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

doi:10.21203/rs.3.rs-2950642/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-2950642/v1

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Journal Publication

published 30 Sep, 2023

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Background and purpose

This study aimed to explore the association between the systemic inflammatory index (SIRI), systemic immune-inflammatory index (SII), and inflammatory prognosis index (IPI) with the 90-day outcomes of patients with acute ischemic stroke (AIS) after intravenous thrombolysis.

Methods

190 AIS patients who underwent intravenous thrombolysis from September 2019 to December 2022 were enrolled in the present study. The National Institutes of Health Stroke Scale (NIHSS) and modified Rankin score (mRS) were assessed in AIS patients who met the inclusion criteria before thrombolysis, immediately after thrombolysis, and at 90 days. Peripheral venous blood was collected within 24 hours after admission and used to assess the following parameters: neutrophil count, lymphocyte count, monocyte count, platelet count, average platelet volume, and albumin. Next, the SIRI, SII, and IPI values were calculated, respectively. The correlation among SIRI, SII, IPI, and admission NIHSS scores was examined by Spearman correlation analysis. ROC curve analysis was conducted to determine the optimal cut-off value of SIRI, SII, and SIRI and their corresponding sensitivity and specificity to evaluate their predictive value on admission for poor prognosis. To investigate whether high SIRI, SII, and IPI were independent predictors of poor outcomes within 90 days, variables with P-value < 0.05 during univariate analysis were included in multivariate analysis.

Results

Compared with the good outcome group, the poor outcome group had higher age, baseline NIHSS, neutrophil count, mRS, NLR, SIRI, IPI, and SII, while the lymphocyte count was lower. Spearman correlation analysis showed that the SIRI, IPI, and SII levels significantly correlated with the admission NIHSS score (r = 0.338, 0.356, 0.427, respectively; P_s < 0.001). The Mann-Whitney U test showed that the SIRI, SII, and IPI levels in patients with moderate and severe AIS were higher than in patients with mild AIS (P < 0.001). Univariate analysis showed that admission NIHSS, NLR, SIRI, SII, and IPI were significantly correlated with poor 90-day prognosis. Although age, smoking and drinking history, and underlying diseases were not significantly associated with prognosis, they were considered potential risk factors for AIS and included in the analysis as potential confounding factors. Using the optimal cut-off value determined by ROC analysis, multivariate logistic regression analysis revealed high SIRI, SII, and IPI values as independent risk factors for poor 90-day prognosis (OR = 2.867, 3.721, and 2.877, respectively).

Conclusions

High SIRI, IPI, and SII values are correlated with poor 90d outcomes in AIS patients undergoing intravenous thrombolysis.

systemic inflammatory index

systemic immune-inflammatory index

inflammatory prognostic index

acute ischemic stroke

intravenous thrombolysis

Approximately 70% of all strokes are cases of acute ischemic stroke (AIS), which results from blockage or a clot that impedes blood vessels carrying blood and oxygen to the brain[1]. This condition is known for its high mortality and disability rates, causing a significant burden on families and society[2]. Intravenous thrombolytic therapy has become the mainstay of treatment for AIS within a 4.5-hour time window. Our previous studies found that N/L was associated with the prognosis, recurrence, and death of acute ischemic stroke at 90 days[3]. Herein, we further analyzed the correlation of NLR-based composite inflammatory indicators with the prognosis of AIS patients that underwent intravenous thrombolysis.

In recent years, three novel inflammatory markers have been introduced: The systemic inflammatory index (SIRI) which uses peripheral blood monocytes and the neutrophil to lymphocyte ratio (NLR); the systemic immune-inflammatory index (SII), which incorporates platelets and the neutrophil to lymphocyte ratio (NLR), and the inflammatory prognosis index (IPI), which takes into account C-reactive protein (CRP), neutrophil to lymphocyte ratio (NLR), and serum albumin protein (ALB) as a prognostic index. We explored the association of systemic inflammatory index (SIRI), systemic immune inflammatory index (SII), and inflammatory prognosis index (IPI) with 90d outcomes in AIS patients undergoing intravenous thrombolysis by analyzing the correlation with NLR-based composite inflammatory indicators.

