Prognostic Values of Prothrombin Time and Inflammation-Related Parameter in Acute Ischemic Stroke Patients After Intravenous Thrombolysis with rt-PA

Abstract

In previous studies, prothrombin time (PT), systemic inflammation response index (SIRI) and systemic immune inflammation Index (SII) levels might be the prognostic factors for patients with ischemic stroke. However, the association between these coagulation and inflammation biomarkers and prognosis in patients with acute ischemic stroke (AIS) who undergo intravenous thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) remains unclear and needs further study. Thus, this study aimed to investigate the relationship between these biomarkers and clinical prognosis after IVT in AIS patients. We included patients at the Hebei general hospital diagnosed with AIS who received standard-dose IVT with rt-PA from September 2017 to August 2022. Demographic information, vascular risk factors, laboratory test results, and other stroke-related data were collected for analysis. Clinical outcomes included short-term outcome at 24 h and functional outcome at 3 months. We enrolled 281 patients in this study. In total, 16 patients had END within 24 h, and 106 patients had an unfavorable outcome at the 3-month visit. In the multivariate analysis, PT level (OR = 1.833; 95% CI: 1.161–2.893; P = 0.009), SIRI level (OR = 2.166; 95% CI: 1.014–4.629; P = 0.046) and SII level (OR = 1.002; 95% CI: 1.000–1.003; P = 0.021) were independently associated with 3-month poor outcome in AIS patients with IVT. In conclusion, the higher PT, SIRI and SII levels were independently associated with poor prognosis in AIS patients after IVT. Additionally, PT, SIRI and SII all can be novel short-term prognostic biomarkers for AIS patients treated with IVT.

Keywords

acute ischemic stroke rt-PA prothrombin time systemic inflammation response index systemic immune inflammation index clinical prognosis

Introduction

Stroke, especially ischemic stroke, is the leading cause of death and disability worldwide.¹ Recombinant tissue plasminogen activator (rt-PA) is an approved intravenous thrombolytic agent used to treat acute ischemic stroke (AIS).² The use of rt-PA has been proven effective in alleviating neurological deficits and improving clinical outcomes. However, early neurological deterioration after intravenous thrombolysis (IVT) occurs in about 10% of patients, and only 30% of patients have a favorable outcome at 90 days after IVT.³ Early determination of risk factors for poor prognosis can help clinicians stratify outcomes and more aggressively determine appropriate treatment for them. Therefore, it is necessary to identify readily available serum biomarkers before IVT that can better predict the prognosis after IVT, so as to instruct our clinical treatment.

Recombinant tissue plasminogen activator (rt-PA), known as a fibrin-specific thrombolytic agent in its pharmacological characteristic, can cause plasmin activation, fibrinolysis and ultimately result in thrombolysis and vessel recanalization. The degree of thrombolysis depends largely on the delicate balance of coagulation and fibrinolysis. While the value of coagulation parameters in routine laboratory examination before IVT has not been well-recognized. Wang et al found that the prolongation of PT from baseline to 24 h after IV rt-PA increases the risk of 3-month unfavorable outcomes after investigating the prognostic value of routine coagulation parameters in AIS patients treated with rt-PA.⁴ Nevertheless, the value of baseline PT before IVT in predicting short-term outcomes in these patients is still unclear.

Inflammation has been increasingly recognized as a key factor in the pathophysiology of secondary cerebral injury after ischemia. Systemic inflammation response index (SIRI), based on the counts of neutrophils, monocytes, and lymphocytes in peripheral blood is considered to be a new inflammatory marker in recent years.⁵ Some studies have found that the predictive role of SIRI in the prognosis of acute coronary syndrome and stroke patients.^5,6 A recent study found that SIRI might be a risk factors of 3-month prognosis for AIS patients with IVT.⁷ And yet, the relatively small sample size might cause selection bias and there is no information about the vascular risk factors which may interfere drastically with the final outcomes. Systemic immune-inflammation index (SII), a novel inflammation index derived from counts of circulating platelets, neutrophils and lymphocytes, has been studied in developing incident cancer.⁸ However, the clinical value of SII in AIS patients still needs to be further investigated. Moreover, baseline PT, SIRI and SII can be easily acquired from coagulation function and blood cells counts in routine clinical examinations. Thus, this study aimed to study the characteristics of PT, SIRI and SII in AIS patients undergoing IVT, assess the relationship between these biomarkers and END as well as 3-month prognosis, and evaluate their prognostic value in AIS patients treated with rt-PA IVT.

