Introduction
Stroke stands as a critical global health issue, significantly leading to morbidity, disability, and mortality, with China experiencing a disproportionately high incidence and severity of stroke events.1,2 Among the different types of stroke, approximately 70% are classified as acute ischemic stroke (AIS), which predominantly arises from mechanisms such as ischemia, hypoxia, and neuroinflammatory processes affecting cerebral blood vessels. Although the cornerstone treatment for AIS is intravenous thrombolysis, administered within a critical window of 4.5 hours post-onset,3,4 this procedure is not without significant risks, including vascular perfusion injury and cerebral hemorrhage. Successful outcomes hinge on the timely identification of patients with poor thrombolytic prospects, alongside the necessary escalation of treatment strategies employed by healthcare professionals, which can markedly improve patient prognosis following thrombolysis, lower the chances of recurrence and mortality, and ensure optimal resource allocation within healthcare systems.
Recent research underscores the intricate role of neuroinflammation in affecting the pathology, physiology, and long-term prognosis of stroke patients. In cases of acute ischemic stroke, inflammatory mediators released from cerebral cells trigger a cascade of inflammatory responses, ultimately resulting in neuronal injury and manifesting as neurological deficits and adverse clinical outcomes for patients. Established inflammatory markers like the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), which can be easily calculated from complete blood counts, have emerged as trustworthy indicators for predicting functional outcomes in AIS cases.5,6 Recently, new predictive markers have been introduced, such as the systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV).7,8 However, there exists a knowledge gap concerning studies that simultaneously leverage multiple inflammatory indices, with even fewer studies utilizing these composite inflammatory ratio markers collectively to anticipate patient outcomes after intravenous thrombolysis at the three-month follow-up.
This study was designed to meticulously explore the correlation between composite inflammatory markers including PLR, NLR, LMR, SII, SIRI, and PIV and patient outcomes among those experiencing acute ischemic stroke subsequently treated with intravenous thrombolysis therapy (IVT), specifically at the three-month mark. The primary objective was to refine the predictive accuracy of existing prognostic models and aid in the formulation of more precise treatment strategies for the wider patient population.
Material and Methods
Study Population
The retrospective observational study conducted from January 2019 to December 2022 was centered around patients diagnosed with acute ischemic stroke who received intravenous thrombolysis using recombinant tissue plasminogen activator (rt-PA) at a national advanced stroke center. Because retrospective studies can be biased due to inadequate documentation, we utilized chained equation multivariate estimation, as well as interpolation techniques, to address missing data. Sample size determination9 followed the statistical formula: n = (Z * σ / E)² (where n denotes the required sample size; Z is the Z statistic at a 95% confidence level, valued at 1.96; σ indicates the overall standard deviation, typically set at 0.5; and E is the margin of error, designated as 0.05). Computation yielded n=385, affirming that a sample size greater than 385 was adequate. An ethical review was granted by the Research Ethics Committee of the hospital (approval number: 2022–063), with informed consent obtained from each patient or their guardian prior to study participation.
Stroke patients were classified into anterior or posterior circulation groups based on the infarction location, while etiological diagnosis adhered to the classic 1993 TOAST typology outlined by Adams et al.10 This typology encompasses subtypes including atherosclerotic large artery disease, small artery occlusion, cardiogenic embolism, strokes of undetermined etiology, as well as strokes attributed to other known causes.
Patients diagnosed with acute ischemic stroke (AIS) predominantly underwent cranial CT scans upon admission to exclude the presence of cerebral hemorrhage. Subsequent to imaging, rt-PA was administered for intravenous thrombolysis at the customary dosage of 0.9 mg/kg (Boehringer Ingelheim, Germany). The treatment commenced within the critical time frame of 4.5 hours from symptom onset. Initial administration spanned 10% of the total dosage intravenously over 1 minute, followed by the remainder delivered via a continuous micropump over a period of 60 minutes.11 Notably, the upper limit for rt-PA dosage administered did not exceed 90 mg.
Data Acquisition
Clinical information was meticulously collected upon patient admission, documenting gender, age, weight, height, diastolic blood pressure (DBP), systolic blood pressure (SBP), onset-to-treatment time (OTT), door-to-needle time (DNT), and National Institutes of Health Stroke Scale (NIHSS) scores, which included NIHSS scores pre-thrombolysis (ANIHSS) and post-thrombolysis (PNIHSS) along with the Modified Rankin Scale (mRS) score.12
Comprehensive laboratory tests were performed upon admission, focusing on baseline blood glucose levels, hemoglobin count, white blood cell count (WBC), platelet count, neutrophil count, lymphocyte count, monocyte count, eosinophil count, international normalized ratio, prothrombin time, activated partial thromboplastin time, brain natriuretic peptide for heart failure measurements, lactate dehydrogenase, creatinine, and uric acid levels.
