Prognostic assessment of acute ischemic stroke by systemic immune-inflammatory index: a comprehensive meta-analysis of multidimensional outcomes

Abstract

Introduction:

Our study aimed to quantify the predictive ability of the Systemic Immune-inflammatory Index (SII) for predicting the prognosis and multidimensional complications in acute ischemic stroke (AIS) patients. The primary outcome was poor prognosis, and secondary outcomes included mortality, severity, hemorrhagic transformation/symptomatic intracerebral hemorrhage, stroke-associated pneumonia/poststroke pneumonia, early neurological deterioration, post-stroke depression, progression or recurrence, and other adverse outcomes.

Methods:

We searched 15 databases from their establishment to 13 October 2024 and selected cohort or case-control analyses that analyzed the association of continuous or categorized SII as exposures with the above adverse outcomes of AIS populations.

Results:

The results showed that 78 studies with 40,682 participants were included in meta-analyses. Continuous SII values were significantly higher in poor prognosis groups than in controls (SMD = 248.13, 95% CI: 198.77 to 297.50; p = 0.000). Poor prognosis incidences rose with higher continuous SII values (OR = 1.004, 95% CI: 1.002 to 1.005; p = 0.000). More patients in High SII groups had poor prognosis (RR = 1.95, 95% CI: 1.66 to 2.28; p = 0.000). The risk of poor prognosis was higher in the high SII groups, though this was not statistically significant (OR = 1.007, 95% CI: 0.998 to 1.015; p = 0.120).

Discussion:

In conclusion, our study found that continuous SII and high SII were associated with poor prognosis of AIS and various complications. Given the accessibility and low cost of SII, integrating it into prognostic scores merits further research for better clinical choices.

Systematic review registration:

PROSPERO (CRD42024586414), https://www.crd.york.ac.uk/PROSPERO/view/CRD42024586414.

1 Introduction

Acute ischemic stroke (AIS), a prominent form of stroke, ranks as the primary cause of disability and mortality on a global scale (1). Given its high prevalence, there is an urgent need for a simple, accurate, and inexpensive prognostic biomarker to better predict AIS outcomes. Systemic Immune-Inflammatory Index (SII) is an inflammatory indicator calculated as Neutrophil ×Platelets/Lymphocyte, which reflects the balance between the body’s inflammatory response and immune state and the state of coagulation. There were three systematic reviews that reported on SII’s predictive value in the prognosis of AIS, but all were published early and flawed in design, with few included studies (2–4). The purpose of this study was to conduct a thorough literature search and pool data on the prognostic ability of SII for outcomes of AIS, including poor prognosis, mortality, severity, complications like hemorrhagic transformation (HT)/symptomatic intracerebral hemorrhage (sICH), stroke-associated pneumonia (SAP)/poststroke pneumonia (PSP), early neurological deterioration (END), post-stroke depression (PSD), progression/recurrence, and other complications.

2 Materials and methods

There were two researchers who independently conducted the entire process under MOOSE (4), with the review protocol deposited in PROSPERO (CRD42024586414). There were 15 databases searched from their establishment to 13 October 2024: PubMed, Embase, Cochrane, EBSCO, Scopus, OVID, Web of Science, CNKI, Wanfang, VIP, Sinomed, Clinical Trials, WHO-ICTRP, Chictr, and DANS EASY. AIS search subject terms included “Brain Infarction,” “Brain Ischemia,” “Cerebral Arterial Diseases,” “Cerebral Infarction,” “Cerebrovascular Disorders,” “Stroke,” and free terms included 122. SII terms included six terms (Search criteria, strategies, and results as shown in Supplementary material 1).

After eliminating duplicate reports, the remaining studies’ titles and abstracts were screened to assess their appropriateness for inclusion. Subsequently, the previously selected papers were evaluated for eligibility, data obtained, and bias risk evaluated by the Newcastle-Ottawa Scale (NOS) using the full text. Another two researchers independently conducted the abovementioned processes, and any disagreements were resolved by consulting a third guide researcher.

Eligible articles were cohort or case–control analyses analyzing the relationship between SII and AIS adverse outcomes, including poor prognosis, mortality, severity, and complications such as HT/sICH, SAP/PSP, END, PSD, progression/recurrence, and others. The inclusion criteria were as follows: 1. Population: Patients of AIS and its complications (any diagnostic criteria); 2. Required data: Continuous SII value of poor prognosis/death/mild severity/HT/SAP/END/PSD/progression or recurrence/other complications groups versus the corresponding control groups; sample size of outcomes’ events, adjusted odds ratio (aOR)/adjusted hazard ratio (aHR) of outcomes, and National Institute of Health Stroke Scale (NIHSS) in High SII groups versus Low SII groups; SII cut-off values and area under curve (AUC) of receiver operating characteristic (ROC) curves. 3. Exclusion criteria were as follows: duplicate publications, obviously incorrect data, mismatched research types, and low quality (NOS ≤ 4) (When studies provided sample sizes of outcomes’ events for high- and low-SII groups, HIGH SII was defined as the highest SII group, and LOW SII was the sum of the other groups. For aOR/aHR or NIHSS data, HIGH SII was defined as the highest SII group, and LOW SII was the lowest SII group).

We assessed the association between SII and AIS adverse outcomes using mean difference (MD), Relative Risk (RR), and pooled aOR/aHR. Using Stata 14.0, we considered a p-value < 0.05 significant, quantified heterogeneity with I² and p value of Cochran’s Q statistics, applied the random-effects model for high heterogeneity, and checked for bias with funnel plots and Begg/Egger tests.