Materials and Methods

Study Population

This retrospective study consecutively included 337 AIS patients at the Hebei general hospital diagnosed with AIS who received standard-dose IVT with rt-PA from September 2017 to August 2022. Inclusion criteria for AIS patients were as follows: (1) Inclusion criteria: ① age > 18 years old; ② Meet the diagnostic criteria of ischemic stroke in Chinese AIS diagnosis and treatment guidelines; ③ The included patients received rt-PA intravenous thrombolysis within 4.5 h; Patients were excluded if they met the following criteria: (1) received mechanical thrombectomy after IVT, (2) with cancer and infection, (3) having a premorbid modified Rankin Scale (mRS) score of > 1. and (4) incomplete laboratory tests or follow-up data. Eventually, a total of 281 patients were included in our study. Figure 1 presents the selection of patients in a flow chart.

Figure 1.

Flow chart for patients’ selection.

Data Collection

The following patients data were collected: (1) demographic information (age and sex), (2) vascular risk factors (cigarette smoking, alcohol consumption, atrial fibrillation, coronary heart disease, hypertension, diabetes mellitus, dyslipidemia, and previous stroke), (3) baseline National Institutes of Health Stroke Scale (NIHSS) score, (4) baseline systolic blood pressure (SBP) and diastolic blood pressure (DBP), (5) baseline blood glucose, (6) onset-to-needle time (ONT), and (7) clinical and follow-up information. Stroke subtypes were categorized according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST).⁹ The TOAST classification includes five subtypes of ischemic stroke: (1) large-artery atherosclerosis, (2) cardioembolism, (3) small-vessel occlusion, (4) other determined etiology, and (5) undetermined etiology.

Blood samples were taken from the antecubital vein at baseline before the infusion of rt-PA. Blood cell count was performed on EDTA with an ADVIA 120 counter (Siemens Healthcare Diagnostics), focusing on the measurement of platelet, Neutrophil, Monocyte and lymphocyte counts. SIRI was calculated as neutrophil × monocyte/lymphocyte. SII was calculated as platelet × neutrophil/lymphocyte. For coagulation parameters of PT, blood was collected in 5 mL evacuated tubes using one-tenth of 0.129 mol/L saline sodium citrate as an anticoagulant. Plasma was obtained by centrifugation at 3000 g for 20 min at room temperature and then was frozen at −40 °C.

Clinical Outcomes

Clinical outcomes included the early neurological deterioration and poor outcome 3 months after thrombolysis. END was defined as a 4-point or greater increase in NIHSS score within 24 h after thrombolysis.¹⁰ Functional outcomes were assessed using the mRS score 3 months after IVT. A poor outcome was defined as an mRS score of 2–6, while a favorable outcome was the mRS score of 0-1.

Statistical Analysis

Normally distributed continuous variables are expressed as mean ± standard deviation, and continuous variables that did not conform to normal distribution are expressed as the median and interquartile range (IQR). Categorical variables are expressed as frequencies and percentages. Differences between groups of continuous variables were tested using the t-test or Mann–Whitney U test, according to normality. Differences between categorical variables were determined using the χ² test. The relationship between biomarkers (PT, SIRI, and SII) and mRS scores was evaluated using the Spearman correlation test. Multifactorial logistic regression analysis was used to test the correlation between indices and clinical outcomes. Three models were developed for the multivariate analysis: Model 1 was adjusted for age and sex; Model 2 was adjusted for Model 1 + vascular risk factors, including cigarette smoking, alcohol consumption, atrial fibrillation, coronary heart disease, hypertension, diabetes mellitus, dyslipidemia, and previous stroke; and Model 3 was adjusted for Model 2 + baseline NIHSS scores, baseline SBP and DBP, baseline blood glucose, ONT, and TOAST. Odds ratios (OR) and 95% confidence intervals (CI) were used to evaluate the risk of poor outcomes.

Receiver operating characteristic (ROC) curves were used to evaluate the ability of the PT, SIRI and SII index to predict the clinical treatment outcome of AIS patients after IVT. The optimal test cut-off point was established by calculating Youden's index. The P < 0.05 was considered statistically significant. Statistical analyses were performed using the Statistical Program for Social Sciences version 22.0 (SPSS, IBM, West Grove, PA, United States).

