsoft Excel 16.57. STATA 16 (Stata CorpLLC, TX, United States) was used to draw margin plots showing the interactions between neutrophils and clinical outcomes. All statistical analyses were performed using IBM SPSS 25 (IBM Corp., Armonk, NY, United States). The significance level was defined as the two-tailed p < 0.05.

Results

Baseline characteristics

We included 586 patients who had complete records of leukocyte and neutrophil counts and NLR from the EVT group. Of these patients, 479 (81.7%) were successfully recanalized. In the recanalized patients at 90 days, mRS 0–3 was noted in 178 (37.2%), and functional independence (mRS 0–2) in 151 (31.5%) patients, while 176 (36.7%) patients had died. Supplementary Table 1 summarizes the clinical features of 147, 147, 146, and 146 patients in the four quartiles based on leukocyte and neutrophil counts and NLR. The clinical outcomes were significantly different in the different groups (p < 0.05) (Table 1). Supplementary Table 2 presents the basic characteristics of patients with favorable and unfavorable outcomes. Leukocyte and neutrophil counts and NLR as continuous variables significantly affected the outcomes in patients with favorable vs. unfavorable outcomes (p < 0.001).

Associations of spotlighted parameters with clinical outcomes

All patients

In the unadjusted analysis, in patients with mRS 0–2 at 90 days, all the parameters deterred patients from independence (p < 0.05) (Table 2). In the adjusted analysis, except for the neutrophil counts in the interval 9.15–11.96 (10⁹/L) (p = 0.055), all other parameters had unfavorable effects on patients (Table 2).

In patients with mRS 0–3, the unadjusted analysis found all parameters except NLR at 4.96–7.93 (p = 0.200) were negatively associated with favorable outcomes, while in the adjusted analysis, neutrophil counts at 9.15–11.96 (×10⁹/L) and NLR at 4.96–7.93 were not associated with favorable outcomes (Table 3). In the unadjusted analysis, leukocyte counts at 8.40–11.06 (×10⁹/L), neutrophil counts at 9.15–11.96 (×10⁹/L), and NLR at 4.96–7.93 as categorical variables, and NLR as a continuous variable did not affect mortality (Table 4). In the adjusted analysis, except leukocyte count at 8.40–11.06 (×10⁹/L) and 11.06–13.66 (×10⁹/L), neutrophil count at 6.60–9.15 (×10⁹/L) and 9.15–11.96 (×10⁹/L), and NLR at 4.96–7.93, the others promoted death (Table 4).

Sensitivity analyses in patients with successful recanalization

In all patients who underwent EVT, increasing counts of leukocytes and neutrophils and NLR had detrimental effects on recovery after stroke and promoted mortality. Sensitivity analyses were performed in patients with successful recanalization defined as mTICI of 2b or 3. As seen in all patients, leukocyte and neutrophil counts and NLR as categorical variables in several intervals and continuous variables had an unfavorable influence on some clinical outcomes (Tables 2–4).

Based on receiver operating characteristic curves and the area under the curve assessing leukocyte and neutrophil counts and NLR predictive of clinical outcomes, the neutrophil count was found to be the best predictor of mRS 0–2, mRS 0–3, and mortality at 90 days after EVT (Figure 1 and Supplementary Figure 1). The optimal cutoff counts for mRS 0–2, mRS 0–3, and mortality at 90 days were 8.06 × 10⁹/L (55.9% sensitivity and 66.8% specificity), 7.23 × 10⁹/L (46.0% sensitivity and 76.8% specificity), and 7.52 × 10⁹/L (73.9% sensitivity and 41.1% specificity), respectively. Similar results were obtained in successfully recanalized patients with optimal cutoff neutrophil counts of 8.06 × 10⁹/L (55.0% sensitivity and 66.5% specificity), 7.23 × 10⁹/L (45.5% sensitivity and 76.4% specificity), and 10.93 × 10⁹/L (43.8% sensitivity and 72.9% specificity), respectively. Neutrophil counts as dichotomous variables were significantly associated with a lower incidence of mRS 0–2 and mRS 0–3 and a greater risk of mortality in all patients and those who were successfully recanalized (Supplementary Table 3).