Study design and participants

We used data from the stroke center of Jiangsu Province Hospital of Chinese Medicine from October 2019 to December 2022. An observational cohort study of 232 patients with acute ischemic stroke who underwent intravenous thrombolysis with alteplase (rt-PA) was enrolled. The study included 190 patients with acute ischemic stroke who met the following inclusion criteria: (1) aged over 18 years old; (2) diagnosed with AIS according to the Chinese AIS diagnosis and treatment guidelines; (3) received rt-PA intravenous thrombolysis within 4.5 hours of symptom onset[4]. Exclusion criteria consisted of (1) bridging therapy patients; (2) patients with malignant tumors, autoimmune diseases, or blood system diseases; (3) acute or chronic infections; (4) severe hepatic or renal insufficiency; (5) patients with missing follow-up data. A flow chart showing the patient selection is shown in Fig. 1.

The Institutional Research Review Board of the Affiliated Hospital of the Nanjing University of Chinese Medicine approved this study which was conducted in accordance with the Declaration of Helsinki (2017NL-012-01).

Data acquisition

Upon admission, all participants underwent a standard assessment of their demographic characteristics, including sex, age, National Institutes of Health Stroke Scale (NIHSS) score, vascular risk factors (hypertension, diabetes, atrial fibrillation, coronary heart disease, prior stroke, smoking, and alcohol history), and preonset medication (antiplatelet drugs, statins, and anticoagulants).

Within 24 hours of admission, all AIS patients underwent assessment using the NIHSS, which ranged from 0 to 42. Patients were classified as mild (NIHSS ≤ 5) and moderate-to-severe (NIHSS > 5) stroke [5]. The modified Rankin score (mRS) was used to assess the 90-day outcomes during follow-up, with a score of 0–2 indicating a favorable prognosis and a score of 3–6 indicating a poor prognosis[6].

Measurement of composite inflammatory ratios based on blood tests

Samples of venous blood were taken within 24 hours of admission. If blood tests were conducted more than once during the 24-hour period, we used the results of the first test. We recorded the data of neutrophils (N), lymphocytes (L), platelets (PLT), monocytes (M), albumin (ALB), and high-sensitivity C-reactive protein (Hs-CRP). These counts were used to determine the inflammatory biomarkers listed below: neutrophil-to-lymphocyte ratio (NLR = N/L), Systemic Immune-inflammation Index (SII = P×[N/L]), System Inflammation Response Index (SIRI = N×[M/L]) and Inflammatory Prognosis Index(IPI = CRP×NLR/ALB).

Statistical analysis

The Mann-Whitney or T-tests were used to compare continuous factors based on the data distribution, whereas Chi-square tests were used to compare categorical data. The Spearman correlation test examined the association between each indicator and the NIHSS score. Each indicator's ability to discriminate for a 90-day prognosis was tested using the receiver operating characteristic (ROC) curve, and the optimal cut-off value was identified based on the greatest sum of sensitivity and specificity. Risk variables with a P-value of less than 0.05 during univariate analysis were incorporated into the multivariate analysis based on the optimal cut-off value. The results were presented as odds ratios (ORs) and 95% confidence intervals (CIs). Kaplan-Meier survival curves were plotted based on the optimal cut-off value, and log-rank tests were conducted for each indicator. All statistical analyses were performed using the Statistical Package for the Social Sciences 25.0 (SPSS; IBM, USA), and a two-sided P < 0.05 was statistically significant.

From October 2019 to December 2022, 232 AIS patients that underwent intravenous thrombolysis were screened in this study. A total of 42 patients were excluded, due to severe liver and kidney dysfunction, and metastasized tumors (n=18), bridging treatments after intravenous thrombolysis (n=10), and incomplete follow-up data (n=14). Ultimately, a total of 190 subjects were included for the final analysis.

Patient Characteristics

Compared to the good prognosis group, the poor prognosis group had higher age, baseline NIHSS scores, neutrophil counts, discharge mRS scores, and Random Blood Glucose (RBG), NLR, SIRI, IPI, and SII values but lower lymphocyte counts.

Table 1 Demographics and clinical characteristics of AIS patients with different prognoses.