Ethics Statement

The study was approved by the Ethics Review Committee of the Hebei General Hospital. The participants or their direct relatives provided their written informed consent to participate in this study.

Results

Characteristics of Study Participants

This study included 337 patients; 32 were excluded because they had undergone mechanical thrombectomy after IVT, 24 were excluded because they had severe liver and renal disease (n = 3), infection (n = 10) and missed follow-up data (n = 11). A flowchart of the patient selection process is shown in Figure 1.

Eventually, 281 patients with AIS were enrolled in this study, of whom 168 (59.8%) were male, aged 66 (range: 56–73) years. The median onset-to-needle time was 198 (159–237) minutes, and the median baseline NIHSS score was 4 (2–6). In the study population, 106 patients (37.7%) had poor outcomes (mRS ≥ 2). The baseline clinical characteristics and outcomes are shown in Table 1.

Table 1.

Clinical Characteristics of Included Patients.

Variables	Total (n = 281)
Age, years (IQR)	66 (56–73)
Male, n (%)	168 (59.8)
Cigarette smoking, n (%)	108 (38.4)
Alcohol consumption, n (%)	90 (32.0)
Hypertension, n (%)	180 (64.1)
Diabetes mellitus, n (%)	71 (25.2)
Coronary heart disease, n (%)	41 (14.6)
Atrial fibrillation, n (%)	9 (3.2)
Previous stroke, n (%)	55 (19.6)
Dyslipidemia, n (%)	12 (4.3)
Baseline SBP, mm Hg (Mean ± SD)	154 ± 24
Baseline DBP, mm Hg (IQR)	88 (79–99)
Baseline blood glucose, mmol/L (IQR)	7.4 (6.6–9.8)
Baseline NIHSS score (IQR)	4 (2–6)
ONT, min (IQR)	170 (120–200)
Large-artery atherosclerosis, n (%)	127 (45.2)
Small-vessel occlusion, n (%)	103 (36.7)
Cardioembolism, n (%)	16 (5.7)
Other determined etiology, n (%)	11 (3.9)
Undetermined etiology, n (%)	23 (8.2)
Poor outcome, n (%)	106 (37.7)
Early neurological deterioration, n (%)	16 (5.7)
PT (s)	10.50 (10.10–11.00)
SIRI (×10⁹/L, IQR)	0.90 (0.58–1.47)
SII(×10⁹/L, IQR)	557.80 (373.59–865.29)

Abbreviations: IQR, inter quartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; ONT, onset to needle time; END, early neurological deterioration; PT, prothrombin time; SIRI, systemic inflammation response index; SII, systemic immune-inflammation index.

The Association of PT, SIRI and SII Levels with END in AIS Patients After IVT

All the eligible patients were classified into no END group and END group. Comparisons of ages, baseline SBP, and NIHSS scores were statistically significant (P < 0.05). We found that PT (10.50 vs 10.70, P = 0.038) and SIRI levels (0.87 × 10⁹/L vs 1.12 × 10⁹/L, P = 0.013) were significantly higher in patients with END. However, SII level was not significantly different between the END and no END groups (546.25 × 10⁹/L vs 740.17 × 10⁹/L, P = 0.087). Comparisons of the clinical characteristics according to no END and END are presented in Table 2.

Table 2.

Clinical Characteristics of Patients of END and no Early Neurological Deterioration.

