Evidence map›Paper›PMID 41823048›Full record

ArticleCNS neuroscience & therapeutics2026

Pan-Immune-Inflammation Value for Mortality Risk Stratification in Critically Ill Patients With Acute Ischemic Stroke: A Cohort Study Based on the MIMIC-IV Database.

Zhenzhu Li

Abstract read
In one paragraph

Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

1 author.

Zhenzhu LiDepartment of Emergency, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, China.ORCID 0009-0007-0511-9755

Funding

Tianjin Municipal Health Commission for Public Health Affairs 2023058Tianjin Union Medical Center 2019YJ012
6 · The paper itself

Abstract

aimsThe pan-immune-inflammation value (PIV) is an emerging inflammatory biomarker whose prognostic role in critical acute ischemic stroke (AIS) remains unclear. This study aimed to explore the association between PIV and all-cause mortality (ACM).

methodsThis retrospective cohort study enrolled 710 ICU patients with AIS from the MIMIC-IV database. The association between PIV and 90-day (primary), 30-day, and 1-year ACM was evaluated using multivariable Cox regression, restricted cubic splines, subgroup analyses, and sensitivity analyses.

resultsEach 1000-unit increase in PIV was independently associated with an 8% higher risk of 90-day ACM (hazard ratio [HR] = 1.08; 95% confidence interval [CI] 1.03-1.13; p < 0.001). A reverse L-shaped nonlinear correlation was observed (threshold: 2987.61). The mortality rate was markedly higher in patients above versus below this threshold (52.7% vs. 28.3%). Subgroup analyses further revealed that this association was significantly stronger in patients without sepsis (p for interaction = 0.007). Both sensitivity analyses-complete-case and multiple imputation-yielded consistent results, confirming the robustness of the main findings. The results remained consistent for 30-day and 1-year ACM.

conclusionsPIV exhibited a reverse L-shaped relationship with short- and long-term ACM in critical AIS, and served as a tool for ACM risk stratification.

Indexed as

Brain IschemiaInflammationIschemic StrokeAgedAged, 80 and overBiomarkersCohort StudiesCritical IllnessDatabases, FactualFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentBiomarkersacute ischemic strokeall‐cause mortalityinflammatory markersMIMIC‐IVpan‐immune‐inflammation valuerisk stratification

Identifiers

PMID41823048
PMCPMC13093308

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.