Prognostic significance of serum inflammatory markers in patients with acute ischemic stroke undergoing revascularization therapy

Abstract

OBJECTIVE:

This study aimed to evaluate the prognostic significance of serum inflammatory factor levels in patients with acute ischemic stroke undergoing revascularization therapy.

METHODS:

The study included 94 patients with acute ischemic stroke who underwent revascularization therapy at our hospital. The primary outcome was the modified Rankin scale (mRS) score assessed three months post-treatment. Patients were categorized into two groups: those with a poor prognosis (mRS score > 2) and those with a good prognosis (mRS score≤2). The patients were divided into two groups based on the type of revascularization treatment received: thrombus extraction or intravenous thrombolysis. Logistic regression analysis was used to identify independent risk factors associated with the prognosis of patients treated with recanalization for acute ischemic stroke.

RESULTS:

Among the 94 patients, 59 had a good prognosis, and 35 had a poor prognosis. At admission, the patients in the good prognosis group exhibited lower NIHSS scores, shorter hospital stays, fewer previous cardiac events, lower LDL levels, fasting glucose, IL-6, and TNF-a compared to those in the poor prognosis group (all P < 0.05). Logistic regression analysis identified TNF-a (odd ratio (OD), 1.623; 95% confidence interval (CI), 1.282–1.933; P = 0.035) and IL-6 (OD, 1.055; 95% CI, 1.024–1.088, P = 0.023) as independent risk factors for poor prognosis in patients after revascularization. Additionally, pre-hospital NIHSS scores, IL-6, and TNF-a levels were significantly lower in the good prognosis group compared to the poor prognosis group, with these differences being statistically significant.

CONCLUSION:

IL-6 and TNF-α may serve as prognostic markers for outcomes following revascularization therapy in patients with acute ischemic stroke, including those receiving intravenous thrombolysis.

Keywords

IL-6 prognosis revascularization therapy risk factors TNF-a

1 Introduction

Acute ischemic stroke is a condition characterized by an abnormality in cerebral blood supply due to various causes, leading to brain tissue necrosis and irreversible neurological damage. It is associated with high rates of morbidity, disability, and mortality, posing a significant threat to public health and placing a considerable burden on families and society [1]. The primary therapeutic objective for acute ischemic stroke is to achieve rapid and safe revascularization, restoring blood flow to salvageable ischemic brain tissues before irreversible neuronal damage occurs. This condition presents a serious challenge to patients and society, making stroke prevention and treatment a high priority both domestically and internationally [2]. Currently, the two main types of revascularization therapy are thrombolysis and thrombectomy. While thrombolysis can achieve a recanalization rate of up to 70%, the 90-day prognosis for patients is only about 50% [3]. This limited outcome is closely associated with ischemia-reperfusion injury, which can further damage neurons in the penumbra following recanalization of the occluded vessel. Consequently, there is a lack of effective preventive and therapeutic strategies in clinical practice. Early prediction of prognosis in patients with acute ischemic stroke is therefore crucial for timely clinical intervention. Examining related biomarkers to predict the prognosis of patients after revascularization therapy is of significant clinical importance.

Ischemic stroke often arises in the context of atherosclerotic plaque formation, where arterial stenosis overlying carotid atherosclerotic plaques and increased viscosity of blood lead to thrombus formation — a process characterized as chronic inflammation [4]. Additionally, conditions like hypo-perfusion and hypoxia can trigger endothelial dysfunction, leading to the release of microglial cells and pro-inflammatory cytokines, which further aggravate the inflammatory response [5 –9]. This inflammatory response facilitates the recruitment of peripheral immune cells to the ischemic region of the brain, exacerbating damage to the blood-brain barrier. This damage can result in cerebral edema, and intracranial hypertension, and ultimately worsen neuronal injury, leading to early neurological deterioration and other adverse prognostic outcomes for patients [10]. Increasing evidence indicates that neuro-inflammatory responses are pivotal in the onset and progression of early neurological deterioration [11]. Therefore, neuro-inflammation plays a significant role in the onset and development of ischemic stroke.