Variables	No END (n = 265) NIHSS score increase within 24 h＜4	END (n = 16) NIHSS score increase within 24h ≥ 4	P
Age, years (IQR)	65 (55–73)	64 (66–78)	0.029
Male, n (%)	160 (60.4)	8 (50.0)	0.411
Cigarette smoking, n (%)	101 (38.1)	6 (37.5)	0.961
Alcohol consumption, n (%)	85 (32.1)	5 (31.3)	0.945
Hypertension, n (%)	169 (63.8)	11 (68.8)	0.687
Diabetes mellitus, n (%)	59 (22.3)	5 (31.3)	0.405
Coronary heart disease, n (%)	39 (14.7)	2 (12.5)	0.807
Atrial fibrillation, n (%)	19 (71.7)	0 (0.0)	0.267
Previous stroke, n (%)	65 (24.5)	6 (37.5)	0.246
Dyslipidemia, n (%)	15 (5.7)	2 (12.5)	0.265
Baseline SBP, mm Hg (Mean ± SD)	153 ± 23	169 ± 20	0.009
Baseline DBP, mm Hg (IQR)	88 (78–99)	89 (79–100)	0.375
Baseline blood glucose, mmo/L (IQR)	7.4 (6.5–9.5)	8.3 (7.2–12.5)	0.136
Baseline NIHSS score (IQR)	2 (2–6)	4 (3–9)	0.034
ONT, min (IQR)	170 (120–200)	180 (152–222)	0.241
TOAST			0.691
Large-artery atherosclerosis, n (%)	118 (44.5)	9 (56.3)	-
Small-vessel occlusion, n (%)	99 (37.4)	5 (31.3)	-
Cardioembolism, n (%)	15 (5.7)	1 (6.3)	-
Other determined etiology, n (%)	10 (3.8)	1 (6.3)	-
Undetermined etiology, n (%)	23 (8.7)	0 (0.0)	-
PT(s)	10.50 (10.10–11.00)	10.70 (10.35–11.45)	0.038
SIRI (10⁹/L, IQR)	0.87 (0.57–1.43)	1.12 (0.71–3.27)	0.013
SII(10⁹/L, IQR)	546.25 (368.50–865.29)	740.17 (424.28-1065.30)	0.087

Abbreviations: END, early neurological deterioration; IQR, inter quartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; ONT, onset to needle time; TOAST, Trial of Org 10172 in acute stroke treatment; PT, prothrombin time; SIRI, systemic inflammation response index; SII, systemic immune-inflammation index; mRS, modified Rankin Scale.

Spearman correlation analysis showed that SIRI was positively correlated with END after IVT (rs = 0.123, P ＜ 0.05), and yet PT and SII were not correlated with END (rs = 0.045, P > 0.05; rs = 0.084, P > 0.05) (Figure 2A).

Figure 2.

(A-D) Correlation between the levels of PT, SIRI, and SII and the scores of NIHSS and mRS.

In the multivariate analysis, SIRI level was independently associated with END, adjusted for Model 1 (OR = 2.019; 95% CI: 1.221–3.337; P = 0.006), Model 2 (OR = 1.981; 95% CI: 1.097–3.576; P = 0.023), and Model 3 (OR = 1.844; 95% CI: 1.038–3.277; P = 0.037). However, PT and SII levels were not associated with END in the multivariate analyses. The results of the multivariate logistic regression analyses for END are shown in Table 3.

Table 3.

Logistic Regression Analysis of the Relationship Between PT, SIRI and SII Levels with END.

	Variables	OR	95% CI	P
Unadjusted	PT	1.098	0.900–1.339	0.359
	SIRI	1.842	1.167–2.907	0.009
	SII	0.999	0.998–1.001	0.315
Model 1	PT	1.121	0.890–1.412	0.330
	SIRI	2.019	1.221–3.337	0.006
	SII	0.999	0.998–1.001	0.254
Model 2	PT	1.440	0.884–2.344	0.143
	SIRI	1.981	1.097–3.576	0.023
	SII	0.999	0.998–1.001	0.375
Model 3	PT	1.611	0.903–2.873	0.106
	SIRI	1.844	1.038–3.277	0.037
	SII	1.000	0.998–1.001	0.592

Model 1 was adjusted for age, sex;

Model 2 was adjusted for Model 1 + vascular risk factors, including cigarette smoking, alcohol consumption, atrial fibrillation, coronary heart disease, hypertension, diabetes mellitus, dyslipidemia, and previous stroke;

Model 3 was adjusted for Model 2 + baseline NIHSS score, baseline SBP and DBP, baseline blood glucose, ONT, and TOAST.

Abbreviations: OR, odds ratio; 95% CI, 95% confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; ONT, onset to needle time; TOAST, Trial of Org 10172 in acute stroke treatment; PT, prothrombin time; SIRI, systemic inflammation response index; SII, systemic immune-inflammation index; mRS, modified Rankin Scale.