3 Results

3.1 General results

Literature search and studies included the initial search, which resulted in 1646 total studies, 670 studies that remained to be screened after removing duplicates, and 99 studies that remained for full-text assessment. Finally, 79 studies (1, 5–82) remained to be included in the systematic review, and 78 studies remained to be included in the meta-analysis (1, 5–33, 35–82) except Wang SN 2024 (34). Details of the process are shown in Figure 1. A summary of the main characteristics of the 78 studies is presented in Table 1; the rating of the quality of the evidence by NOS is presented in Table 2.

Figure 1

3.2 Study characteristics

This systematic review and meta-analysis encompassed 40,682 individuals; the sample size ranged from a minimum of 22 (56) to a maximum of 9,107 (60). Not all studies reported the sex distribution and age data, preventing the accurate calculation of these data. Geographically, 68 studies were conducted in China (1, 5, 7–9, 11, 13–33, 36–38, 40–60, 62–65, 68–72, 75–81), 11 studies were conducted in other states or area including Turkey (n = 3) (6, 10, 67), China Taiwan (n = 3) (66, 74, 82), America (n = 2) (61), Korea (n = 2) (12, 73), Mexico (n = 1) (35). Moreover, studies (1, 5–8, 10, 13, 15–33, 35–41, 43, 44, 46–51, 53–59, 61–71, 73–75, 77–79, 81, 82) were retrospective, and 11 studies (9, 11, 12, 14, 42, 45, 52, 60, 72, 76, 80) were prospective. At the same time, 73 studies (5–13, 15–33, 35–59, 61, 63–72, 74–82) were single-center, and 5 studies (1, 14, 60, 62, 73) were multi-center. The number of studies reporting data on outcomes were as follows: poor prognosis (n = 43) (1, 6–8, 12–14, 16–18, 20, 21, 23, 33, 35–38, 41, 42, 44–46, 48, 50–52, 55, 56, 60, 62, 65–71, 73, 75, 79–82), mortality (n = 12) (1, 5, 6, 10, 16, 37, 39, 60, 61, 66, 67, 80), severity (n = 9) (7, 21, 31, 35, 42, 69, 70, 75, 79), HT/sICH (n = 12) (8, 13, 16, 22, 26, 33, 44, 51, 54, 64, 69, 73), END (n = 8) (12, 13, 16, 21, 25, 29, 38, 52), SAP/PSP (n = 10) (19, 24, 30, 33, 40, 67, 76, 77, 79, 80), PSD (n = 3) (28, 49, 72), progression/recurrence (n = 4) (12, 37, 60, 78), admission NIHSS (n = 11) (5, 12–14, 16, 46, 59, 64, 72, 73, 75), and other complications (n = 25) (1, 9, 11, 15, 27, 32, 33, 42, 43, 47, 49, 51, 53, 57–59, 62, 63, 67, 71–74, 78, 82).

3.3 Predictive value of SII for AIS poor prognosis (primary outcome)

3.3.1 Predictive value of continuous SII for AIS poor prognosis

A total of 32 studies (1, 6–8, 14, 16–18, 20, 21, 23, 33, 35–38, 41, 42, 48, 50, 51, 55, 66–71, 73, 79, 81, 82), which included 42 designs and involved 14,915 AIS patients, were included. Among them, 6,198 patients were in the poor prognosis groups, and 8,717 were in the favorable prognosis groups. A total of 16 studies (8, 14, 16, 17, 23, 36, 37, 42, 50, 55, 68–71, 73, 79) with 20 designs adopted the guideline-recommended 3-month modified Rankin Scale (mRS) 3–6 as the poor prognostic criterion (83). Ultimately, 26 studies came from China (1, 7, 8, 14, 16–18, 20, 21, 23, 33, 36–38, 41, 42, 48, 50, 51, 55, 68–71, 79, 81), and 6 from other countries and regions (6, 35, 66, 67, 73, 82). In the meantime, 12 studies mentioned IVT (16–18, 20, 21, 23, 36, 38, 41, 50, 51, 68), 6 mentioned EVT (8, 14, 48, 55, 71, 73), and 14 used pure medication therapy (1, 6, 7, 33, 35, 37, 42, 66, 67, 69, 70, 79, 81, 82). I² = 89.9% > 50%, Q statistics p = 0.000, indicating a high level of heterogeneity among 42 designs. Meta-regression was conducted with effect size (ES) as the dependent variable and the 5 possible sources of heterogeneity (mRS rating, follow-up time, treatment modality, regional distribution, and mRS rating + follow-up time) as independent variables. The meta-regression results showed that for the 5 independent variables, all p-values were > 0.05 (0.444; 0.380; 0.275; 0.745; 0.643), indicating that the heterogeneity was not related to these 5 factors, and the source of heterogeneity needs to be further explored. Random-effects model showed the baseline SII value was significantly higher in poor prognosis groups (SMD = 248.13, 95% CI: 198.77 to 297.50, p = 0.000, Figure 2A), meaning that the SII value of the poor prognosis groups was 248.13 × 10⁹/L higher than that of the favourable prognosis groups significantly. Figure 3A shows the funnel plot was asymmetric, Begg p = 0.319 > 0.05, Egger p = 0.004 < 0.05, indicating a slight publication bias in the 42 designs. After applying the trim-and-fill method, the significance of the overall effect size and the heterogeneity did not change, suggesting that publication bias did not distort the conclusions of this meta-analysis (Figure 3B).