FIGURE 1

Unfavorable outcomes were noted in 301 patients accounting for 62.8% of all the successfully recanalized patients (479). In these patients, high neutrophil counts led to unfavorable outcomes (Supplementary Table 4).

The rate of favorable outcomes was best with neutrophil count < 6.60 × 10⁹/L. It was lower with higher neutrophil counts, and significantly lower with counts > 11.96 × 10⁹/L (50.3% vs. 29.9% vs. 28.8% vs. 19.9% for subgroups < 6.60 vs. 6.60–9.15 vs. 9.15–11.96 vs >11.96 × 10⁹/L respectively, p < 0.001, Figure 2A and Table 1). Similar results were seen in patients with successful recanalization (Figure 2B and Supplementary Table 5).

FIGURE 2

The probability of a favorable outcome declined while mortality increased as the neutrophil count increased in all patients and those who were successfully recanalized (Figure 3).

FIGURE 3

Subgroup analyses

Subgroup analyses in all patients with neutrophil counts >11.96 × 10⁹/L stratified by clinical characteristics were most likely to go from a lower to higher mRS score. However, in male patients, >64 years old, with baseline NIHSS ≤27 or >27, baseline pc-ASPECTS >8, PC-CS score >4 or OTT ≤6 h, neutrophil counts in the intervals 6.60–9.15 (×10⁹/L) or 9.15–11.96 (×10⁹/L) were unfavorable for the decrease of mRS score (Table 5).

Discussion

We evaluated the effects of leukocytes, neutrophils, and the ratio (NLR) on clinical outcomes in patients with acute BAO who underwent EVT. Although all three parameters affected outcomes, neutrophils most significantly impacted mRS 0–2, mRS 0–3, and mortality within 90 days after EVT. The optimal cutoff neutrophil counts that best discriminated these clinical outcomes were 8.06 × 10⁹/L (mRS 0–2), 7.23 × 10⁹/L (mRS 0–3), and 7.52 × 10⁹/L (mortality). Higher neutrophil counts were associated with more unfavorable outcomes.

This nationwide multicenter study in China included patients who underwent EVT for BAO. Blood samples were drawn before treatment. Since neutrophil and leukocyte counts are estimated as a part of the routine blood work, evaluating them as predictors of inflammation and clinical outcomes did not incur additional costs.

However, some significant limitations of the study should be taken into consideration. First, neutrophil counts were determined only at admission. The kinetics of neutrophils during the stroke and their influence on the clinical outcomes should be assessed. Previous studies have shown that an increase in leukocytes or neutrophils was significantly associated with unfavorable outcomes in patients with ischemic stroke (Grau et al., 2004; Shi et al., 2018). However, one study indicated that neutrophils taken within 12 h after the onset of stroke affected the ischemic infarction size measured by diffusion-weighted MR imaging (DWI), suggesting an association between early proinflammation and deterioration of AIS (Buck et al., 2008). Second, only mRS was evaluated as a clinical outcome. Whether neutrophils affect other clinical outcomes needs to be evaluated. Third, the time of venipuncture could influence the serum sample. In our study, the time from stroke onset to blood sample collection was not documented. Fourth, there might be residual bias because of a pre-existing infection, trauma, or tumor. A pre-existing infection has been shown to result in worse outcomes in young adults with AIS (Heikinheimo et al., 2013). Finally, it was hard for us to validate a cause-result relationship between neutrophil counts and clinical outcomes because of the observational nature of the study.

Our study indicated that higher neutrophils predicted worse outcomes regardless of the underlying cause for the increase in neutrophils. Inflammatory factors released by neutrophils play an essential role in increasing the neutrophil counts, leading to endothelium damage and platelet aggregation impairing the microcirculation (Ernst et al., 1987; Borissoff and ten Cate, 2011; Döring et al., 2015). In addition, neutrophil recruitment and parietal fibrin deposition are detected in the microvasculature during AIS, termed downstream microvascular thromboinflammation (DMT), which contributes to incomplete reperfusion (del Zoppo and Mabuchi, 2003; Desilles et al., 2015, 2017, 2018). Cerebral thrombus consists of neutrophils and NETs (neutrophil extracellular traps), and the NETs affect tPA-induced reperfusion resistance (Laridan et al., 2017; Ducroux et al., 2018). Animal studies evidenced that pharmacologically induced neutropenia has a neuroprotective role in rats with experimental ischemic stroke (Pétrault et al., 2005). Similar findings were reported in experimental models investigating neutrophilic inhibition through the neutrophil inhibitory factor (Jiang et al., 1995). Consistent with these reports, we found that neutrophils negatively affected clinical outcomes despite successful recanalization. Moreover, the disruption of the blood–brain barrier (BBB) and transmigration of neutrophils reinforced each other (Tang et al., 2014; Jickling et al., 2015).