Inflammatory markers such as CRP, IL-6, IL-1β, TNF-α, and TPA have been extensively studied across various diseases due to their convenience, affordability, and accessibility as serum indicators [12, 13]. CRP and IL-6, in particular, serve as peripheral markers of inflammation and have been associated with cardiovascular and cerebrovascular risk factors [14]. Elevated CRP levels are frequently observed in patients following acute ischemic events. Recent clinical studies have identified a correlation between CRP, IL-6, D-dimer, and TPA with cardiovascular event risk. Although conventional inflammatory markers like PCT and IL-6 are used in assessing acute ischemic stroke (AIS), they are influenced by infection and other variables, resulting in limited specificity [15]. Given the integral role of neuroinflammation in the progression of ischemic stroke, predicting prognosis using blood markers has significant clinical value. Literature indicates that glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and IL-6 are elevated in the serum of patients with acute ischemic stroke. However, there aren’t many comprehensive reports that link these markers with disease severity and prognosis [16].

Research has revealed that CRP levels can serve as a predictive marker for cerebrovascular events independent of traditional markers. Gastillo et al. identified CRP and IL-6 as independent risk factors for cerebral infarction in a group of 231 patients with ischemic stroke. Despite these findings, there is limited research on the role of inflammation-related factors in predicting the prognosis of patients with acute ischemic stroke following recanalization therapy. Therefore, the objective of the present study is to examine the relationship between serum levels of inflammation-related indicators, like IL-1, IL-6, and TNF-α, and prognosis after revascularization therapy in patients with acute ischemic stroke.

2 Information and methodology

2.1 Design of study program

We conducted a retrospective study involving patients diagnosed with acute ischemic stroke who received revascularization therapy at the Jinshan Branch of Shanghai Sixth People’s Hospital, Shanghai, China, from January 1, 2022, to January 1, 2024. The inclusion criteria were as follows: 1) adherence to the diagnostic and therapeutic criteria for stroke established by the 4th Conference on Cerebrovascular Diseases of the Chinese Medical Association; 2) an NIHSS score≥10; 3) receipt of either intravenous thrombolysis with alteplase or arterial thrombolysis using the SWIM technique; and 4) availability of complete serologic data. Exclusion criteria were: 1) severe cardiac, hepatic, or renal failure; 2) fibrinogen levels < 2 g/L; 3) presence of malignant tumors; 4) patients who did not undergo revascularization; and 5) patients lost to follow-up. Serum inflammation-related markers like IL-6, TNF-α, and Netrin-1 were assessed using enzyme-linked immunosorbent assay (ELISA). Additionally, routine blood tests, blood lipid profiles, coagulation function tests, liver and kidney function assessments, electrocardiograms, cranial MRIs, and cervical vascular ultrasounds were performed. At three months post-treatment, mRS scores, and NIHSS scores were recorded. This study was approved by the Ethics Committee of the Jinshan Branch of Shanghai Sixth People’s Hospital, and informed consent was obtained.

2.2 Methods

2.2.1 Methods of revascularization treatment

Thrombolysis with alteplase was administered in this study as follows: an initial dose of 10% of the total dose (0.9 mg/kg) was given as an intravenous push over 1 minute, followed by continuous intravenous infusion of the remaining 90% over 1 hour. The total amount of alteplase administered did not exceed 90 mg. After thrombolysis, neuroprotective sequential treatment was used, and a head CT scan was performed within 24 hours. Patients who did not exhibit intracranial hemorrhage were subsequently treated with antiplatelet therapy and other interventions. Bleeding instances were managed with appropriate antiplatelet and additional therapies.