Figure 2

Figure 3

A total of fourteen studies (6, 14, 16–18, 20, 21, 23, 45, 50, 52, 69, 79, 81), with 15 designs, evaluated the aORs of continuous SII in predicting AIS poor prognosis. High heterogeneity was found (I² = 90.9%, Q-statistic, p = 0.000). Meta-regression indicated that neither follow-up time nor treatment modality was a source of heterogeneity (p = 0.578; 0.489). Figure 2B shows a trend: with an increase in continuous SII, the incidence of poor prognosis may be slightly higher (OR = 1.004, 95% CI: 1.002 to 1.005, p = 0.000). The funnel plot in Figure 3C shows a specific publication bias in the 15 designs (Begg p = 0.020, Egger p = 0.834). The trim-and-fill analysis showed that the number of imputed missing studies was negligible, and the adjusted effect size (OR = 1.003, 95% CI: 1.002 to 1.005, p = 0.000) was almost consistent with the unadjusted one (Figure 3D).

Additionally, 2 studies involved aORs of SII per 1 standard deviation (SD) to predict AIS poor prognosis. Chen GJ 2024 (14) reported aOR = 1.241 (95% CI: 1.051 to 1.465), and Huang SW 2024 (1) reported aOR = 1.191 (95% CI: 1.006 to 1.410), indicating that for every 1 SD increase in SII, the likelihood of a poor prognosis increases in AIS patients.

3.3.2 Predictive value of categorized SII for AIS poor prognosis

A total of 10 studies (12–14, 16, 44–46, 60, 65, 75), with 11 designs, provided data on the sample size of poor/favorable prognosis patients in both high and low SII groups; all criteria of poor prognosis were mRS 3–6. Among 21,719 patients, 5,761 were in high SII groups, and 15,958 were in low SII groups. High heterogeneity was noted (I² = 92.9%, Q statistics p = 0.000), and meta-regression showed follow-up time, regional distribution, and treatment modality were not sources of heterogeneity (p = 0.590; 0.459; 0.593). Subgroup analysis by treatment modality in Figure 2C revealed less within-group heterogeneity. A random-effects model for all designs indicated RR = 1.95 (95% CI: 1.66 to 2.28, p = 0.000), meaning patients with High SII were 1.95 times more likely to have a poor prognosis significantly. The almost symmetrical funnel plot (Begg p = 0.876, Egger p = 0.134) suggests that there is no expected publication bias, as shown in Figure 3E.

A total of 16 studies (1, 8, 12, 14, 32, 35, 37, 38, 46, 51, 60, 62, 65, 70, 75, 80) with 19 designs reported aORs of categorized SII in predicting poor prognosis. Among them, CAO 2024 (8) with 2 designs was removed as its aOR = 1.000 (95% CI: 1.000 to 1.000) made log-conversion in STATA difficult. The remaining studies had substantial heterogeneity (I² = 92.8%, Q-statistic p = 0.000). Meta-regression showed that four variables (follow-up time, regional distribution, treatment modality, and mRS rating+follow-up time) were not the source of heterogeneity (p = 0.866; 0.893; 0.710; 0.949 > 0.05). Figure 2D shows that the random-effects model pooled OR = 1.007 (95% CI: 0.998 to 1.015, p = 0.120), indicating a higher but non-significantly poor prognosis risk in the high SII groups compared to the low SII groups. Funnel plots for the 17 designs were symmetrical, and bias tests (Begg p = 0.760, Egger p = 0.833) suggested likely no publication bias in the designs (Figure 3F).

3.4 Predictive value of SII for AIS secondary outcomes (mortality, severity, HT/sICH, END, PSD, progression/recurrence, and other complications)

3.4.1 Continuous SII

Figure 4

Figure 5

3.4.2 Categorized SII

3.5 Other complications

A total of 17 studies (1, 9, 11, 15, 32, 42, 43, 47, 51–53, 57–59, 62, 71, 78) listed continuous/categorized SII aORs/aHRs in other complication groups studied, as shown in Table 3. A total of 13 studies (9–11, 15, 33, 43, 47, 51, 57, 62, 63, 70, 78) listed continuous SII values in other complications groups were studied, as shown in Table 4.

3.6 SII cut-off values and AUC of ROC curves

A total of 51 studies (5, 6, 8, 9, 11–13, 15–21, 24–29, 32, 33, 35–39, 43, 44, 46, 47, 49–52, 54, 56, 58, 62, 65, 66, 68–70, 72, 73, 76, 77, 79, 80, 82) listed cut-off values, AUC (95% CI), sensitivity, and specificity of ROC curves, as shown in Table 5.

4 Discussion

In this study, we conducted a comprehensive systematic review and meta-analysis to explore the link between SII and AIS. A total of 40,682 individuals from 78 studies (1, 5–33, 35–82) were involved in the meta-analysis, while 79 studies (1, 5–82) were included in the systematic review.

The principal findings of this study are as follows: (1) The continuous SII values in poor prognosis, death, moderate–severe severity, HT/sICH, SAP/PSP, END, PSD, Progression/Recurrence groups were significantly higher than those in favorable prognosis, survival, mild severity, non-HT/sICH, non-SAP/PSP, non-END, non-PSD, no-progression/recurrence groups. (2) The incidence of poor prognosis, mortality, moderate–severe severity, HT/sICH, SAP/PSP, and END could be higher with an increase in continuous SII, significantly higher except for the incidence related to severity. (3) The sample size of poor prognosis, death, HT/sICH, END, progression/recurrence patients of high SII groups was significantly higher than that of low SII groups. (4) The risk of mortality, severity, HT/sICH, SAP/PSP, END, PSD, Progression/Recurrence in high SII groups was higher than in low SII groups, significantly higher except for the risks of poor prognosis and END. (5) The Admission NIHSS in AIS patients with high SII groups was significantly higher than in low SII groups.