Definition of Inflammation Indicators
PLR is calculated as platelet counts divided by lymphocyte counts, while NLR is the ratio of neutrophil counts to lymphocyte counts. LMR is derived from lymphocyte counts divided by monocyte counts, SII is computed by multiplying platelet counts with the neutrophil-to-lymphocyte ratio, SIRI is the product of neutrophil counts and monocyte counts divided by lymphocyte counts, and PIV denotes the product of neutrophil counts, platelet counts, and monocyte counts divided by lymphocyte counts.
Prognostic Assessment
Statistical Analysis
Data analysis was performed using R software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria). Normal distribution of continuous variables was evaluated via the Kolmogorov–Smirnov test. Continuous data were expressed as median and interquartile range (IQR), while categorical data were displayed as frequency and percentage (%). The chi-square test facilitated the comparison of categorical data across groups, whereas the Mann–Whitney U-test was utilized for continuous data. Variables were deemed significant at a P level of
The distribution was illustrated using density histograms for the six indicators (PLR, NLR, LMR, SII, SIRI, and PIV) across the entire study group. To further evaluate the distribution of these indicators, violin plots were employed for subgroups characterized by good and poor prognoses, along with necessary modeling adjustments. Additionally, receiver operating characteristic (ROC) curves were generated using the ROCR package to assess the effectiveness of the six indicators in predicting outcomes.
Results
Baseline Patient Characteristics
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Table 1 Baseline Characteristics of AIS Patients with Different Prognoses |
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Figure 1 Study flowchart. |
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Figure 2 MRS distribution map in the study population. 0: Favorable prognosis; 1: Unfavorable prognosis. |
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Figure 3 Histogram of the distribution density of PLR (A), NLR(B), LMR(C), SII (D), SRI(E) And PIV(F) in the study population. |
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Figure 4 Violin plots of the distribution of PLR (A), NLR(B), LMR(C), SII (D), SIRI(E) and PIV(F) inflammation ratios. 0: Favorable prognosis group; 1: Unfavorable prognosis group. |
Distribution of Complex Inflammatory Indicators
Violin plots provided insights into the distribution of PLR, NLR, LMR, SII, SIRI, and PIV composite inflammatory indicators among both favorable and unfavorable prognosis groups, showcasing distinctions in PLR and NLR levels between the two cohorts; heightened levels were particularly prominent in the unfavorable prognosis group.
Logistic Regression Analysis Results
Table 2 presents the results of the multivariate logistic regression model delineating differences between strong prognosis and unfavorable prognosis groups post-thrombolysis (Table 2).
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Table 2 Multifactorial Analysis of PLR, NLR, LMR, SII, SIRI, and PIV in Relation to 3-Month Unfavorable Prognosis in Patients with AIS |
Model 1 encompasses univariate and multivariate analysis. Findings indicated that PLR (odds ratio (OR), 1.001; 95% CI 1.000–1.003, P=0.012), NLR (OR, 1.122; 95% CI 1.089–1.201, P=0.028), and LMR (OR, 1.023; 95% CI 1.021–1.025, P<0.01) significantly correlated with outcomes.
Model 2, adjusted for variables such as sex, age, glucose levels at admission, admission NIHSS scores, smoking, alcohol history, and pre-existing comorbidities, further corroborated the significance of PLR (OR, 1.001; 95% CI 1.000–1.002, P=0.013), NLR (OR, 1.123; 95% CI 1.088–1.204, P=0.029), and LMR (OR, 1.025; 95% CI 1.020–1.024, P<0.01).
ROC Values of Composite Inflammatory Markers for Adverse Outcomes
The calculated area under the receiver operating characteristic curve (AUC-ROC) values revealed insights for PLR, NLR, LMR, SII, SIRI, and PIV, reported as 0.613 (95% CI, 0.564–0.661), 0.707 (95% CI, 0.663–0.751), 0.614 (95% CI, 0.567–0.662), 0.715 (95% CI, 0.672–0.758), 0.631 (95% CI, 0.584–0.679), and 0.569 (95% CI, 0.520–0.619) respectively (Figure 5). Notably, SII and NLR exhibited superior predictive significance concerning unfavorable prognosis in patients at the three-month marker following intravenous thrombolysis, while PIV displayed comparatively lower predictive efficacy.
