Evidence map›Paper›PMID 40035033›Full record

ArticleFrontiers in neurology2025

A predictive model for early neurological deterioration after intravenous thrombolysis in patients with ischemic stroke.

Liping He, Meng Zhang, Fei Xu, Zhangsong Wu, Huijuan Chen, Ying Li, Ran Chen

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. [A prediction model for early neurological improvement after intravenous thrombolysis in patients with acute ischemic stroke].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
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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

7 authors.

Liping HeDepartment of School, Bengbu Medical University Graduate School, Bengbu, China.
Meng ZhangDepartment of School, Bengbu Medical University Graduate School, Bengbu, China.
Fei XuDepartment of Neurology, Anhui Medical University Lu'an People's Hospital, Lu'an, China.
Zhangsong WuDepartment of Neurology, Anhui Medical University Lu'an People's Hospital, Lu'an, China.
Huijuan ChenDepartment of Neurology, Anhui Medical University Lu'an People's Hospital, Lu'an, China.
Ying LiDepartment of Neurology, Anhui Medical University Lu'an People's Hospital, Lu'an, China.
Ran ChenDepartment of School, Bengbu Medical University Graduate School, Bengbu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Intravenous thrombolysis (IVT) is the treatment of choice for acute ischemic stroke (AIS), but some patients develop early neurological deterioration (END) within 24 h after IVT. Therefore, we aimed to identify predictors of END in AIS patients following treatment with IVT. Methods: We retrospectively analyzed the clinical data of 621 AIS patients who received IVT with recombinant tissue-type plasminogen activator (rt-PA) at the Stroke Centre of the People's Hospital of Lu'an City, China, from July 2018 to July 2023. Clinical data, including demographic characteristics, clinical assessment results, underlying diseases, and laboratory indices, were collected at the time of admission. The patients were divided into training and validation cohorts, after which LASSO regression was applied to select the most important predictor variables, and multivariate logistic regression was used to construct a nomogram. The discriminative power of the model was determined by calculating the area under the curve (AUC), and calibration and decision curve analyses (DCA) were performed. Results: The platelet-to-lymphocyte ratio (PLR) (OR 1.01, 95% CI 1.01-1.01, Conclusion: This predictive model can be used for the early identification of the risk of developing END after IVT and development of active interventions to improve the prognosis of AIS.

Indexed as

acute ischemic strokeearly neurological deteriorationintravenous thrombolysismachine learningnomogrampredictive models

Identifiers

PMID40035033
PMCPMC11873837

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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.