From a pathophysiological perspective, the body’s immune-inflammatory response is activated following the onset of AIS. SII, a biomarker of systemic immune inflammation, has an elevated SII level that often implies an exacerbated inflammatory response, triggering a cascade of adverse events (84). Inflammatory cells infiltrate the brain tissue, releasing diverse inflammatory factors that disrupt the blood–brain barrier, exacerbate brain edema, and intensify neurological damage (83). Additionally, high SII levels are associated with platelet activation and aggregation, promoting thrombosis, aggravating cerebral ischemia, and influencing AIS prognosis, mortality, severity, END, progression, and recurrence (2–4). Patients in high SII groups are at a significantly higher risk of developing HT (64), likely due to high-SII-induced vascular endothelial damage, increased vascular permeability, and blood component exudation. Patients in High SII groups are also more susceptible to PSD (72), as the inflammatory response interferes with neurotransmitter synthesis, metabolism, and release, leading to an imbalance in neurotransmitters like 5-hydroxytryptamine and dopamine. Moreover, high SII levels, reflecting a perturbed immune-inflammatory state, increase the risk of SAP by reducing the body’s resistance and making it more vulnerable to pulmonary infections (76, 80).

Our study boasts noteworthy strengths. First, given that the concept of the SII was first proposed by Chinese researchers (84), we specifically retrieved several Chinese databases as sources. This effort significantly broadened the scope of our system review. The search strategy we implemented was more sophisticated. For the research on AIS, our search keywords included 6 subject terms and 122 free terms, effectively reducing the probability of missed or inaccurate retrievals. By incorporating studies from more recent years, we broadened the scope further, guaranteeing the inclusion of the latest research findings. Moreover, our analysis encompassed additional outcomes, such as SAP/PSP, END, and PSD, which were integrated into the meta-analysis for the first time, facilitating a more multi-dimensional assessment.

There are several limitations to our study. First, language is a constraint, as we only included literature in Chinese and English, while relevant studies in other languages may contain valuable information, affecting the generalizability and comprehensiveness of the findings. Second, due to the variability of cut-offs of SII used in different studies, we could not determine a consensus on the best cut-off value based on our analysis, which may limit clinical guidance. Third, although we used various methods to assess and deal with heterogeneity, some analysis results still have high heterogeneity, which may affect the accuracy and reliability of pooled effect values, reducing the persuasiveness of the findings. Sources of heterogeneity may include differences in study participants (age, sex, nationality, etc.), differences in study design (prospective cohort studies, retrospective cohort studies, and case–control studies), differences in interventions (different treatments, drug use, etc.), differences in SII grouping criteria (time of blood sampling and instruments), and differences in outcome measures (definitions and evaluation tools).

This result suggests that SII levels may represent an important diagnostic and prognostic tool for AIS complications in clinical practice. Monitoring and treatment should be strengthened for patients with higher SII levels, and more active measures should be taken to control the inflammatory response and clotting state. However, the role of SII in predicting poor prognosis, mortality, severity, and a variety of other complications is not fully understood.

In summary, high SII levels are linked to poor AIS prognosis and multiple complications, and SII may function as a cost-effective prognostic biomarker. Evaluating the role of SII in therapeutic decision-making is necessary, as our preliminary results suggest its potential to reflect clinical conditions and assist decision-makers. However, more research, especially large-sample and multi-center studies, is needed to better understand the utility of SII through dynamic monitoring.

References

• 1.

  HuangSXieWGaoYJinYChenYZhouGet al. A role for systemic inflammation in stroke-associated infection and the long-term prognosis of acute ischemic stroke: a mediation analysis. Jir. (2024) 17:6533–45. doi: 10.2147/JIR.S474344

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2.

  HanJYangLLouZZhuY. Association between systemic immune-inflammation index and systemic inflammation response index and outcomes of acute ischemic stroke: a systematic review and meta-analysis. Ann Indian Acad Neurol. (2023) 26:655–62. doi: 10.4103/aian.aian_85_23

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3.

  HuangYYinXLiZ. Association of the systemic immune-inflammation index (SII) and clinical outcomes in patients with stroke: a systematic review and meta-analysis. Front Immunol. (2022) 13:1090305. doi: 10.3389/fimmu.2022.1090305

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4.

  YeZHuTWangJXiaoRLiaoXLiuMet al. Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: a systematic review and meta-analysis. Front Cardiovasc Med. (2022) 9:933913. doi: 10.3389/fcvm.2022.933913

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5.

  YangYHePZhangY. Systemic immune-inflammation index predicts short-term mortality in acute ischemic stroke with severe stenosis of internal carotid artery associated pneumonia. Brain Behav. (2024) 14:e70047. doi: 10.1002/brb3.70047

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6.

  ArslanKSultan SahinA. Prognostic value of systemic immune-inflammation index and systemic inflammatory response index on functional status and mortality in patients with critical acute ischemic stroke. Tohoku J Exp Med. (2024) 265:91–97. doi: 10.1620/tjem.2024.J094

  • CrossRef
  • Google Scholar

• 7.

  ZhuFWangZSongJJiY. Correlation analysis of inflammatory markers with the short-term prognosis of acute ischaemic stroke. Sci Rep. (2024) 14:17772. doi: 10.1038/s41598-024-66279-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8.

  CaoWSongYBaiXYangBLiLWangXet al. Systemic-inflammatory indices and clinical outcomes in patients with anterior circulation acute ischemic stroke undergoing successful endovascular thrombectomy. Heliyon. (2024) 10:e31122. doi: 10.1016/j.heliyon.2024.e31122

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9.

  ChengYZhuHLiuCLiLLinFGuoYet al. Systemic immune-inflammation index upon admission correlates to post-stroke cognitive impairment in patients with acute ischemic stroke. Aging. (2024) 16:8810–21. doi: 10.18632/aging.205839

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10.

