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

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

• 

  Mujie Yao 1,2

• J

  Jun Li 1,2

• S

  Sen Xu 1,2

• 

  Yue Wan 2*

• 1. School of Medicine, Wuhan University of Science and Technology, Wuhan, China

• 2. Department of Neurology, Hubei No. 3 People’s Hospital of Jianghan University, Wuhan, China

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 (P = 0.057). Bootstrap-corrected AUC for Base+GLR was 0.861. GLR also showed the most substantial calibration improvement (Hosmer-Lemeshow P = 0.858 vs. 0.081 for baseline; Brier score reduction 5.4%) and achieved significant integrated discrimination improvement (IDI = 0.0376, 95% CI: 0.002–0.078) and continuous net reclassification improvement (NRI = 0.712). Decision curve analysis revealed limited net benefit difference between GLR and the baseline model, suggesting GLR requires integration with multidimensional factors for comprehensive assessment.

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.