Evidence map›Paper›PMID 32416675›Full record

ArticleCurrent neurovascular research2020

Platelet-to-neutrophil Ratio after Intravenous Thrombolysis Predicts Unfavorable Outcomes in Acute Ischemic Stroke.

Mei-Qi Wang, Ying-Ying Sun, Yan Wang, Xiu-Li Yan, Hang Jin, Xin Sun, Peng Zhang, Hong-Jing Zhu, Zhen-Ni Guo, Yi Yang

Open access · hybridAbstract read
In one paragraph

Article in Current neurovascular research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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

18 citing papers in PubMed, 22 citations in OpenAlex.

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  18. Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
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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

10 authors at 1 institution in 1 country.

Mei-Qi WangDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Ying-Ying SunDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Yan WangDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Xiu-Li YanDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Hang JinDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Xin SunDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Peng ZhangDepartment of Neurology, Clinical Trail and Research Center for Stroke, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Hong-Jing ZhuDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Zhen-Ni GuoDepartment of Neurology, Clinical Trail and Research Center for Stroke, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Yi YangDepartment of Neurology, Stroke Center, Neuroscience Center, The First Hospital of Jilin University, Chang Chun, Jilin, China.
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposePlatelet-to-neutrophil ratio (PNR) was suggested to be an independent protective predictor for 90-days outcomes in acute ischemic stroke (AIS) patients in previous studies. This study aims to investigate the association between PNR and outcomes of AIS in intravenous thrombolysis (IVT) group.

methodsData on acute ischemic stroke patients who received intravenous thrombolysis from April 2015 to March 2019 were collected. We defined the PNR value at admission as pre-IVT PNR and after IVT within 24 h was defined as post-IVT PNR. Clinical outcome indicators included early neurological deterioration (END), hemorrhagic transformation (HT), delayed neurological deterioration (DND), and poor 3-month outcome (3m-mRS >2).

resultsA total of 581 patients were enrolled in the final analysis. The age was 61(53-69) years, and 423(72.8%) were males. Post-IVT PNR was independently associated with hemorrhagic transformation (OR = 0.974; 95%CI = 0.956-0.992; P=0.006), early neurological deterioration (OR = 0.939; 95%CI = 0.913-0.966; P = 0.01), delayed neurological deterioration (OR = 0.949; 95%CI = 0.912- 0.988; P = 0.011), and poor outcome (OR = 0.962; 95%CI = 0.948-0.976; P<0.001). PNR level was identified as high (at the cut-off value or above) or low (below the cut-off value) according to receiver operating curve (ROC) analyses on each endpoint. Comparison of early neurological deterioration, hemorrhagic transformation, delayed neurological deterioration, and poor 3-month outcome (3m-mRS >2) between patients at high and low levels for platelet-to-neutrophil ratio (PNR) showed statistical differences (p<0.001).

conclusionPost-IVT PNR was independently associated with early neurological deterioration, hemorrhagic transformation, delayed neurological deterioration, and poor 3-month outcome. Lower PNR can predict a worse outcome.

Indexed as

Administration, IntravenousAgedBlood PlateletsBrain IschemiaFemaleHumansIschemic StrokeMaleMiddle AgedNeutrophilsPredictive Value of TestsRetrospective StudiesThrombolytic TherapyTreatment OutcomeAcute ischemic strokedelayed neurological deteriorationearly neurological deteriorationhemorrhagic transformationintravenous thrombolysis

Identifiers

PMID32416675
PMCPMC8493787
OpenAlexW3024522362

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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 OpenQuestion graph.