Thrombus extraction was performed as follows: Initially, under general anesthesia, whole-brain angiography was conducted to identify the location of the occluded vessel. Subsequently, a stent (Solitaire AB) was advanced to the vessel site using a catheter. The catheter was then slowly withdrawn to allow the stent to open naturally. The SWIM technique was used to minimize retrograde displacement in all patients. Next, the aspiration force of the stent was applied, and the intermediate catheter was gradually advanced to the thrombus exit. Imaging was reviewed, and if necessary, antiplatelet therapy was administered. In cases where additional thrombus retrieval was required, contrast-enhanced imaging and repeated thrombus retrieval procedures were conducted. Postoperatively, cranial CT was reviewed immediately and again 24 hours later, while maintaining stringent control over blood pressure. In the absence of intracranial hemorrhage, aspirin 100 mg daily was prescribed to prevent thrombus formation.

2.2.2 Measurement of serum inflammation-related indexes

Serum inflammation-related indicators in this study were analyzed by standard laboratories. Blood samples from all patients were collected immediately upon admission, centrifuged at 3000 rpm for 10 minutes, and the serum was separated and transported on dry ice. The samples were then promptly sent to the relevant laboratories for testing. The ELISA test kits used were sourced from Nanjing. The laboratory technicians who conducted the measurements of inflammation indicators (IL-1, IL-6, TNF-α, Netrin-1) and other markers were blinded to the baseline characteristics and clinical outcomes of the study participants.

2.2.3 NHISS scores and mRS scores

The NIHSS scale assesses various neurological functions including consciousness, motor abilities, sensory responses, speech, eye movement, visual field, and ataxia. The scale ranges from 0 to 42, with higher scores indicating greater neurological deficits. A NIHSS score of≤5 is used to classify a mild stroke.

The mRS score ranges from 0- 6 points and there are a total of 7 levels: Level 1 (0 points): The patient is asymptomatic. Level 2 (1 point): The patient experiences no significant functional impairment and can perform daily activities independently. Level 3 (2 points): The patient has a mild disability but can manage daily tasks independently. Level 4 (3 points): The patient has a moderate disability, and can walk independently but requires assistance with some daily tasks. Level 5 (4 points): The patient has moderate to severe disability, requires assistance for most daily tasks, and cannot walk independently. Level 6 (5 points): The patient has a severe disability and is completely dependent on others for all daily activities. Level 7 (6 points): The patient has passed away [17].

2.2.4 Prognostic outcome assessment

The primary endpoint was the mRS score assessed at the 3-month follow-up after discharge. Patients with mRS scores of 0–2 were classified as having a good prognosis, while those with scores of 3–6 were classified as having a poor prognosis.

2.3 Statistical methods

Statistical analysis and data processing were conducted using SPSS version 21.0. Count data were reported as percentages, while continuous measurements were expressed as mean±standard deviation. Comparisons of continuous variables were conducted using the Mann-Whitney U test or Student’s t-test, depending on the data distribution. Categorical variables were compared using the χ² test or Fisher’s exact test. Significant factors were further analyzed using logistic regression to identify independent risk factors associated with prognosis. Statistical significance was set at a P-value≤0.05.

3 Results

3.1 Characterization of good and poor prognosis groups of revascularized patients

A total of 94 patients with acute ischemic stroke were included in this study, categorized into two groups based on their mRS scores: 59 patients with a good prognosis and 35 patients with a poor prognosis. The analysis revealed some differences between the two groups in terms of age, gender, emergency glucose levels, INR, and the presence of diabetes or hypertension; however, these differences were not statistically significant (all P > 0.05). Significant differences were observed in NIHSS scores at admission, length of hospitalization, and history of cardiac conditions. Specifically, patients in the good prognosis group had lower pre-hospital NIHSS scores (P < 0.001), fewer instances of cardiac history (P = 0.004), and shorter hospital stays (P = 0.006) compared to those in the poor prognosis group. Laboratory data indicated that patients in the good prognosis group had lower levels of LDL (P = 0.018), fasting blood glucose (P < 0.001), IL-6 (P < 0.001), and TNF-α (P < 0.001) compared to those in the poor prognosis group, with these differences being statistically significant. Refer to Table 1.