  MisirliogluNFUzunNOzenGDÇalikMAltinbilekESutasirNet al. The relationship between neutrophil–lymphocyte ratios with nutritional status, risk of nutritional indices, prognostic nutritional indices and morbidity in patients with ischemic stroke. Nutrients. (2024) 16:1225. doi: 10.3390/nu16081225

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11.

  HaoXZhuMSunZLiPMengQTanLet al. Systemic immune-inflammation index is associated with cardiac complications following acute ischemic stroke: a retrospective single-center study. Clin Neurol Neurosurg. (2024) 241:108285. doi: 10.1016/j.clineuro.2024.108285

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12.

  LeeMLeeEJKimROPyunJJooBKwonKet al. Systemic immune-inflammation index as a predictor of early stroke progression/recurrence in acute atherosclerotic ischemic stroke. Clin Neurol Neurosurg. (2024) 238:108182. doi: 10.1016/j.clineuro.2024.108182

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13.

  WangNWangLZhangMDengBWuT. Correlations of 2 novel inflammation indexes with the risk for early neurological deterioration in acute ischemic stroke patients after intravenous thrombolytic therapy. Neurologist. (2024) 29:146–51. doi: 10.1097/NRL.0000000000000557

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14.

  ChenGWangAZhangXLiYXiaXTianXet al. Systemic immune-inflammation response is associated with futile recanalization after endovascular treatment. Neurocrit Care. (2024) 41:165–73. doi: 10.1007/s12028-023-01930-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15.

  ZhangLXuXZhangXJiangSHuiP. Systemic immune-inflammation index is associated with ulcerative plaque in patients with acute ischemic stroke: a single center exploratory study. J Clin Ultrasound. (2024) 52:295–304. doi: 10.1002/jcu.23632

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16.

  WeiCXueJZhouXXiaXLiX. Systemic immune-inflammation index is a prognostic predictor for patients with acute ischemic stroke treated with intravenous thrombolysis. Neurologist. (2024) 29:22–30. doi: 10.1097/NRL.0000000000000508

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17.

  ZhaoKJWuXQ. Relationship between peripheral blood inflammation indicators and short-term adverse outcomes in patients with acute ischemic stroke treated with intravenous thrombolysis. Signa Vitae. (2024) 20:96–103. doi: 10.22514/sv.2024.087

  • CrossRef
  • Google Scholar

• 18.

  GuoqingBYanJAmitiDMingyuanWFuXUHongyanLI. Systemic immune-inflammation index, serum amyloid a and N-terminal pro-brain natriuretic peptide for predicting prognosis of patients with acute cerebral infarction after thrombolytic therapy. J Chin Pract Diagn Therap. (2024) 38:1023–8. doi: 10.13507/j.issn.1674-3474.2024.10.009

  • CrossRef
  • Google Scholar

• 19.

  ZhouLNZhangXLiXH. Predictive value of systemic immune inflammation index and neutrophil lymphocyte ratio for acute ischemic stroke associated pneumonia. Syst Med. (2024) 9:4–7. doi: 10.19368/j.cnki.2096-1782.2024.17.004

  • CrossRef
  • Google Scholar

• 20.

  MengtingCJianHYunnanLUXiaohuaZ. Study on the value of systemic immune-inflammation index in predicting short-term functional prognosis of patients with a-cute cerebral infarction. Chin J Geriatric Care. (2024) 22:28–33. doi: 10.3969/j.issn.1672-2671.2024.04.006

  • CrossRef
  • Google Scholar

• 21.

  MaL. Correlation analysis between SII, NAR and short-term prognosis ofPatients with acute cerebral infarction after intravenous thrombolysis. Master Type, Anhui Medical University (2024)

  • Google Scholar

• 22.

  YuH. Correlation analysis of NLR, dNLR, PLR, SII and hemorrhagic transformation after EVT in acute ischaemic stroke. Master Type, 吉林大学 (2024). doi: 10.27162/d.cnki.gjlin.2024.002156

  • CrossRef
  • Google Scholar

• 23.

  ZhouquanHUPengJYongTBaoliXUShuZ. Efficacy of systemic immune inflammation index and National Institutes of Health stroke scale score on evaluating the short-term prognosis of patients with acute ischemic stroke receiving intravenous thrombolysis. West China Med J. (2024) 39:699–704. doi: 10.7507/1002-0179.202311160

  • CrossRef
  • Google Scholar

• 24.

  TianluWRanWJingMLeiT. Predictive value of multiple inflammatory markers in stroke-associated pneumonia. Int J Geriatrics. (2024) 45:308–13. doi: 10.3969/j.issn.1674-7593.2024.03.011

  • CrossRef
  • Google Scholar

• 25.

  JiaxiangYXiaoxiLIJunXHaopengLIJunW. Predictive value of hypoperfusion intensity ratio combined with systemic immune-inflammation index for early neurological deterioration after thrombolysis in elderly patients with acute ischemic stroke. Pract Geriatrics. (2024) 38:598–602. doi: 10.3969/j.issn.1003-9198.2024.06.013

  • CrossRef
  • Google Scholar

• 26.

  NiuFLGaoLH. Influencing factors and predictive value of hemorrhagic transformation of recombinant tissue plasminogen activator in patients with acute ischemic stroke. Doctor. (2024) 9:86–9. doi: 10.3969/j.issn.2096-2665.2024.05.029

  • CrossRef
  • Google Scholar

• 27.

  ZhangJ. Research on the predictive value of MHR, NPAR, and SI for the occurrence of progressive ischemic stroke. Master Type, Chengde Medical University (2024). doi: 10.27691/d.cnki.gcdyx.2024.000187

  • CrossRef
  • Google Scholar

• 28.

  DongLGuoliangLIHuijunWJunwenW. Predictive value of systemic immune-inflammatory index on post-stroke depression in patients with acute ischemic stroke. Chin J Stroke. (2024) 19:190–6. doi: 10.3969/j.issn.1673-5765.2024.02.010

  • CrossRef
  • Google Scholar

• 29.