Characteristics		Total (n=190)	Good Prognosis Group (n=157)	Poor Prognosis Group (n=33)	p
Gender(male ,n,%)		122(64.211)	102(64.968)	20(60.606)	0.635
Age, mean(±SD)		70.389±11.675	69.854±11.201	72.939±13.416	0.100
Vascular risk factors
Smoking, n (%)	yes	51(26.842)	45(28.662)	6(18.182)	0.217
Smoking, n (%)	no	139(73.158)	112(71.338)	27(81.818)	0.217
Drinking, n (%)	yes	28(14.737)	26(16.561)	2(6.061)	0.122
Drinking, n (%)	no	162(85.263)	131(83.439)	31(93.939)	0.122
Diabetes, n (%)	yes	56(29.474)	46(29.299)	10(30.303)	0.908
Diabetes, n (%)	no	134(70.526)	111(70.701)	23(69.697)	0.908
Hypertension, n (%)	yes	128(67.368)	104(66.242)	24(72.727)	0.470
Hypertension, n (%)	no	62(32.632)	53(33.758)	9(27.273)	0.470
CHD, n (%)	yes	33(17.368)	25(15.924)	8(24.242)	0.252
CHD, n (%)	no	157(82.632)	132(84.076)	25(75.758)	0.252
AF, n ( %)	yes	13(6.842)	11(7.006)	2(6.061)	0.845
AF, n ( %)	no	177(93.158)	146(92.994)	31(93.939)	0.845
Medical history
Antiplatelet, n (%)	yes	44(23.158)	37(23.567)	7(21.212)	0.771
Antiplatelet, n (%)	no	146(76.842)	120(76.433)	26(78.788)	0.771
Statins, n (%)	yes	27(14.211)	22(14.013)	5(15.152)	0.865
Statins, n (%)	no	163(85.789)	135(85.987)	28(84.848)	0.865
Antihypertensive, n (%)	yes	117(61.579)	95(60.510)	22(66.667)	0.509
Antihypertensive, n (%)	no	73(38.421)	62(39.490)	11(33.333)	0.509
Hypoglycemic drug, n(%)	yes	52(27.368)	42(26.752)	10(30.303)	0.677
Hypoglycemic drug, n(%)	no	138(72.632)	115(73.248)	23(69.697)	0.677
TOAST, n(%)	LAA	110(57.895)	92(58.599)	18(54.545)	0.029
	CE	25(13.158)	16(10.191)	9(27.273)
	SAO	47(24.737)	43(27.389)	4(12.121)
	Unclassified	8(4.211)	6(3.822)	2(6.061)
OCSP, n(%)	TACI	34(17.895)	25(15.924)	9(27.273)	0.142
	PACI	63(33.158)	52(33.121)	11(33.333)
	LACI	69(36.316)	62(39.490)	7(21.212)
	POCI	24(12.632)	18(11.465)	6(18.182)
NIHSS, median[IQR]		4.000[3.000,6.000]	3.000[2.000,5.000]	7.000[6.000,10.000]	<0.001
SBP, mean(±SD)		153.000±23.806	152.204±23.473	156.788±24.983	0.317
DBP, mean(±SD)		85.637±14.868	84.701±14.799	90.091±14.381	0.056
Laboratory data
LDL, median[IQR]		2.750[2.130,3.320]	2.750[2.130,3.320]	2.810[2.140,3.280]	0.875
HDL, median[IQR]		1.260[1.080,1.500]	1.260[1.100,1.500]	1.220[1.060,1.390]	0.458
RBG, median[IQR]		6.180[4.920,8.650]	6.050[4.910,7.680]	7.420[5.820,10.570]	0.024
Lymphocyte, median[IQR]		1.750[1.260,2.350]	1.830[1.330,2.410]	1.450[0.930,1.770]	<0.001
Monocyte, median[IQR]		0.500[0.390,0.660]	0.510[0.390,0.660]	0.460[0.400,0.670]	0.946
Neutrophil, median[IQR]		4.420[3.290,5.550]	4.320[3.210,5.210]	4.970[4.130,8.250]	0.001
Platelet, median[IQR]		186.000[153.000,224.000]	188.000[154.000,224.000]	177.000[153.000,209.000]	0.372
MPV, median[IQR]		10.300[9.700,11.200]	10.300[9.700,11.200]	10.400[9.700,11.200]	0.818
NLR, median[IQR]		2.368[1.596,3.893]	2.159[1.463,3.566]	3.509[2.838,7.237]	<0.001
CRP, median[IQR]		0.500[0.500,1.740]	0.500[0.500,1.500]	0.740[0.500,4.260]	0.107
Albumin, median[IQR]		40.600[37.700,42.900]	40.400[37.700,42.600]	41.600[37.700,43.500]	0.176
IPI, median[IQR]		0.047[0.025,0.136]	0.043[0.023,0.113]	0.109[0.041,0.522]	<0.001
SII, median[IQR]		451.644[293.248,715.759]	413.571[264.923,654.741]	636.842[461.159,1088.124]	<0.001
SIRI, median[IQR]		1.179[0.732,2.036]	1.083[0.707,1.844]	1.912[1.298,3.726]	<0.001