Table 1
Comparison of basic data of patients in the good prognosis group and the poor prognosis group

Variable Favorable prognosis group n = 59 Unfavorable prognosis group n = 35 P

Age/Year 72.85±12.35 75.03±10.32 0.382

Weight/Kg 63.26±9.93 56.26±12.20 0.006

GLU 8.21±4.4.58 9.32±3.60 0.238

INR 0.94±0.09 1.67±2.89 0.072

NHISS / point before recanalization 13.69±4.88 25.20±4.47 0.000

Days of hospitalization /d 11.22±4.73 15.51±9.48 0.004

Sex Female 22(37.29) 16(45.71) 0.515

Male 37(62.71) 19(54.28)

Vascular risk factors Smoking 42(71.19) 30(85.71) 0.099

Diabetes 25(42.37) 20(57.14) 0.166

Hypertension 49(83.05) 31(52.54) 0.467

Coronary Heart Disease 21(35.59) 23(65.71) 0.006

Laboratory examination Cholesterol 4.16±1.17 3.69±1.14 0.065

HDL 1.13±0.23 1.28±0.37 0.023

LDL 2.81±1.57 2.12±0.89 0.018

Cysteine 13.73±8.00 14.56±9.15 0.643

Fasting Glucose 6.79±2.32 9.15±3.66 0.000

Homocysteine 11.44±4.89 12.57±4.78 0.305

Hba1c 6.85±2.22 6.73±1.59 0.797

Protein index Netrin-1 206.19±21.10 207.51±20.66 0.770

IL-6 21.23±1.59 35.86±4.19 0.000

PAF 53.85±5.20 52.87±5.61 0.536

LY6G 865.14±69.69 881.52±78.06 0.295

TNF-a 433.86±36.09 479.52±30.44 0.000

Variable	Favorable prognosis group n = 59	Unfavorable prognosis group n = 35	P
Age/Year	72.85±12.35	75.03±10.32	0.382
Weight/Kg	63.26±9.93	56.26±12.20	0.006
GLU	8.21±4.4.58	9.32±3.60	0.238
INR	0.94±0.09	1.67±2.89	0.072
NHISS / point before recanalization	13.69±4.88	25.20±4.47	0.000
Days of hospitalization /d	11.22±4.73	15.51±9.48	0.004
Sex	Female	22(37.29)	16(45.71)	0.515
	Male	37(62.71)	19(54.28)
Vascular risk factors	Smoking	42(71.19)	30(85.71)	0.099
	Diabetes	25(42.37)	20(57.14)	0.166
	Hypertension	49(83.05)	31(52.54)	0.467
	Coronary Heart Disease	21(35.59)	23(65.71)	0.006
Laboratory examination	Cholesterol	4.16±1.17	3.69±1.14	0.065
	HDL	1.13±0.23	1.28±0.37	0.023
	LDL	2.81±1.57	2.12±0.89	0.018
	Cysteine	13.73±8.00	14.56±9.15	0.643
	Fasting Glucose	6.79±2.32	9.15±3.66	0.000
	Homocysteine	11.44±4.89	12.57±4.78	0.305
	Hba1c	6.85±2.22	6.73±1.59	0.797
Protein index	Netrin-1	206.19±21.10	207.51±20.66	0.770
	IL-6	21.23±1.59	35.86±4.19	0.000
	PAF	53.85±5.20	52.87±5.61	0.536
	LY6G	865.14±69.69	881.52±78.06	0.295
	TNF-a	433.86±36.09	479.52±30.44	0.000

3.2 Comparison of risk factors related to patients in the poor prognosis group and good prognosis group

Based on the mRS scores assessed 3 months after revascularization treatment, patients were categorized into two groups: those with scores of 0–2 were classified as having a good prognosis, and those with scores of 3–6 were classified as having a poor prognosis. Logistic regression analysis included pre-treatment NIHSS scores, serum LDL levels, serum IL-6, and serum TNF-α. The analysis identified pre-treatment NIHSS scores (odd ratio (OD), 1.533; 95% confidence interval (CI), 1.274–1.845; P < 0.001), serum IL-6 (OD, 1.623; 95% CI, 1.282–1.933; P = 0.035), and serum TNF-α (OD, 1.055; 95% CI, 1.024–1.088; P = 0.023) as significant risk factors for patient prognosis at 3 months post-treatment. Serum LDL levels (OD, 1.467; 95% CI, 1.273–1.690; P = 0.010) were also found to be statistically significant. Refer to Table 2.