  HuangHLiuZWYuZHSongCJLiDHanYY. Systemic inflammatory response index, systemic immune-inflammatory index, and CT perfusion imaging parameters predict early neurological deterioration in patients with minor stroke due to anterior circulation large vessel occlusion. Int J Cerebrovasc Dis. (2024) 32:407–13. doi: 10.3760/cma.j.issn.1673-4165.2024.06.002

  • CrossRef
  • Google Scholar

• 30.

  HaimeiLHuiyingFUYangB. Predictive value of inflammatory markers on the risk of stroke-associated pneumonia in acute ischemic stroke patients. J Clin Neurol. (2024) 37:9–14. doi: 10.3969/j.issn.1004-1648.2024.01.003

  • CrossRef
  • Google Scholar

• 31.

  LijunWPingZMeihuaHJieYHongjianSFangSet al. Relationships between peripheral blood inflammatory markers and acute ischemic stroke. Acad J Naval Med Univ. (2024) 45:174–80. doi: 10.16781/j.CN31-2187/R.20230539

  • CrossRef
  • Google Scholar

• 32.

  ZhangMZhaoWWuCXuJGuoWRenCet al. Inflammation index in failure of delay functional independence after successful recanalization. Int J Neurosci. (2024) 1:1–8. doi: 10.1080/00207454.2024.2414280

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33.

  ZhengLLinZ. The relationship between composite inflammatory ratios and complications of massive ischaemic stroke. J Coll Physicians Surg Pak. (2024) 34:434–9. doi: 10.29271/jcpsp.2024.04.434

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34.

  WangS.N. Predictive value of inflammatory markers and blood urea nitrogen to creatinine ratio in progressive cerebral infarction. Master Type, Jilin University (2024). doi: 10.27162/d.cnki.gjlin.2024.004422

  • CrossRef
  • Google Scholar

• 35.

  Fernández-GarzaLEGonzález-AquinesABotello-HernándezEPérez-VázquezGCristobal-NiñoMGóngora-RiveraF. Segmented neutrophil-to-monocyte ratio and systemic immune-inflammation index associated with the severity and functional prognosis of acute ischemic stroke. Int J Neurosci. (2023) 135:228–36. doi: 10.1080/00207454.2023.2294705

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36.

  WangSZhangLQiHZhang FLFangQQiuL. Pan-immune-inflammatory value predicts the 3 months outcome in acute ischemic stroke patients after intravenous thrombolysis. Curr Neurovasc Res. (2023) 20:464–71. doi: 10.2174/0115672026276427231024045957

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37.

  MaFLiLXuLWuJZhangALiaoJet al. The relationship between systemic inflammation index, systemic immune-inflammatory index, and inflammatory prognostic index and 90-day outcomes in acute ischemic stroke patients treated with intravenous thrombolysis. J Neuroinflamm. (2023) 20:220. doi: 10.1186/s12974-023-02890-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38.

  ZhaoJDongLHuiSLuFXieYChangYet al. Prognostic values of prothrombin time and inflammation-related parameter in acute ischemic stroke patients after intravenous thrombolysis with rt-PA. Clin Appl Thromb Hemost. (2023) 29:1299587750. doi: 10.1177/10760296231198042

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39.

  HuXLiangJHaoWZhouJGaoYGongXet al. Prognostic value of inflammatory markers for in-hospital mortality in intensive care patients with acute ischemic stroke: a retrospective observational study based on MIMIC-IV. Front Neurol. (2023) 14:1174711. doi: 10.3389/fneur.2023.1174711

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40.

  ZhangPChenLJiangYYuanHZhuXZhangMet al. Risk factors for and outcomes of poststroke pneumonia in patients with acute ischemic stroke treated with mechanical thrombectomy. Front Neurol. (2023) 14:1023475. doi: 10.3389/fneur.2023.1023475

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41.

  ChuMLuoYWangDLiuYWangDWangYet al. Systemic inflammation response index predicts 3-month outcome in patients with mild acute ischemic stroke receiving intravenous thrombolysis. Front Neurol. (2023) 14:1095668. doi: 10.3389/fneur.2023.1095668

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42.

  LinJ.X. Effect of serum inflammatory cytokines on neurological impairment and prognosis in patients with acute cerebral infarction. Master Type, Southern Medical University (2023). doi: 10.27003/d.cnki.gojyu.2023.001348

  • CrossRef
  • Google Scholar

• 43.

  XiaoLJWenXJ. Diagnostic value of SIl, NLR and PLR levels in acute ischemic stroke with patent foramen ovale. Guangzhou Med J. (2023) 54:28–33. doi: 10.3969/j.issn.1000-8535.2023.09.005

  • CrossRef
  • Google Scholar

• 44.

  Dan-danYLin-zhiJLi-yanLIDe-qinGLeiLI. Value of systemic immune inflammation index in predicting hemorrhage transformation after thrombolysis in elderly patients with stroke. Pract Geriatrics. (2023) 37:789–93. doi: 10.3969/j.issn.1003-9198.2023.08.009

  • CrossRef
  • Google Scholar

• 45.

  LiL. Assessment of serum IL-1β, IL-6, hs-CRP and systemic Immunoinflammatory index (SII) studies on the severity and prognosis of acute ischemic stroke. Master Type, Yanan University (2023). doi: 10.27438/d.cnki.gyadu.2023.000285

  • CrossRef
  • Google Scholar

• 46.

  ZhouY.X. Predictive value of the systemic immune inflammation index for adverse outcomes in patients with acute ischemic stroke. Master Type, Guilin Medical University (2023). doi: 10.27806/d.cnki.gglyx.2023.000084

  • CrossRef
  • Google Scholar

• 47.