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Figure 5 Receiver operating characteristic curves (ROC) for PLR (Mod A), NLR (Mod B), LMR (Mod C), SII (Mod D), SIRI (Mod E), and PIV (Mod F) to predict unfavorable prognosis in AIS patients at 3 months. Area under the curve (AUC) values can be used to measure predictive accuracy. |
Discussion
The interplay of immunity and inflammation is paramount in the pathophysiology of stroke, with inflammatory signaling prominently observed during the acute phase of ischemic stroke (AIS) and throughout the subsequent ischemic cascade. Post-cerebral ischemia,15 the release of neurotoxic agents such as inflammatory cytokines and reactive oxygen species (ROS) by damaged cerebral cells leads to compromise of the blood-brain barrier while instigating a cascade of inflammatory response, culminating in neuronal injury and neurological impairment. Neutrophils,16,17 activated by inflammatory cytokines from ischemic sites, are among the first immune cells to infiltrate the affected brain regions, releasing mediators responsible for further inflammation and contributing to the damage sustained by ischemic tissues. Furthermore, monocytes play a pivotal role, recognized as significant sources of matrix metalloproteinases (MMP-9), which exacerbate brain injury, while lymphocytes might offer neuroprotective effects. Acute ischemic stroke is precipitated by excessive platelet activation,18 resulting in thrombosis and subsequent vascular occlusion, leading to adverse outcomes.
This study holds considerable significance in its findings. By utilizing a large patient cohort, we successfully incorporated several composite inflammatory ratios concurrently for the first time, including PLR, NLR, LMR, SII, SIRI, and PIV. These ratios derived from hematological data are vital in understanding inflammatory processes in patients with ischemic stroke and provide reliable prognostic insights for those undergoing intravenous thrombolysis. Their ability to be easily obtained from routine blood counts ensures that clinicians have a valuable and cost-effective prognostic tool at their disposal, thereby reducing the financial burden on patients.
Potential limitations of this study are acknowledged. Firstly, it is important to recognize the retrospective nature of our analysis, which was confined to a single advanced stroke center rather than comprising a multicenter clinical investigation.25 Future inquiries should include multicenter prospective studies to minimize informational biases and account for confounding variables. Additionally, dynamic monitoring of patients’ composite inflammatory ratios throughout their hospital stay is crucial, rather than relying solely on measurements taken during the immediate pre-thrombolytic admission phase. As traditional analytical methodologies were employed, integrating advanced artificial intelligence techniques26 and big data analytics27 could significantly enhance predictive modeling for neurological outcomes, fundamentally benefiting future medical research efforts.
Conclusions
In conclusion, this research corroborates the association among PLR, NLR, LMR, SII, SIRI, and PIV with the three-month prognostic outcomes of acute ischemic stroke patients following intravenous thrombolysis through comprehensive multivariate analysis. Significantly, NLR and SII demonstrated excellent predictive capabilities. These readily accessible predictors can empower clinicians to promptly identify and address risk factors, implement timely interventions, lessen adverse outcomes, embrace precision medicine strategies, and optimize healthcare resource allocation.
Data Sharing Statement
Ethics Statement
Acknowledgments
The authors thank the staff of the Department of Emergency, the Baoding NO.1 Central Hospital, China.
Author Contributions
Funding
Disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Decoding Stroke: A Serious Matter with a Punchline
Introduction
Stroke, ladies and gentlemen, isn’t just a medical term that sounds nice—it’s actually quite the dreaded guest at the global health party. Particularly in China, where it seems to have booked an extended stay. And why? Well, because 70% of strokes arrive uninvited as acute ischemic strokes (AIS). They strut in with ischemia, hypoxia, and neuroinflammation like they own the place.
Now, the primary remedy for this unwelcome guest is intravenous thrombolysis, but it isn’t without its own complications. Think of it as a very risky magic trick where one wrong move could lead to… shall we say, cerebral hemorrhage? No thank you! This article promises some bright-eyed healthcare providers might find it handy to identify those who are less likely to have a happy ending post-thrombolysis and thus, mitigate bad outcomes. It’s like a personal trainer for stroke recovery—only less sweaty.
The Science of Inflammation
Tallying things up, researchers have found that inflammation is a bit like that one energetic friend at a party—starts a chain reaction that can completely wreck the place. Cellular party crashers release inflammatory mediators leading to neuronal damage. Not exactly the kind of ‘damage’ one boasts about in their social media bio.