Our findings are consistent with previous reports as follows suggesting a significant relationship between neutrophils and clinical outcomes in patients with anterior and posterior circulation or only anterior circulation ischemic stroke. Leukocytes, neutrophils, and NLR were positively associated with clinical outcomes in patients treated with intravenous thrombolysis (Malhotra et al., 2018). Similarly elevated neutrophil counts promoted worse outcomes and the recurrence of stroke (Maestrini et al., 2015; Zhu et al., 2018a). However, these studies evaluated neutrophils as a continuous variable or one of the binary categorical variables. In contrast, we divided the neutrophil counts into quartiles and found that neutrophils played the most crucial role in predicting clinical outcomes. However, neutrophil counts in the interval 9.15–11.96 (×10⁹/L) showed no association with mRS 0–2 and mRS 0–3. This could be due to uncertain confounders leading to a lack of statistical power, affecting the actual association between this interval and outcomes. Moreover, we did not consider the seasonal and diurnal fluctuations in neutrophil counts and pre-existing infections, which need to be further studied (Fisch and Freedman, 1975; Woodhouse et al., 1994). Published studies focusing on the leukocyte and neutrophil quartiles and other subgroups of leukocytes have reported similar effects of neutrophils on the recurrence of ischemic events (Grau et al., 2004; Zhu et al., 2018b).

Based on our findings, neutrophils could be a valuable 90-days prognostic factor in patients with acute BAO treated with EVT. However, we are not suggesting neutrophil counts at admission as the most vital determinant of whether a patient should receive EVT or not. The prognostic potential of neutrophils still needs to be further validated in various clinical settings.

In conclusion, leukocytes, neutrophils, and NLR were significantly associated with a lower likelihood of favorable outcomes and increased odds of unfavorable outcomes. Among them, neutrophils contributed most to these clinical outcomes. Additionally, neutrophils might serve as potential neuroprotective targets in these patients.

References

• 1

  AdamsH. P.BendixenB. H.KappelleL. J.BillerJ.LoveB. B.GordonD. L.et al (1993). Classification of subtype of acute ischemic stroke. definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment.Stroke2435–41. 10.1161/01.STR.24.1.35

  • CrossRef
  • Google Scholar

• 2

  BoisseauW.DesillesJ.-P.FahedR.KyhengM.ZuberK.SabbenC.et al (2019). Neutrophil count predicts poor outcome despite recanalization after endovascular therapy.Neurology93e467–e475. 10.1212/WNL.0000000000007859

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  BorissoffJ. I.ten CateH. (2011). From neutrophil extracellular traps release to thrombosis: an overshooting host-defense mechanism?J. Thromb. Haemost.91791–1794. 10.1111/j.1538-7836.2011.04425.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  BrooksS. D.SpearsC.CummingsC.VanGilderR. L.StinehartK. R.GutmannL.et al (2014). Admission neutrophil-lymphocyte ratio predicts 90 day outcome after endovascular stroke therapy.J. Neurointerv. Surg.6578–583. 10.1136/neurintsurg-2013-010780

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  BrottT.AdamsH. P.OlingerC. P.MarlerJ. R.BarsanW. G.BillerJ.et al (1989). Measurements of acute cerebral infarction: a clinical examination scale.Stroke20864–870. 10.1161/01.STR.20.7.864

  • CrossRef
  • Google Scholar

• 6

  BuckB. H.LiebeskindD. S.SaverJ. L.BangO. Y.YunS. W.StarkmanS.et al (2008). Early neutrophilia is associated with volume of ischemic tissue in acute stroke.Stroke39355–360. 10.1161/STROKEAHA.107.490128

