Evidence map›Paper›PMID 40776826›Full record

ArticleBiomarkers in medicine2025

Systemic immune-inflammation index as a biomarker for stroke prognosis: insights from a multi-time point analysis.

Yanan Wang, Jiaojiao Wang, Fengmei Tian, Mengyun Peng, Xiaomin Ma, Dahong Zheng, Xiaoxiao Li, Jingya Jiao, Liping Zheng, Zhengbao Zhu and 2 more

Abstract read
In one paragraph

Article in Biomarkers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yanan WangSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Jiaojiao WangSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Fengmei TianSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Mengyun PengSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Xiaomin MaDepartment of Nursing, The First People's Hospital of Kunshan, Suzhou, China.
Dahong ZhengSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Xiaoxiao LiSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Jingya JiaoSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Liping ZhengSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.
Zhengbao ZhuDepartment of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
Shu JiDepartment of Nursing, The First People's Hospital of Kunshan, Suzhou, China.
Daoxia GuoSchool of Nursing, Suzhou Medical College of Soochow University, Soochow University, Suzhou, China.ORCID 0000-0002-4537-0705

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThis study aimed to investigate the association between the systemic immune-inflammation index (SII) and prognosis trajectories in ischemic stroke(IS).

methodsPatients from two tertiary hospitals in Suzhou were included in this study. SII was calculated as neutrophils×platelets/lymphocytes, and patients were categorized into quartiles based on SII values. Latent class growth modeling (LCGM) was employed to describe the trajectories of modified Rankin Scale (mRS) at different time points.Logistic regression models were used to evaluate the association between SII quartiles and prognosis trajectories at multiple time points (14 days, 1 month, 3 months, 6 months)and prognostic trajectories.

resultsPatients in the highest quartile (Q4) of SII had a significantly higher risk of adverse outcomes compared to those in the lowest quartile (Q1). A three-group model was identified as the optimal trajectory model for stroke prognosis. SII was associated with 4.06-fold increased odds (95% CI: 1.64-10.08) of unfavorable prognosis trajectories. Per standard deviation increase in the logarithmic SII, the odds of unfavorable prognosis trajectories were 1.64 (95% CI: 1.18-2.29).

conclusionsBaseline SII is significantly associated with unfavorable outcome trajectories across multiple time points in IS. These findings highlight the potential value of SII as a predictive biomarker for sequential prognosis in stroke patients.

Indexed as

BiomarkersInflammationIschemic StrokeStrokeAgedFemaleHumansMaleMiddle AgedNeutrophilsPrognosisBiomarkersimmunity inflammationprognosisprognostic trajectorystrokesystemic immune-inflammation index

Identifiers

PMID40776826
PMCPMC12344814

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.