Table 2
Logistic regression analysis of risk factors affecting the prognosis of patients with revascularization

Parameter Regression coefficients Standard error Odds ratio 95% Confidence interval P-Value Wald χ²

NIHSS 0.428 0.094 1.533 1.274–1.845 0.000 20.482

LDL 0.383 0.072 1.467 1.273–1.690 0.010 23.691

IL-6 0.471 0.098 1.623 1.282–1.933 0.035 19.271

TNF-a 0.054 0.015 1.055 1.024–1.088 0.023 12.404

Parameter	Regression coefficients	Standard error	Odds ratio	95% Confidence interval	P-Value	Wald χ²
NIHSS	0.428	0.094	1.533	1.274–1.845	0.000	20.482
LDL	0.383	0.072	1.467	1.273–1.690	0.010	23.691
IL-6	0.471	0.098	1.623	1.282–1.933	0.035	19.271
TNF-a	0.054	0.015	1.055	1.024–1.088	0.023	12.404

3.3 Characterization of patients with acute ischemic stroke with good prognosis and poor prognosis groups after thrombolysis

A total of 56 patients with acute ischemic stroke who underwent thrombolysis were included in this study. The patients were divided into two groups based on their mRS scores: 21 patients with a good prognosis and 35 patients with a poor prognosis. While some differences in age, gender, emergency glucose levels, INR, and the presence of diabetes or hypertension were noted between the two groups, these differences were not statistically significant (all P > 0.05). Notably, patients in the good prognosis group had lower pre-hospital NIHSS scores (P < 0.001). Additionally, laboratory analyses indicated that IL-6 (P < 0.001) and TNF-α (P < 0.001) levels were significantly lower in the good prognosis group compared to the poor prognosis group, with the differences being statistically significant. Refer to Table 3. Positive predictive value (PPV) reflects the proportion of subjects with a positive test result who truly have the outcome of interest. 27.6 ng/l was taken as cutoff value for IL-6 and 384.9 ng/l was set as cutoff value for TNF-α. The PPV for IL-6 and TNF-α were 100% and 100%, respectively.

Table 3
Characterization of patients in the embolization group with good prognosis versus those with poor prognosis

Variable Favorable prognosis group n = 21 Unfavorable prognosis Group n = 35 P

Age/Year 77.76±9.35 75.03±10.32 0.325

Weight/Kg 59.71±9.79 56.26±12.20 0.359

GLU 8.00±6.48 9.32±3.60 0.347

INR 0.96±0.10 1.67±2.89 0.289

NHISS / point before recanalization 18.57±5.18 25.20±4.47 0.000

Days of hospitalisation/d 12.57±6.26 15.51±9.48 0.211

Sex Female 10(37.29) 16(45.71) 0.890

Male 11(62.71) 19(54.28)