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  CastellanosM.LeiraR.SerenaJ.PumarJ. M.LizasoainI.CastilloJ.et al (2003). Plasma metalloproteinase-9 concentration predicts hemorrhagic transformation in acute ischemic stroke.Stroke3440–46. 10.1161/01.STR.0000046764.57344.31

  • CrossRef
  • Google Scholar

• 8

  CelikbilekA.IsmailogullariS.ZararsizG. (2014). Neutrophil to lymphocyte ratio predicts poor prognosis in ischemic cerebrovascular disease.J. Clin. Lab. Anal.2827–31. 10.1002/jcla.21639

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  del ZoppoG. J.MabuchiT. (2003). Cerebral microvessel responses to focal ischemia.J Cereb. Blood Flow Metab.23879–894. 10.1097/01.WCB.0000078322.96027.78

  • CrossRef
  • Google Scholar

• 10

  DesillesJ.-P.LoyauS.SyvannarathV.Gonzalez-ValcarcelJ.CantierM.LouedecL.et al (2015). Alteplase reduces downstream microvascular thrombosis and improves the benefit of large artery recanalization in stroke.Stroke463241–3248. 10.1161/STROKEAHA.115.010721

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  DesillesJ.-P.SyvannarathV.Di MeglioL.DucrouxC.BoisseauW.LouedecL.et al (2018). Downstream microvascular thrombosis in cortical venules is an early response to proximal cerebral arterial occlusion.J. Am. Heart Assoc.7:e007804. 10.1161/JAHA.117.007804

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  DesillesJ.-P.SyvannarathV.OllivierV.JournéC.DelboscS.DucrouxC.et al (2017). Exacerbation of thromboinflammation by hyperglycemia precipitates cerebral infarct growth and hemorrhagic transformation.Stroke481932–1940. 10.1161/STROKEAHA.117.017080

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  Di CarloA. (2009). Human and economic burden of stroke.Age Ageing384–5. 10.1093/ageing/afn282

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  DöringY.DrechslerM.SoehnleinO.WeberC. (2015). Neutrophils in atherosclerosis: from mice to man.Arterioscler. Thromb. Vasc. Biol.35288–295. 10.1161/ATVBAHA.114.303564

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  DucrouxC.Di MeglioL.LoyauS.DelboscS.BoisseauW.DeschildreC.et al (2018). Thrombus neutrophil extracellular traps content impair tpa-induced thrombolysis in acute ischemic stroke.Stroke49754–757. 10.1161/STROKEAHA.117.019896

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  ErnstE.HammerschmidtD. E.BaggeU.MatraiA.DormandyJ. A. (1987). Leukocytes and the risk of ischemic diseases.JAMA2572318–2324. 10.1001/jama.1987.03390170074031

  • CrossRef
  • Google Scholar

• 17

  FischI. R.FreedmanS. H. (1975). Smoking, oral contraceptives, and obesity, Effects on white blood cell count.JAMA234500–506. 10.1001/jama.1975.03260180040020

  • CrossRef
  • Google Scholar

• 18

  GökhanS.OzhaseneklerA.Mansur DurgunH.AkilE.UstündagM.OrakM. (2013). Neutrophil lymphocyte ratios in stroke subtypes and transient ischemic attack.Eur. Rev. Med Pharmacol. Sci.17653–657.

  • Google Scholar

• 19

  GrauA. J.BoddyA. W.DukovicD. A.BuggleF.LichyC.BrandtT.et al (2004). Leukocyte count as an independent predictor of recurrent ischemic events.Stroke351147–1152. 10.1161/01.STR.0000124122.71702.64

  • CrossRef
  • Google Scholar

• 20

  HeikinheimoT.BromanJ.HaapaniemiE.KasteM.TatlisumakT.PutaalaJ. (2013). Preceding and poststroke infections in young adults with first-ever ischemic stroke: effect on short-term and long-term outcomes.Stroke443331–3337. 10.1161/STROKEAHA.113.002108

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  HerzJ.SabellekP.LaneT. E.GunzerM.HermannD. M.DoeppnerT. R. (2015). Role of neutrophils in exacerbation of brain injury after focal cerebral ischemia in hyperlipidemic mice.Stroke462916–2925. 10.1161/STROKEAHA.115.010620

