Evidence map›Paper›PMID 42037706›Full record

ArticleFrontiers in neurology

Related factors and a predictive model for early neurological deterioration after mechanical thrombectomy for acute ischemic stroke due to large vessel occlusion.

Yang Li, Yuanyuan Xue, Shuai Liu, Sifei Wang, Leilei Luo, Shuling Liu, Ming Wei

Abstract read
In one paragraph

Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yang Li *Department of Neurosurgery, Tianjin Medical University, Tianjin, China.
Yuanyuan Xue *Department of Neurosurgery, Tianjin Medical University, Tianjin, China.
Shuai LiuDepartment of Neurosurgery, Tianjin Medical University, Tianjin, China.
Sifei WangDepartment of Neurosurgery, Tianjin Medical University, Tianjin, China.
Leilei LuoDepartment of Neurosurgery, Tianjin Medical University, Tianjin, China.
Shuling LiuDepartment of Neurosurgery, Tianjin Medical University, Tianjin, China.
Ming WeiDepartment of Neurosurgery, Tianjin Medical University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mechanical thrombectomy (MT) is an established reperfusion therapy for acute ischemic stroke due to large vessel occlusion (LVO-AIS) and has been proven to significantly improve 90-day functional outcomes. However, some patients still experience early neurological deterioration (END) despite successful recanalization. This study aimed to systematically identify independent risk factors for END after MT via retrospective cohort analysis and construct a nomogram by integrating laboratory and clinical characteristics. Methods: A total of 486 LVO-AIS patients with successful recanalization (eTICI≥2b) were first categorized as END (ΔNIHSS≥4) or non-END (ΔNIHSS<4) according to the change in the NIHSS score from baseline to 24 h post-procedure. The entire patient group was then randomly divided to obtain training (70%, Results: We conducted a LASSO regression analysis on the clinical data of the patients in the modelling cohort and identified 6 disease characteristic variables. These variables were subsequently entered into a multivariate logistic regression model, which ultimately retained 5 independent predictors: smoking status (OR = 3.90, 95% CI 1.760-9.681, Conclusion: The constructed prediction model accurately estimated the risk of END in LVO-AIS patients who underwent MT with successful recanalization and may help optimize patient selection for endovascular therapy and provide reliable prognostic information.

Indexed as

early neurological deteriorationendovscularlarge vessel occlusionmechanical thrombectomystroke

Identifiers

PMID42037706
PMCPMC13102835

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