Vascular risk factors Smoking 20(71.19) 30(85.71) 0.265

Diabetes 7(42.37) 20(57.14) 0.084

Hypertension 18(83.05) 31(52.54) 0.754

Coronary Heart Disease 11(35.59) 23(65.71) 0.401

Laboratory examination cholesterol 3.77±1.37 3.69±1.14 0.818

HDL 1.17±0.26 1.28±0.37 0.248

LDL 2.49±1.56 2.12±0.89 0.211

Fasting glucose 7.09±2.06 9.15±3.66 0.022

Homocysteine 12.69±6.92 12.57±4.78 0.946

Hba1c 6.7±2.13 6.73±1.59 0.940

Protein index Netrin-1 203.49±19.46 207.51±20.66 0.475

IL-6 21.11±1.89 35.86±4.19 0.000

PAF 53.39±4.98 52.87±5.61 0.730

LY6G 858.11±64.12 881.52±78.06 0.252

TNF-a 427.01±32.98 479.52±30.44 0.000

Variable	Favorable prognosis group n = 21	Unfavorable prognosis Group n = 35	P
Age/Year	77.76±9.35	75.03±10.32	0.325
Weight/Kg	59.71±9.79	56.26±12.20	0.359
GLU	8.00±6.48	9.32±3.60	0.347
INR	0.96±0.10	1.67±2.89	0.289
NHISS / point before recanalization	18.57±5.18	25.20±4.47	0.000
Days of hospitalisation/d	12.57±6.26	15.51±9.48	0.211
Sex	Female	10(37.29)	16(45.71)	0.890
	Male	11(62.71)	19(54.28)
Vascular risk factors	Smoking	20(71.19)	30(85.71)	0.265
	Diabetes	7(42.37)	20(57.14)	0.084
	Hypertension	18(83.05)	31(52.54)	0.754
	Coronary Heart Disease	11(35.59)	23(65.71)	0.401
Laboratory examination	cholesterol	3.77±1.37	3.69±1.14	0.818
	HDL	1.17±0.26	1.28±0.37	0.248
	LDL	2.49±1.56	2.12±0.89	0.211
	Fasting glucose	7.09±2.06	9.15±3.66	0.022
	Homocysteine	12.69±6.92	12.57±4.78	0.946
	Hba1c	6.7±2.13	6.73±1.59	0.940
Protein index	Netrin-1	203.49±19.46	207.51±20.66	0.475
	IL-6	21.11±1.89	35.86±4.19	0.000
	PAF	53.39±4.98	52.87±5.61	0.730
	LY6G	858.11±64.12	881.52±78.06	0.252
	TNF-a	427.01±32.98	479.52±30.44	0.000

4 Discussion

Recent studies indicate that the incidence of acute ischemic stroke in China is approximately 1700 per 100,000 patients. This disease is marked by its rapid onset and high rates of disability and mortality, placing a significant burden on both national and global healthcare systems. Currently, revascularization treatments, including thrombolysis and thrombus extraction, often yield unsatisfactory results, with clinical practice still predominantly focused on prevention. Consequently, there is a pressing need to identify cost-effective and straightforward assessment indexes to help in predicting the prognosis of patients diagnosed with acute stroke. Our findings indicate that elevated levels of IL-6 and TNF-α are independent risk factors for patient outcomes following revascularization therapy and can also be useful in assessing prognosis after thrombus extraction.

Patients were categorized into good and poor prognosis groups based on their mRS scores following revascularization treatment. Comparative analysis of general characteristics revealed that pre-treatment NIHSS scores and serum LDL levels were significantly higher in the poor prognosis group compared to the good prognosis group, with these differences reaching statistical significance. This finding aligns with previous research indicating that higher NIHSS scores are associated with a worse prognosis. Furthermore, an examination of neuro-inflammation-related indicators revealed that pre-treatment levels of IL-6 and TNF-α were significantly elevated in the poor prognosis group. Logistic regression analysis identified TNF-α, NIHSS scores, and IL-6 as independent risk factors for prognosis at 3 months following acute ischemic stroke. Elevated levels of NIHSS, TNF-α, or IL-6 were associated with an increased likelihood of poor prognosis.

IL-6 is crucial in the acute inflammatory response and regulates the production of acute-phase proteins like the C-reactive protein. It contributes to the inflammatory response by activating endothelial cells and accelerating fibrinogen synthesis, indicating its potential significance in vascular inflammation pathogenesis. Elevated IL-6 levels are frequently observed in ischemic conditions, primarily due to the substantial antigen production in brain tissues following cerebral ischemia, which triggers a robust immune response and the activation of inflammatory factors [18]. Extensive clinical and experimental studies have underscored the significant role of immunoinflammation mediated by TNF-α and IL-6 in the pathophysiology of acute ischemic stroke.