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  IadecolaC.AnratherJ. (2011). The immunology of stroke: from mechanisms to translation.Nat. Med.17796–808. 10.1038/nm.2399

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  IshikawaM.ZhangJ. H.NandaA.GrangerD. N. (2004). Inflammatory responses to ischemia and reperfusion in the cerebral microcirculation.Front. Biosci.9:1339–1347. 10.2741/1330

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  JiangN.MoyleM.SouleH. R.RoteW. E.ChoppM. (1995). Neutrophil inhibitory factor is neuroprotective after focal ischemia in rats.Ann. Neurol.38935–942. 10.1002/ana.410380615

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  JicklingG. C.LiuD.AnderB. P.StamovaB.ZhanX.SharpF. R. (2015). Targeting neutrophils in ischemic stroke: translational insights from experimental studies.J. Cereb. Blood Flow Metab.35888–901. 10.1038/jcbfm.2015.45

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  KimJ.SongT.-J.ParkJ. H.LeeH. S.NamC. M.NamH. S.et al (2012). Different prognostic value of white blood cell subtypes in patients with acute cerebral infarction.Atherosclerosis222464–467. 10.1016/j.atherosclerosis.2012.02.042

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  KimJ. Y.ParkJ.ChangJ. Y.KimS.-H.LeeJ. E. (2016). Inflammation after ischemic stroke: the role of leukocytes and glial cells.Exp. Neurobiol.25241–251. 10.5607/en.2016.25.5.241

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  LaridanE.DenormeF.DesenderL.FrançoisO.AnderssonT.DeckmynH.et al (2017). Neutrophil extracellular traps in ischemic stroke thrombi.Ann. Neurol.82223–232. 10.1002/ana.24993

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  MaestriniI.StrbianD.GautierS.HaapaniemiE.MoulinS.SairanenT.et al (2015). Higher neutrophil counts before thrombolysis for cerebral ischemia predict worse outcomes.Neurology851408–1416. 10.1212/WNL.0000000000002029

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  MalhotraK.GoyalN.ChangJ. J.BroceM.PandhiA.KerroA.et al (2018). Differential leukocyte counts on admission predict outcomes in patients with acute ischaemic stroke treated with intravenous thrombolysis.Eur. J. Neurol.251417–1424. 10.1111/ene.13741

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  MoX.LiT.JiG.LuW.HuZ. (2013). Peripheral polymorphonuclear leukocyte activation as a systemic inflammatory response in ischemic stroke.Neurol. Sci.341509–1516. 10.1007/s10072-013-1447-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  MontanerJ.Alvarez-SabínJ.MolinaC.AnglésA.AbilleiraS.ArenillasJ.et al (2001). Matrix metalloproteinase expression after human cardioembolic stroke: temporal profile and relation to neurological impairment.Stroke321759–1766. 10.1161/01.STR.32.8.1759

  • CrossRef
  • Google Scholar

• 33

  Perez-de-PuigI.Miró-MurF.Ferrer-FerrerM.GelpiE.PedragosaJ.JusticiaC.et al (2015). Neutrophil recruitment to the brain in mouse and human ischemic stroke.Acta Neuropathol.129239–257. 10.1007/s00401-014-1381-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  PétraultO.OukT.GautierS.LapraisM.GeléP.BastideM.et al (2005). Pharmacological neutropenia prevents endothelial dysfunction but not smooth muscle functions impairment induced by middle cerebral artery occlusion.Br. J. Pharmacol.1441051–1058. 10.1038/sj.bjp.0706124

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  PriceC. J. S.MenonD. K.PetersA. M.BallingerJ. R.BarberR. W.BalanK. K.et al (2004). Cerebral neutrophil recruitment, histology, and outcome in acute ischemic stroke: an imaging-based study.Stroke351659–1664. 10.1161/01.STR.0000130592.71028.92

  • CrossRef
  • Google Scholar

• 36

  PuetzV.SylajaP. N.CouttsS. B.HillM. D.DzialowskiI.MuellerP.et al (2008). Extent of hypoattenuation on CT angiography source images predicts functional outcome in patients with basilar artery occlusion.Stroke392485–2490. 10.1161/STROKEAHA.107.511162

