Evidence map›Paper›PMID 35463120›Full record

ArticleFrontiers in neurology2022

Clinical Prediction Model for Screening Acute Ischemic Stroke Patients With More Than 10 Cerebral Microbleeds.

Yifan Li, Haifeng Gao, Dongsen Zhang, Xuan Gao, Lin Lu, Chunqin Liu, Qian Li, Chunzhi Miao, Hongying Ma, Yongqiu Li

Abstract read
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

3 citing papers in PubMed.

  1. Observational
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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.

Yifan LiDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Haifeng GaoDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Dongsen ZhangDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Xuan GaoDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Lin LuDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Chunqin LiuDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Qian LiDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Chunzhi MiaoDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Hongying MaDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.
Yongqiu LiDepartment of Neurology, Tangshan Gongren Hospital, Tangshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hemorrhagic transformation is one of the most serious complications in intravenous thrombolysis. Studies show that the existence of more than 10 cerebral microbleeds is strongly associated with hemorrhagic transformation. The current study attempts to develop and validate a clinical prediction model of more than 10 cerebral microbleeds. Methods: We reviewed the computed tomography markers of cerebral small vessel diseases and the basic clinical information of acute ischemic stroke patients who were investigated using susceptibility weighted imaging from 2018 to 2021. A clinical prediction model of more than 10 cerebral microbleeds was established. Discrimination, calibration, and the net benefit of the model were assessed. Finally, a validation was conducted to evaluate the accuracy and stability of the model. Results: The multivariate logistic regression model showed hypertension, and some computed tomography markers (leukoaraiosis, lacunar infarctions, brain atrophy) were independent risk factors of more than 10 cerebral microbleeds. These risk factors were used for establishing the clinical prediction model. The area under the receiver operating characteristic curve (AUC) was 0.894 (95% CI: 0.870-0.919); Hosmer-Lemeshow chi-squared test yielded χ Conclusions: We developed and validated a simple scoring tool for acute ischemic stroke patients with more than 10 cerebral microbleeds; this tool may be beneficial for paradigm decision regarding intravenous recombinant tissue plasminogen activator therapy of acute ischemic stroke.

Indexed as

cerebral microbleedscerebral small vessel diseasehemorrhagic transformationintravenous thrombolysisprediction model

Identifiers

PMID35463120
PMCPMC9021829

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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.