Molecular signals generated by cerebral ischemia activate the innate immune system, which amplifies the inflammatory cascade and leads to tissue damage [19]. Additionally, interleukins have been revealed to promote the expression of other inflammatory factors, intensify the inflammatory response, and affect infarcted arterioles, thereby increasing infarct size and exacerbating blood stasis in ischemic brain injury. A DNA methylation-based study on the differential expression of the IL-6 gene in coronary heart disease associated with blood stasis revealed significantly higher methylation of the first seven gene sites of the IL-6 gene transcriptional start site in the blood stasis group compared to the non-blood stasis group. IL-6 is thus recognized not only as an intermediary in the acute inflammatory phase of stroke but also as a neurotrophic factor in the later stages of cerebral ischemia [20].

Similarly, TNF-α is closely linked to the onset and progression of acute cerebral infarction. Its release activates complement and coagulation systems, reduces thrombomodulin expression in the endothelium, and promotes the release of tissue factors. This activation of the exogenous coagulation pathway contributes to blood hyper-viscosity and the formation of atherosclerosis and thrombosis [21]. Collectively, these findings indicate that IL-6 and TNF-α play pivotal roles in the pathogenic processes of patients with acute ischemic stroke. In conjunction with the results of the present study, IL-6 and TNF-α may serve as valuable predictors of prognosis following revascularization therapy for acute ischemic stroke.

Patients who received thrombolysis and intravenous thrombolysis were categorized into two groups for analysis. After 3 months, patients who received intravenous thrombolysis exhibited a favorable prognosis, with 21 patients in the thrombolysis group achieving a good prognosis. When the thrombolysis group was subdivided into good and poor prognosis categories, it was observed that patients in the good prognosis group had lower pre-hospital NIHSS scores and significantly lower levels of IL-6 and TNF-α compared to those in the poor prognosis group. These differences were statistically significant. Consequently, NIHSS score, IL-6, and TNF-α may serve as potential predictors of prognosis following thrombolysis in patients with acute ischemic stroke.

With the establishment of specialized stroke centers and advancements in revascularization techniques, the number of patients with acute ischemic stroke receiving timely treatment is rapidly increasing [22]. Consequently, assessing the prognosis of recanalization therapy for patients with stroke has become highly significant. However, while a metric may be suitable for predicting stroke patient prognosis, its applicability to predicting outcomes of recanalization therapy in patients with acute stroke warrants further investigation. Therefore, research into prognostic assessment indicators should consider both treated and untreated aspects of stroke care. This study has several limitations: 1) it is a retrospective study, which introduces selection bias and is limited by a small sample size; 2) the study only assessed IL-6 and TNF-α as risk factors for prognosis after revascularization therapy, without stratifying the levels of these inflammatory markers.

5 Conclusion

Our findings indicate that emergency IL-6 and TNF-α are independent risk factors for prognosis following revascularization therapy and play a role in assessing outcomes after thrombus removal. Given the retrospective nature of this study and the relatively small sample size, these results should be interpreted with caution. The study is subject to selection bias, which may impact its validity. The objective of future research should be to enhance the credibility and accuracy of these findings through larger sample sizes and prospective study designs.

Declarations

Ethics approval and consent to participate

This study was conducted with approval from the Ethics Committee of the Jinshan Branch of Shanghai Sixth People’s Hospital (Approval Number: jszxyy202234). This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Conflict of interests

The authors declare that they have no conflict of interests.

Funding

Research reported in this publication was supported by the grant of the Scientific Research Foundation of Jinshan District Science and Technology Committee (Grant Number: 2022-WS-02). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Authors’ contributions

Ding-Zhong Tang: Conceptualization, Funding acquisition, Writing – original draft. Wei-Wei Wang: Formal Analysis, Software. Xin-Xin Chen: Data curation, Investigation, Resources. Song-He Yin: Data curation, Formal Analysis. Lei Zhang: Data curation, Software. Xue-Lin Liang: Data curation, Formal Analysis, Software. Guo-Jun Luo: Conceptualization, Validation, Writing – review & editing. Chun-Li Yu: Conceptualization, Project administration, Writing – review & editing.

All authors read and approved the final draft.

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