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  RammalS. A.AlmekhlafiM. A. (2016). Diabetes mellitus and stroke in the Arab world.J. Taibah Univ. Med. Sci.11295–300. 10.1016/j.jtumed.2016.05.001

  • CrossRef
  • Google Scholar

• 38

  ShiJ.PengH.YouS.LiuY.XuJ.XuY.et al (2018). Increase in neutrophils after recombinant tissue plasminogen activator thrombolysis predicts poor functional outcome of ischaemic stroke: a longitudinal study.Eur. J. Neurol.25687–e645. 10.1111/ene.13575

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  ShichitaT.AgoT.KamouchiM.KitazonoT.YoshimuraA.OoboshiH. (2012). Novel therapeutic strategies targeting innate immune responses and early inflammation after stroke.J. Neurochem.12329–38. 10.1111/j.1471-4159.2012.07941.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40

  TangG.LiuY.ZhangZ.LuY.WangY.HuangJ.et al (2014). Mesenchymal stem cells maintain blood-brain barrier integrity by inhibiting aquaporin-4 upregulation after cerebral ischemia.Stem Cells323150–3162. 10.1002/stem.1808

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  TokgozS.KayrakM.AkpinarZ.SeyithanoğluA.GüneyF.YürütenB. (2013). Neutrophil lymphocyte ratio as a predictor of stroke.J. Stroke Cerebrovasc. Dis.221169–1174. 10.1016/j.jstrokecerebrovasdis.2013.01.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  van der HoevenE. J.McVerryF.VosJ. A.AlgraA.PuetzV.KappelleL. J.et al (2016). Collateral flow predicts outcome after basilar artery occlusion: The posterior circulation collateral score.Int. J. Stroke11768–775. 10.1177/1747493016641951

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  WalshS. R.CookE. J.GoulderF.JustinT. A.KeelingN. J. (2005). Neutrophil-lymphocyte ratio as a prognostic factor in colorectal cancer.J. Surg. Oncol.91181–184. 10.1002/jso.20329

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  WangH.NaghaviM.AllenC.BarberR. M.BhuttaZ. A.CarterA. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015.Lancet3881459–1544. 10.1016/S0140-6736(16)31012-1

  • CrossRef
  • Google Scholar

• 45

  WoodhouseP. R.KhawK. T.PlummerM.FoleyA.MeadeT. W. (1994). Seasonal variations of plasma fibrinogen and factor VII activity in the elderly: winter infections and death from cardiovascular disease.Lancet343435–439. 10.1016/S0140-6736(94)92689-1

  • CrossRef
  • Google Scholar

• 46

  ZahorecR. (2001). Ratio of neutrophil to lymphocyte counts–rapid and simple parameter of systemic inflammation and stress in critically ill.Bratisl. Lek. Listy1025–14.

  • Google Scholar

• 47

  ZaidatO. O.YooA. J.KhatriP.TomsickT. A.von KummerR.SaverJ. L.et al (2013). Recommendations on angiographic revascularization grading standards for acute ischemic stroke: a consensus statement.Stroke442650–2663. 10.1161/STROKEAHA.113.001972

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48

  ZhuB.PanY.JingJ.MengX.ZhaoX.LiuL.et al (2018b). Neutrophil counts, neutrophil ratio, and new stroke in minor ischemic stroke or TIA.Neurology90e1870–e1878. 10.1212/WNL.0000000000005554

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  ZhuB.LiuH.PanY.JingJ.LiH.ZhaoX.et al (2018a). Elevated Neutrophil and Presence of Intracranial Artery Stenosis Increase the Risk of Recurrent Stroke.Stroke492294–2300. 10.1161/STROKEAHA.118.022126

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  ZiW.QiuZ.WuD.LiF.LiuH.LiuW.et al (2020). Assessment of Endovascular Treatment for Acute Basilar Artery Occlusion via a Nationwide Prospective Registry.JAMA Neurol.77561–573. 10.1001/jamaneurol.2020.0156

  • Pubmed Abstract
  • CrossRef
  • Google Scholar