Abbreviations: CHD, Coronary heart disease; AF, Atrial fibrillation; NIHSS, National Institutes of Health Stroke Scale; MPV, Mean Platelet Volume; NLR, Neutrophil to Lymphocyte Ratio; CRP, C-reactive protein; IPI, Inflammatory Prognosis Index; SII, Systemic Immune-inflammation Index; SIRI, System Inflammation Response Index; RBG, Random Blood Glucose.

Correlation Between IPI, SII, and SIRI Level and Admission NIHSS Score in different groups of AIS Patients

Spearman correlation analysis revealed a significant correlation between IPI, SII, and SIRI values with NIHSS score (r=0.338, 0.356, and 0.427, respectively, P<0.05) (Figure 2A, 2C, and 2E). IPI, SII, and SIRI levels in moderate to severe AIS patients (NIHSS score <5=0) remained higher than in mild AIS patients (NIHSS score≥5=1) (median:0.101 vs. 0.034, 675.669 vs. 377.752, 1.685 vs. 1.010), according to the Mann-Whitney U-test (Figure 2B, 2D, and 2F).

Levels of composite inflammatory indicators with different prognosis

Figure 3 shows the violin plots of SIRI, IPI, and SII between the two groups. Patients in the good prognosis group had lower SIRI, IPI, and SII levels than in the poor prognosis group (P<0.05).

Prognostic value of SIRI, IPI, and SII in AIS Patients

ROC analysis revealed that a SIRI cut-off value of 1.298×109 /L demonstrated a sensitivity of 0.758 and a specificity of 0.618 for poor 3-month outcome, with an AUC of 0.720 (95% CI=0.612-0.751, P<0.05)(Figure 4). The SII index's predictive accuracy was poor. A 3-month poor outcome was differentiated by the SII cut-off value of 392.903×10⁹ /L, which had a sensitivity of 0.879, a specificity of 0.465, and an AUC of 0.715 (95% CI: 0.546 to 0.826; P < 0.05). An IPI cut-off value of 0.223 yielded a sensitivity of 0.424 and specificity of 0.873 for a poor 3-month outcome, with an AUC of 0.701 (95% CI = 0.604 to 0.826, P < 0.05).

Univariate and Multivariate Logistic Analysis of SIRI, SII, IPI, and Poor 3-Month Outcome in AIS Patients

During univariate regression analysis, the admission NIHSS scores, NLR, SIRI, SII, and IPI were significantly associated with poor outcomes at three months (P <0.05). To ascertain whether elevated SIRI, SII, and IPI were independent prognostic markers of poor outcomes within three months, variables with P <0.05 during the binary analysis were included in multivariate analysis.

The presence of comorbidities were considered as potential risk factors for AIS. These factors were included as confounding variables, despite not showing a statistically significant difference between age, smoking, alcohol history and prognosis. However, based on multivariate logistic regression analysis using the optimal threshold determined by ROC analysis, it was found that high SIRI, SII, and IPI values were independently associated with poor prognosis at three months (OR=2.867, 3.721, 2.877; P <0.05) (Table 2).

Table 2 Univariate and Multivariate Logistic Analysis of SIRI, SII, IPI, and Poor 3-Month Outcome in AIS Patients

	Model 1		Model 2		Model 3
	OR(95%CI )	P-value	OR(95%CI)	P-value	OR(95%CI)	P-value
SIRI	4.788(2.030,11.293)	<0.001	5.283(2.158,12.930)	<0.001	2.867(1.062,7.740)	0.038
SII	6.301(2.115,18.766)	0.001	6.447(2.139,19.436)	0.001	3.721(1.123,12.335)	0.032
IPI	5.047(2.191,11.629)	<0.001	5.338(2.281,12.495)	<0.001	2.877(1.064,7.782)	0.037

Model 1 was univariate analysis. SIRI, SII, and IPI were associated with different outcomes at three months (P <0.05)

Model 2 was adjusted for gender and age.

Model 3 was adjusted for gender, age, Random Blood Glucose, neutrophil, lymphocyte, admission NIHSS scores, smoking, drinking history, and comorbidities. High SIRI, SII, and IPI (P<0.05) were independent predictors of poor 3-month prognosis, according to multivariate logistic regression analysis (OR, odds ratio; CI, confidence interval).