The Association of PT, SIRI and SII Levels with Poor Outcome in AIS Patients After IVT

We divided all eligible patients into favorable outcome and poor outcome groups. Comparisons of baseline blood glucose, NIHSS scores, and ONT were statistically significant (P < 0.05). PT (10.40 vs 10.90, P < 0.001), SIRI levels (0.74 × 10⁹/L vs 1.40 × 10⁹/L, P < 0.001) and SII levels (453.79 × 10⁹/L vs 854.12 × 10⁹/L, P < 0.001) were significantly higher in patients with poor outcomes. Comparisons of the clinical characteristics according to favorable and poor outcomes are presented in Table 4.

Table 4.

Clinical Characteristics of Patients in Favorable Outcome and Poor Outcome.

Variables	Favorable outcome (n = 175) mRS＜2	Poor outcome (n = 106) mRS ≥ 2	P
Age, years (IQR)	73 (65–79)	74 (66–81)	0.236
Male, n (%)	107 (61.1)	61 (57.5)	0.551
Cigarette smoking, n (%)	65 (37.1)	42 (39.6)	0.678
Alcohol consumption, n (%)	59 (33.7)	31(29.2)	0.436
Hypertension, n (%)	111(63.4)	69 (65.1)	0.778
Diabetes mellitus, n (%)	36 (20.6)	28 (26.4)	0.258
Coronary heart disease, n (%)	26 (14.9)	15 (14.2)	0.871
Atrial fibrillation, n (%)	10 (5.7)	9 (8.5)	0.369
Previous stroke, n (%)	43 (24.6)	28 (26.4)	0.730
Dyslipidemia, n (%)	11 (6.3)	6 (5.7)	0.831
Baseline SBP, mm Hg (Mean ± SD)	153 ± 24	157 ± 22	0.212
Baseline DBP, mm Hg (IQR)	88 (79–98)	88 (78–99)	0.425
Baseline blood glucose, mmo/L (IQR)	7.3 (6.3–9.0)	7.8 (6.8–11.3)	0.010
Baseline NIHSS score (IQR)	2 (2–4)	6 (4–9)	0.000
ONT, min (IQR)	164 (120–190)	180 (135–209)	0.035
TOAST			0.390
Large-artery atherosclerosis, n (%)	72 (42.9)	55 (51.9)	-
Small-vessel occlusion, n (%)	71 (40.6)	33 (31.1)	-
Cardioembolism, n (%)	9 (5.1)	7 (6.6)	-
Other determined etiology, n (%)	8 (4.6)	3 (2.8)	-
Undetermined etiology, n (%)	15 (8.6)	8 (7.5)	-
PT(s)	10.40 (9.90–10.80)	10.90 (10.30–11.23)	<0.001
SIRI (10⁹/L, IQR)	0.74 (0.50–1.07)	1.40 (0.94–2.35)	<0.001
SII(10⁹/L, IQR)	453.79 (313.18–621.56)	854.12 (531.59–1351.10)	<0.001

Abbreviations: IQR, inter quartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; ONT, onset to needle time; TOAST, Trial of Org 10172 in acute stroke treatment; PT, prothrombin time; SIRI, systemic inflammation response index; SII, systemic immune-inflammation index; mRS, modified Rankin Scale.

Spearman correlation analysis indicated that PT, SIRI, and SII were all positively correlated with mRS scores 3 months after IVT (rs = 0.307, P < 0.05; rs = 0.411, P < 0.05; rs = 0.409, P < 0.05). (Figure 2B-2D)

Logistic regression analyses were used to explore the association between PT, SIRI, and SII levels and poor outcome. In multivariate analysis, PT level was independently associated with poor outcome adjusted by Model 1 (OR = 1.683; 95% CI: 1.641–2.432; P = 0.006), Model 2 (OR = 1.693; 95% CI: 1.143–2.508; P = 0.009), and Model 3 (OR = 1.833; 95% CI: 1.161–2.893; P = 0.009). SIRI level adjusted by Model 1 (OR = 1.979; 95% CI: 1.075–3.645; P = 0.028), Model 2 (OR = 2.278; 95% CI: 1.186–4.375; P = 0.013), and Model 3 (OR = 2.166; 95% CI: 1.014–4.629; P = 0.046) was independently associated with poor outcome. SII level adjusted by Model 1 (OR = 1.002; 95% CI: 1.002–1.003; P = 0.003), Model 2 (OR = 1.002; 95% CI: 1.002–1.003; P = 0.003), and Model 3 (OR = 1.002; 95% CI: 1.002–1.003; P = 0.021) was independently associated with poor outcome. Table 5 shows the results of the logistic regression analysis for poor outcomes after IVT.