  ShaoY.Y. Study on the relationship between plasma fibrin level, SI and enlargement of perivascular space inpatients with acute lacunar infarction. Master Type, Bengbu Medical University (2023). doi: 10.26925/d.cnki.gbbyc.2023.000075

  • CrossRef
  • Google Scholar

• 48.

  WangX. A single-center retrospective study of prognostic factors in patients with simple mechanical thrombectomy and remedial stent implantation. Master Type, Shihezi University (2023). doi: 10.27332/d.cnki.gshzu.2023.000515

  • CrossRef
  • Google Scholar

• 49.

  SongC. Study on the correlation between systemic immune-inflammation index and post-stroke depression in acute stage of ischemic stroke. Master Type, Jinzhou Medical University (2023). doi: 10.27812/d.cnki.glnyx.2023.000031

  • CrossRef
  • Google Scholar

• 50.

  WangY.L. The relationship between the neutrophil tolymphocyte ratio and systemic immune inflammation index and prognosis in patients with acute cerebral infarction before intravenous thrombolysis. Master Type, Jinzhou Medical University (2023). doi: 10.27812/d.cnki.glnyx.2023.000115

  • CrossRef
  • Google Scholar

• 51.

  LiuH.T. Association of systemic immunoinflammatory index with hemorrhagic transformation and short-term prognosis after intravenous thrombolysis in patients with acute ischemic stroke with atrial fibrillation. Master Type, Dalian Medical University (2023). doi: 10.26994/d.cnki.gdlyu.2023.000333

  • CrossRef
  • Google Scholar

• 52.

  WangZ.T. Correlation between composite inflammatory indicators and early eurological outcome and prognosis after intravenous thrombolysis in acute ischemic stroke. Master Type, China Medical University (2023). doi: 10.27652/d.cnki.gzyku.2023.001168

  • CrossRef
  • Google Scholar

• 53.

  DongLGuoliangLJunwenW. Predictive value of the systemic immuno-inflammatory index for the first pass effect of mechanical thrombectomy in acute anterior circulation large vessel occlusions. Chin J Cerebrovasc Dis. (2023) 20:10–9. doi: 10.3969/j.issn.1672-5921.2023.01.002

  • CrossRef
  • Google Scholar

• 54.

  GaoJSunBZhaoWDChenXYTianXY. Systemic immune-inflammation index predicts hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke. Int J Cerebrovasc Dis. (2023) 31:94–9. doi: 10.3760/cma.j.issn.1673-4165.2023.02.003

  • CrossRef
  • Google Scholar

• 55.

  HuixinSWeiSXiaoWHaiqingSFeiCXiaoqinH. Influence of inflammatory markers on predicting clinical prognosis in patients with acute ischemic stroke after endovascular treatment. Chin J Cerebrovasc Dis. (2023) 20:382–91. doi: 10.3969/j.issn.1672-5921.2023.06.004

  • CrossRef
  • Google Scholar

• 56.

  LiuY.Y. Analysis of the sequencing characteristics of T cell receptor library in patients with acute ischemic stroke. Master Type, Zhengzhou University (2023)

  • Google Scholar

• 57.

  SuHEShiyuLYangweiZXiaoliDUHaiyanW. Establishment and validation of risk predictive model for vascular dementia in patients with acute cerebral infarction after thrombolytic therapy. Mil Med Joint Logist. (2023) 37:761–7. doi: 10.13730/j.issn.2097-2148.2023.09.008

  • CrossRef
  • Google Scholar

• 58.

  LiWZhouYZhuGZengKZengHChenJet al. Systemic immune inflammatory index is an independent predictor for the requirement of decompressive craniectomy in large artery occlusion acute ischemic stroke patients after mechanical thrombectomy. Front Neurol. (2022) 13:945437. doi: 10.3389/fneur.2022.945437

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59.

  ZhangLLyuQZhouWLiXNiQJiangSet al. High systemic immune-inflammation index is associated with carotid plaque vulnerability: new findings based on carotid ultrasound imaging in patients with acute ischemic stroke. Front Neurol. (2022) 13:959531. doi: 10.3389/fneur.2022.959531

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60.

  WangNYangYQiuBGaoYWangAXuQet al. Correlation of the systemic immune-inflammation index with short- and long-term prognosis after acute ischemic stroke. Aging. (2022) 14:6567–78. doi: 10.18632/aging.204228

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61.

  WuSShiXZhouQDuanXZhangXGuoH. The association between systemic immune-inflammation index and all-cause mortality in acute ischemic stroke patients: analysis from the MIMIC-IV database. Emerg Med Int. (2022) 2022:1–10. doi: 10.1155/2022/4156489

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62.

  JiYXuXWuKSunYWangHGuoYet al. Prognosis of ischemic stroke patients undergoing endovascular Thrombectomy is influenced by systemic inflammatory index through malignant brain edema. Cia. (2022) 17:1001–12. doi: 10.2147/CIA.S365553

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63.

  LinKFanFCaiMYuYFuCDingLet al. Systemic immune inflammation index and system inflammation response index are potential biomarkers of atrial fibrillation among the patients presenting with ischemic stroke. Eur J Med Res. (2022) 27:106. doi: 10.1186/s40001-022-00733-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64.

  YangYCuiTBaiXWangAZhangXWanJet al. Association between systemic immune-inflammation index and symptomatic intracranial hemorrhage in acute ischemic stroke patients undergoing Endovascular Treatment. Cnr. (2022) 19:83–91. doi: 10.2174/1567202619666220406102429

  • CrossRef
  • Google Scholar

• 65.