Kaplan-Meier survival curve of each indicator

This study also investigated whether each indicator was associated with 90-day recurrence or mortality. As shown in Figure 5, SIRI>1.298×10⁹, SII>392.903×10⁹, and IPI>0.223 were associated with the risk of 90-day recurrence or mortality.

It is now understood that during an acute ischemic stroke, a substantial amount of inflammatory substances are produced[7]. Although the prognosis of AIS has been linked to the neutrophil-to-lymphocyte ratio, there is a scarcity of research focusing on composite inflammatory indicators like SIRI, IPI, and SII. Some clinical studies have confirmed that SIRI, IPI, and SII are related to disease progression in tumors and cardiovascular diseases[8-10]. Some human clinical trials and mouse experiments have confirmed that platelet-neutrophil interaction plays a role in clinical ischemic disease and microthrombosis in stroke[11-13]. Our study further explored the relationship between SIRI, IPI, and SII and the short-term prognosis of intravenous thrombolysis in AIS patients. According to the clinical and laboratory (SIRI, IPI, SII, etc.) data of AIS patients with intravenous thrombolysis registered in the Stroke Center of Jiangsu Provincial Hospital of Chinese Medicine, we evaluated the severity of admission and the 90-day clinical outcome of AIS patients. The results showed that SIRI, IPI, and SII could effectively evaluate the severity of AIS patients upon admission. Moreover, ROC analysis showed that SIRI, IPI, and SII exhibited good performance in predicting the 90-day prognosis of AIS patients. Additionally, SIRI, IPI, and SII were linked to the 90-day recurrence or mortality of AIS patients. Higher SIRI, IPI, and SII levels were associated with a greater risk of recurrence or mortality. In addition, univariate and multivariate binary logistic regression showed that SIRI, IPI, and SII were independent predictors of short-term prognosis in AIS patients.

Interestingly, some AIS patients had a poor prognosis despite having lower NIHSS scores on admission. Such patients should be monitored for dynamic changes in their SIRI, IPI, and SII indices. Even if their initial NIHSS scores are not high, those with elevated SIRI, IPI, and SII levels may require more proactive intervention to enhance their short-term prognosis. However, it should be noted that the ROC analysis revealed relatively low accuracy for SII, warranting further studies to validate its predictive value.

Indeed, it should be borne in mind that the indicators SIRI, IPI, and SII are all based on NLR. During AIS, neutrophils are first produced in the infarction core and penumbra, releasing inflammatory factors on the endothelial cell membrane and basement membrane, damaging the blood-brain barrier and aggravating brain edema[14], and inducing the generation of oxygen free radicals to aggravate brain damage[15]. Studies have found that neutrophils are correlated with stroke severity, infarct size, and prognosis[16].

In the present study, SIRI was used to examine the role of monocytes in AIS based on NLR. In AIS, monocytes undergo early migration and infiltration. Under the stimulation of OX-LDL, after differentiation into macrophages, they engulf low-density lipoprotein to become foam cells and then secrete various inflammatory factors[17]. Activated monocytes also increase vascular permeability and destroy the blood-brain barrier by secreting VEGF[18]. Our results substantiated that compared to peripheral blood cell ratios such as NLR, LMR, and PLR, the SIRI index exhibits better performance in reflecting the state of systemic inflammatory response and is an independent predictor of the prognosis. The Mann-Whitney U-test showed that the SIRI index of the moderate-to-severe group was higher than the mild group, while the SIRI index of the poor short-term prognosis group was significantly higher than the good prognosis group. Multivariate analysis showed that a high SIRI index was an independent predictor of poor 90-day prognosis in AIS patients. We found that for AIS patients with SIRI>1.298×10⁹, the incidence of poor prognosis, including the risk of recurrence or death, was increased at 90 days.

It has been established that SII mirrors three pathways of thrombosis, inflammation, and immune response, which are important pathophysiological processes in AIS. A high SII suggests thrombogenic and immune dysregulation states, which have been associated with major adverse events [19]. Our results substantiate that the optimal threshold of SII (392.903×109) exhibits good performance in predicting prognosis of AIS patients at 90 days. However, its accuracy is lower than the SIRI and IPI index.