Table 5.

Logistic Regression Analysis of the Relationship Between PT, SIRI and SII Levels with Poor Outcome.

	Variables	OR	95% CI	P
Unadjusted	PT	1.700	1.185–2.438	0.004
	SIRI	1.947	1.079–3.514	0.027
	SII	1.002	1.001–1.003	0.001
Model 1	PT	1.683	1.641–2.432	0.006
	SIRI	1.979	1.075–3.645	0.028
	SII	1.002	1.001–1.003	0.003
Model 2	PT	1.693	1.143–2.508	0.009
	SIRI	2.278	1.186–4.375	0.013
	SII	1.002	1.001–1.003	0.003
Model 3	PT	1.833	1.161–2.893	0.009
	SIRI	2.166	1.014–4.629	0.046
	SII	1.002	1.000–1.003	0.021

Model 1 was adjusted for age, sex;

Model 3 was adjusted for Model 2 + baseline NIHSS score, baseline SBP and DBP, baseline blood glucose, ONT, and TOAST.

The ROC Curve Analysis of PT, SIRI and SII in the Diagnosis of END and Short-Term Poor Outcome in AIS Patients with IVT

According to ROC analysis, the SIRI cut-off value that best-distinguished END was 2.001 × 10⁹/L with a sensitivity of 0.438 and a specificity of 0.872, with the AUC of 0.666 (95% CI = 0.521–0.810, P = 0.026). Nevertheless, the cut-off values of PT and SII could not well-distinguished END. The area under curve (AUC) was 0.633 (95% CI = 0.500–0.765, P = 0.075) and 0.601 (95% CI = 0.473–0.730, P = 0.173), respectively (Figure 3).

Figure 3.

(A-B) The value of ROC calculation of PT, SIRI, and SII in the diagnosis of END and short-term poor outcome in AIS patients. Abbreviations: PT, prothrombin time; SIRI, systemic inflammation response index; SII, systemic immune-inflammation index.

In the diagnosis of short-term poor outcome in AIS Patients with IVT, the PT had low accuracy. The PT cut-off value that distinguished 3-month poor outcome was 10.85s with a sensitivity of 0.509 and a specificity of 0.771. The area under curve (AUC) was 0.669 (95% CI = 0.605–0.734, P < 0.001). The SIRI cut-off value that best-distinguished 3-month poor outcome was 0.926 × 10⁹/L with a sensitivity of 0.764 and a specificity of 0.697. The AUC was 0.773 (95% CI = 0.715–0.831, P < 0.001). The SII cut-off value that best-distinguished 3-month poor outcome was 621.68 × 10⁹/L with a sensitivity of 0.717 and a specificity of 0.754. The area under curve (AUC) was 0.787 (95% CI = 0.731-0.843, P < 0.001) (Figure 3).

Discussion

The association between PT levels and clinical outcomes of stroke has been reported in previous studies. Wang et al indicated that the prolongation of PT from baseline to 24 h after IVT increases the risk of 3-month unfavorable outcomes in AIS patients.⁴ However, the relationship between baseline PT levels and clinical outcomes remains unclear in patients undergoing IVT. In this study, the level of baseline PT after rt-PA thrombolysis was significantly associated with the 3-month unfavorable functional outcome in stroke patients. Whereas, no relationship was observed between PT and END. Our study also suggested that the PT level before IVT might be a potential predictor for 3-month poor prognosis in AIS patients after IVT, but not for the END.

PT is used to reflect the efficiency of the extrinsic coagulation pathway initiated by tissue factor. Although known as a “fibrin specific” thrombolytic agent, rt-PA can produce a partial conversion of plasminogen to plasmin in the absence of fibrin. The plasmin is capable of hydrolyzing the coagulation factors which participated in the extrinsic coagulation pathway, such as factors II, V, VII and X, resulting in the prolongation of PT. The decrease of coagulation factors and prolongation of PT might increase the probability of hemorrhagic transformation, while our study was unable to verify the above assumption owing to the limited number of bleeding events. Although the baseline PT failed to reach the threshold of hemorrhagic transformation in the acute phase, it did increase the risk of 3-month poor prognosis among IVT patients. Yet the underlying mechanisms are not fully understood.