  ZhouYLiWXiaSXiangTTangCLuoJet al. Predictive value of the systemic immune inflammation index for adverse outcomes in patients with acute ischemic stroke. Front Neurol. (2022) 13:836595. doi: 10.3389/fneur.2022.836595

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66.

  ChenPChenGHsiaoCHsuPYangFLiuCet al. Comparison of clinical features, immune-inflammatory markers, and outcomes between patients with acute in-hospital and out-of-hospital ischemic stroke. J Inflamm Res. (2022) 15:881–95. doi: 10.2147/JIR.S342830

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67.

  AdiguzelAArsavaEMTopcuogluMA. Temporal course of peripheral inflammation markers and indexes following acute ischemic stroke: prediction of mortality, functional outcome, and stroke-associated pneumonia. Neurol Res. (2022) 44:224–31. doi: 10.1080/01616412.2021.1975222

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68.

  MaXYangJYangXY. The clinical value of system immune inflammation index and fibrinogen to albumin ratio in severity and short-term prognosis of acute ischemic stroke. Int J Lab Med. (2022) 43:2639–44. doi: 10.3969/j.issn.1673-4130.2022.21.017

  • CrossRef
  • Google Scholar

• 69.

  LiuZ. Correlation between systemic immune-inflammation index and acute ischemic stroke severity and prognosis. Master Type, Qingdao University (2022). doi: 10.27262/d.cnki.gqdau.2022.001260

  • CrossRef
  • Google Scholar

• 70.

  LaiyunZJingLChenghuaX. Study on the relationship between systemic immune inflammation index and D-dimer and the severity and prognosis of young acute cerebral infarction patients. J Neurosci Mental Health. (2022) 22:363–8. doi: 10.3969/j.issn.1009-6574.2022.05.011

  • CrossRef
  • Google Scholar

• 71.

  WenliZChuanyangZMingyangPGuozhongCWenSU. Risk factors and nomogram prediction model construction of ineffective recanalization after endovascular treatment in acute stroke. Chongqing Med. (2022) 51:4043–7. doi: 10.3969/j.issn.1671-8348.2022.23.017

  • CrossRef
  • Google Scholar

• 72.

  HuJWangLFanKRenWWangQRuanYet al. The association between systemic inflammatory markers and post-stroke depression: a prospective stroke cohort. Cia. (2021) 16:1231–9. doi: 10.2147/CIA.S314131

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73.

  YiHJSungJHLeeDH. Systemic inflammation response index and systemic immune-inflammation index are associated with clinical outcomes in patients treated with mechanical Thrombectomy for large artery occlusion. World Neurosurg. (2021) 153:e282–9. doi: 10.1016/j.wneu.2021.06.113

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74.

  LiLChenCChangYChenYLeeIHowC. Prognostic role of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammation index in acute ischemic stroke. Medicine. (2021) 100:e26354. doi: 10.1097/MD.0000000000026354

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75.

  WengYZengTHuangHRenJWangJYangCet al. Systemic immune-inflammation index predicts 3-month functional outcome in acute ischemic stroke patients treated with intravenous thrombolysis. Cia. (2021) 16:877–86. doi: 10.2147/CIA.S311047

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76.

  WeiLLiliW. Value of neutrophil/lymphocyte ratio and systemic-immune-inflammation index for diagnosing stroke-associated pneumonia in ACI patients. Chin J Geriatric Heart Brain Vessel Dis. (2021) 23:968–71. doi: 10.3969/j.issn.1009-0126.2021.09.019

  • CrossRef
  • Google Scholar

• 77.

  ChengQUZunshengZ. Predictive value of systemic immune-inflammation index for stroke-associated pneumonia in patients with acute ischemic stroke. Acta Acad Med Xuzhou. (2021) 41:528–33. doi: 10.3969/j.issn.2096-3882.2021.07.011

  • CrossRef
  • Google Scholar

• 78.

  Wei-shiWJiaWUWen-nanNXiao-yangYUJun-junW. Clinical value of blood routine ratio parameters in patients with acute ischemic stroke. J Med Postgraduates. (2021) 34:594–9. doi: 10.16571/j.cnki.1008-8199.2021.06.006

  • CrossRef
  • Google Scholar

• 79.

  ZhongX. Clinical application of systemic immune InflammatoryIndex in acute ischemic stroke patients. Master Type, Kunming Medical University (2021). doi: 10.27202/d.cnki.gkmyc.2021.000651

  • CrossRef
  • Google Scholar

• 80.

  CengX. Study of relationship between systemic immune inflammation index and acute stroke-associated pneumonia and stroke. Master Type, Zhengzhou University (2020). doi: 10.27466/d.cnki.gzzdu.2020.004041

  • CrossRef
  • Google Scholar

• 81.

  ZhaoHNLiuXJChenX. Prognostic value of neutrophil lymphocyte ratio and systemic immune inflammation index in patients with acute cerebral infarction. Electron J Gen Stomatol. (2020) 7:65–7.

  • Google Scholar

• 82.

  ChuYChenPLinS. Correlation between immune-inflammatory markers and clinical features in patients with acute ischemic stroke. Acta Neurol Taiwanica. (2020) 29:103–13.

  • Google Scholar

• 83.

  PowersWJRabinsteinAAAckersonTAdeoyeOMBambakidisNCBeckerKet al. Guidelines for the early Management of Patients with Acute Ischemic Stroke: 2019 update to the 2018 guidelines for the early Management of Acute Ischemic Stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. (2019) 50:e344–418. doi: 10.1161/STR.0000000000000211

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84.

  HuBYangXXuYSunYSunCGuoWet al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. (2014) 20:6212–22. doi: 10.1158/1078-0432.CCR-14-0442

  • Pubmed Abstract
  • CrossRef
  • Google Scholar