Current evidence suggests that IPI is related to CRP, NLR, and ALB (IPI=CRP×NLR/ALB). Albumin is the main protein in human blood, which maintains body nutrition and balances colloid osmotic pressure[20]. Albumin has many physiological functions, such as anti-oxidation, anti-inflammation, inhibition of platelet aggregation, and thrombus formation[21]. Studies on animal models of AIS have shown that albumin has neuroprotective effects, while clinical studies have substantiated that low albumin levels can lead to poor prognosis of AIS[22, 23].

CRP is an index that clinically reflects the level of inflammation in the body during the early stage of infection. It has been established that CRP levels are related to the severity, infarct size, and prognosis of AIS patients[24]. CRP may interact with various cells, such as the vascular endothelium, promote vascular inflammation, destabilize or even rupture atherosclerotic plaque, and then form thrombi[25, 26]. There is an increasing consensus suggesting that elevated CRP levels are associated with the risk of all-cause death and cardiovascular and cerebrovascular events[27-29]. Consistently, a meta-analysis showed that CRP concentration was associated with the risk of death in AIS[30].

SIRI, IPI, and SII represent novel predictive indices derived from the neutrophil-to-lymphocyte ratio (NLR), combining various inflammatory parameters. These composite ratios offer a practical and reproducible approach to predicting prognosis. Moreover, they can be integrated with other biomarkers to develop more robust prediction models, facilitating more precise and timely decision-making in clinical practice. Our results revealed a correlation between SIRI, SII, and IPI and early prognostic outcomes in AIS patients, highlighting the potential of these composite inflammatory indices as novel predictors. This hypothesis was further validated through various analyses, including ROC, multivariate analysis, and survival curve. Our findings corroborate that higher SIRI, SII, and IPI values indicate greater disease severity at admission and an increased incidence of poor outcomes at 90-day prognosis. As a result, these indices offer a new approach to conducting stratified interventions in AIS patients, potentially leading to improved prognosis in clinical practice.

This study has several limitations that should be acknowledged. Firstly, it is important to note that this was not a multi-center clinical trial but a retrospective analysis based on stroke registration data. This introduces a potential bias and limits the generalizability of the findings. To establish the predictive value of these composite inflammatory indices, larger population studies are needed for verification, and future research should consider conducting multi-center prospective studies.

Additionally, the present study did not investigate the diagnostic and predictive value of the composite inflammatory indices in relation to complications associated with AIS. Furthermore, patients who underwent mechanical thrombectomy were not included in the analysis, which may limit the applicability of the results to this specific subgroup.

Finally, it should be recognized that composite inflammatory indices can vary significantly during the course of hospitalization. Future studies should focus on dynamically monitoring these indices throughout the entire treatment process, rather than solely relying on measurements taken before thrombolysis or at admission.

AIS: Acute ischemic stroke; NLR: Neutrophil to lymphocyte ratio; PLR: Platelet to lymphocyte ratio; LMR: Lymphocyte to monocyte ratio; HT: Hemorrhagic transformation; OTT: Onset to treatment time; PAO: Proximal arterial occlusion; TOAST: Trial of Org 10172 in Acute Stroke Treatment; TC: Total cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; FBG: Fasting blood glucose; Hs-CRP: Hypersensitive C-reactive protein; OR: Odds ratio; 95% CI: 95% Confidence interval; AUC: Area under the curve.

ACKNOWLEDGMENTS

This publication was made possible by support from brain center in Jiangsu Province Hospital of Chinese Medicine.

Authors’ contributions

Fei Ma and Lulu Li were mainly involved in study design, data analysis, data interpretation, and manuscript preparation. Liang Xu, Jiacheng Wu and Aimei Zhang were mainly involved in data acquisition and data analysis. Junqi Liao, Jingyi Chen, Yunze Li, and Li Li were mainly involved in data acquisition. Zhaoyao Chen and Wenlei Li were mainly involved in data analysis and manuscript preparation. Qing Zhu, Yuan Zhu and Minghua Wu were mainly involved in study design, data interpretation, and manuscript preparation. The authors read and approved the final manuscript.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 82274428, 81973794), National Administration of Traditional Chinese Medicine: Evidence-Based Capacity Building Project (2019XZZX-NB007), 333 high level talents training project in Jiangsu (grant no. BRA 2016507), Jiangsu Province Administration of Chinese Medicine (ZT202102).

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine(2017NL-012-01). Informed consent was obtained from participants or their legal representatives.

Consent for publication

All the authors agree to publish.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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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

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Measurement of composite inflammatory ratios based on blood tests

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