Cerebral ischemia is accompanied by a marked inflammatory reaction that is initiated by ischemia-induced expression of cytokines, chemokine, and adhesion molecules. Inflammatory cytokines or chemokines form immediately after the onset of cerebral ischemia, stimulate the expression of adhesion molecules on leukocytes and endothelial cells and cause the adherence and infiltration of various inflammatory cells (neutrophils, monocytes/macrophages, different subtypes of T cells and other inflammatory cells) into the ischemic zone aggravating the brain injury.¹¹ Inflammatory factors accumulate in the focus and surrounding tissues for several weeks, leading to secondary brain injury of stroke, aggravation of patients’ condition, and occurrence of adverse prognostic events.¹² For example, cerebral ischemia can stimulate monocytes to generate inflammatory mediators, resulting in more extensive brain tissue destruction.¹³ Monocytes can also activate platelets to become platelet-monocyte aggregates (PMA), facilitating the liberation of an inflammatory response, adhesion, and vasoactive substances. The PMA can also promote thrombosis and vascular occlusion, causing hemodynamic changes and exacerbating the cerebral ischemic injury.¹⁴ The above mechanisms might explain why currently various inflammatory biomarkers are associated with AIS patient's severity and prognosis, such as SIRI combining neutrophil-monocyte-lymphocyte counts and SII combining platelet-neutrophil-lymphocyte counts.

SIRI index is obtained according to the counts of neutrophils, monocytes, and lymphocytes in peripheral blood, which is easy to obtain, non-invasive, and cheap marker. SII, combining platelet counts and leukocytes subpopulations, represented the systemic immune-inflammation status.⁶ SIRI and SII are new inflammatory markers, which are found to be closely related to tumors and cardiovascular ischemia.^15–17 In recent years, elevated SIRI was found to be a new predictor of END and poor functional outcome of AIS, and SII was associated with severity of AIS patients.^18–20 There are few studies on the relationship between above inflammatory indexes and clinical outcome such as END and short-term prognosis of AIS patients undergoing IVT. Ma et al showed that SIRI was independent predictors of 3-month prognosis in AIS patients after IVT.⁷ SIRI can be a novel short-term prognostic biomarker for AIS patients treated with IVT. Weng et al found that SII was correlated with stroke severity at admission and can be a novel prognostic biomarker for AIS patients treated with IVT.²¹ However, they did not focus on the association between SIRI, SII, and END. Our study found that both SIRI and SII were independent risk factors for the poor 3-month outcome after IVT, and could be regarded as predictive indicators of short-term prognosis in patients with IVT, which was consistent with previous studies. Another major finding of our study was the association between elevated SIRI level and END in patients with AIS after IVT. Nevertheless, only SIRI was associated with END. SII level was not related with END. It showed that SIRI index could effectively evaluate the END of AIS patients. Multivariate logistic analysis indicated that SIRI was independent risk factors for END of AIS patients. Results of ROC analysis demonstrated that SIRI had certain accuracy in predicting the END of AIS patients.

This study has some limitations. First, this study retrospectively analyzed data from a single medical center. Second, blood samples were only tested before intravenous thrombolytic therapy, and changes that may be affected by IVT treatment were not observed. In addition, larger prospective cohorts should be established to observe the predictive roles of PT, SIRI, and SII in patients with AIS who receive IVT and to explore the underlying mechanisms. Further large-scale, polycentric, and prospective studies with more testing points and comprehensive indicators are needed to verify our results.

Conclusion

Our study confirmed that SIRI level was an independent associated factor and predictor of END in patients with AIS after IVT. We also found that higher PT, SIRI, and SII levels were all independently associated with 3-month poor outcome, and all of which could be potential short-term prognostic indicators for AIS patients undergoing rt-PA.

Footnotes

Acknowledgments

The authors gratefully appreciate all of the participants and staff for their contributions.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Medical tracking program by Hebei Province, Advanced Programs of Postdoctoral Research in Hebei Province, National Natural Science Foundation of China, Clinical Medical Talents Training Project funded by Hebei Provincial, (grant number No. GZ2023002, No. B2022003037, No. 82001229, No. ZF2023187).

Disclosure

The authors do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Ethical Approval

This study was conducted in accordance with the guidelines from the Helsinki Declaration and was approved by the Ethics Committees of Hebei General Hospital.

Informed Consent

This study was conducted without requiring individual patient informed consent under the common rule.

ORCID iD

Baoming Yang

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