Evidence map›Paper›PMID 42807046›Full record

ArticleFrontiers in neuroscience2026

Predictive value of blood composite biomarkers for hemorrhagic transformation following mechanical thrombectomy in acute anterior circulation large vessel occlusion stroke patients.

Mujie Yao, Jun Li, Sen Xu, Yue Wan

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2026. 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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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mujie YaoSchool of Medicine, Wuhan University of Science and Technology, Wuhan, China.
Jun LiSchool of Medicine, Wuhan University of Science and Technology, Wuhan, China.
Sen XuSchool of Medicine, Wuhan University of Science and Technology, Wuhan, China.
Yue WanDepartment of Neurology, Hubei No. 3 People's Hospital of Jianghan University, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hemorrhagic transformation (HT) is a devastating complication of mechanical thrombectomy (MT) for acute anterior circulation large vessel occlusion (LVO), yet reliable early prediction tools remain limited. This study systematically compared nine blood composite biomarkers to identify the optimal metabolic-immune integrative predictor of HT. Methods: A total of 206 patients with acute anterior circulation LVO who underwent MT were retrospectively enrolled. Nine composite biomarkers were calculated from routine admission laboratory tests: platelet-to-lymphocyte ratio (PLR), neutrophil-to-platelet ratio (NPR), systemic inflammation response index (SIRI), pan-immune inflammation value (PIV), monocyte-to-HDL ratio (MHR), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), glucose-to-potassium ratio (GPR), and glucose-to-lymphocyte ratio (GLR). A stratified screening strategy was employed: elastic net regression for baseline variable selection, Spearman correlation for biomarker clustering, and univariate AUC with DeLong test for representative selection. Model evaluation incorporated discrimination (AUC with DeLong test and Bootstrap 1,000-iteration optimism correction), calibration (Hosmer-Lemeshow test and Brier score), and clinical net benefit (NRI, IDI, and decision curve analysis). Results: HT occurred in 53 patients (25.7%). Elastic net regression identified six baseline variables: mean platelet volume, blood glucose, D-dimer, history of alcohol consumption, leukoaraiosis, and pulmonary infection. Spearman clustering yielded five representative biomarkers: GLR, SIRI, GPR, NPR, and MHR. Among these, GLR was the only biomarker that showed a trend toward improving? the baseline model's discrimination (AUC: 0.852-0.882; △AUC = 0.030), although this difference did not reach statistical significance ( Conclusion: Among nine composite biomarkers, GLR provides the greatest incremental predictive value for HT following MT in acute anterior circulation LVO patients. As a routinely accessible metabolic-immune integrative index, GLR may serve as an early warning layer for perioperative risk stratification, though integration with imaging and procedural information remains essential for clinical decision-making.

Indexed as

acute ischemic strokecomposite biomarkersglucose-to-lymphocyte ratiohemorrhagic transformationmechanical thrombectomypredictive model

Identifiers

PMID42807046
PMCPMC13616940

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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.