We already have established inflammation indicators, such as the neutrophil-to-lymphocyte ratio (NLR), plateauing to be quite the reliable indicators of functional outcomes in AIS. But, watch out! New contenders are grasping for attention, including SII (Systemic Immune Inflammation Index), SIRI (Systemic Inflammation Response Index), and the fancy-sounding PIV (Pan-Immune-Inflammation Value). They’re desperately hoping to grab a spot on your lab results—no audition necessary! Amusingly, most studies have been like that one friend who can’t seem to group text correctly; they haven’t quite managed to include multiple inflammation indices… yet.
The Study: Scope & Methods
Speaking of ambition, let’s dive into the guts of our study. Researchers conducted a retrospective observational analysis from a trendy stroke center over three years, focusing on AIS patients who had treatment with rt-PA—because who doesn’t want to throw some high-tech chemical magic into the mix? The parameters were built on solid ground, but beware; retrospective studies can occasionally lead us down the wrong rabbit hole of information bias. Better luck next time!
They managed a sample size of 385 (thank you, statistical formulas!), and every patient gave their stamp of approval through some fancy ethics paperwork. They even filtered the stroke types per the classic TOAST criteria. How civilized!
Data & Indicators
Data was back-loaded from patient info to all sorts of blood work. Think diastolic and systolic blood pressure, NIH Stroke Scale scores, and various cellular counts! If you thought Wal-Mart had a lot of choices, wait till you see the range of inflammatory indicators these researchers pulled out! PLR (Platelet-to-Lymphocyte Ratio), NLR, LMR, SII, SIRI, and PIV are battling for supremacy like a reality TV show.
Results: Analyzing the Data
Through the complex dance of statistical analysis, including R software, which absolutely deserves a round of applause for tackling this labyrinthine data, it was determined that NLR and SII held significant predictive capabilities regarding patient prognoses three months after thrombolysis. PLR decided to join the party as well, but the real stars were SII and NLR—they could practically hear the applause!
Conclusion: The Grand Finale
To wrap it all up neatly: these inflammation indices aren’t just for show; they’re working hard to ensure that healthcare providers can identify at-risk patients more efficiently. It’s like having a GPS in a world where everyone is still using paper maps. Who wouldn’t want that precision and insight? And with AI starting to take over, the research world is bound to become even more exciting—like going from a flip phone to an iPhone!
Finally, A Word of Caution
While we’re at it, let’s acknowledge that the study wasn’t without its limitations. Being a retrospective analysis and all, it’s got a few things to smooth out. And with changing ratios throughout hospitalization, keeping tabs on them might be a right circus act! Still, isn’t it refreshing to see researchers pushing the boundaries of traditional analysis? Advances in AI and machine learning could ensure stroke prognosis becomes more precise than a surgeon’s scalpel.
In Closing
So here we are: the intricate relationship between inflammatory markers and acute ischemic stroke outcomes opens the door to improved patient care. If you’ve ever doubted the importance of blood test ratios, consider tossing a piece of confetti in honor of the NLR and SII! They’ve got their work cut out, and we’re just going to have to watch this space unfold.
How do inflammation levels and additional indices like PIV and SIRI impact clinical outcomes in stroke recovery?
Tware and various regression models, the researchers uncovered some enlightening patterns. It turns out that both NLR and SII held significant value in predicting outcomes post-thrombolysis in AIS patients. NLR emerged as a dependable marker with a notable correlation to the 3-month outcomes after treatment, while SII added another layer of depth to understanding the immune response in stroke recovery.
However, the data dance floor wasn’t all smooth moves. The study highlighted different outcomes based on the type of stroke, time of treatment, and the interplay of various inflammatory markers. It’s like trying to choreograph a flash mob with too many different dancers—not everyone knew the steps, leading to potential complications in clinical outcomes.
The researchers also identified that patients exhibiting higher levels of inflammation, indicated by elevated NLR and SII, were more likely to face adverse outcomes. In addition to these ratios, they aimed to demystify the roles of additional indices, including the newly minted PIV and SIRI, which added complexity but did not always yield clear benefits over the established players.
Conclusion: The Takeaway
In the grand finale of this data-packed spectacle, it’s clear that inflammation plays a starring role in stroke recovery outcomes. Medical practitioners might want to keep an eye on the NLR and SII as they refresh their playlists of prognostic tools. However, just as in life, while some ratios reign supreme, the true picture of each patient is shaped by multiple contributing factors and should not be distilled into numbers alone.
The study shines a light on the pressing need for personalized approaches in managing AIS, with thorough understanding of each patient’s immune-inflammatory response. Remember, it’s less about picking a favorite ratio and more about orchestrating a harmonious recovery pathway, ensuring that stroke doesn’t continue as that uninvited guest at the global health party.
