Development of a predictive model for hemorrhagic transformation following mechanical thrombectomy in patients with acute ischemic stroke

The predictive model developed in this study demonstrated high accuracy in identifying patients with AIS at increased risk of HT after MT. This model may support early identification of high-risk individuals and inform the implementation of targeted preventive strategies.

Authors

Institutions

Publication Details

Journal
BMC Neurology
Published
2026-02-16
DOI
https://doi.org/10.1186/s12883-026-04707-w
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a predictive model for hemorrhagic transformation following mechanical thrombectomy in patients with acute ischemic stroke

Zhang He, Bing-xia Li, Yong-ai Li, Peng Li et al.
BMC Neurology
Acute Ischemic Stroke Management
article

Development of a predictive model for hemorrhagic transformation following mechanical thrombectomy in patients with acute ischemic stroke

Zhang He, Bing-xia Li, Yong-ai Li, Peng Li, Li Ma, Yao Liu
article en

Abstract

The predictive model developed in this study demonstrated high accuracy in identifying patients with AIS at increased risk of HT after MT. This model may support early identification of high-risk individuals and inform the implementation of targeted preventive strategies.

BMC NeurologyVol. 26(1)
Changzhi University (CN), Changzhi Medical College (CN)
Good health and well-being
Openalex Percentile: Top 54%
Acute Ischemic Stroke Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.