Pan-immune inflammation value and neutrophil-to-albumin ratio predict hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke: a dual-center cohort study

Background and purpose Hemorrhagic transformation (HT) remains a critical complication of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS), yet reliable pre-thrombolysis predictors are limited. We investigated whether pan-immune inflammation value (PIV) and neutrophil-to-albumin ratio (NAR), individually and in combination, are associated with HT and its subtypes. Methods In this dual-center retrospective cohort study, 4,313 AIS patients treated with IVT were included. PIV and NAR were calculated from routine baseline blood tests. The primary outcome was any HT, with secondary outcomes including ECASS-classified subtypes: hemorrhagic infarction (HI1, HI2) and parenchymal hematoma (PH1, PH2). Associations were evaluated using multivariable logistic regression, restricted cubic spline (RCS) modeling, subgroup analyses, and receiver operating characteristic (ROC) curves. Results Hemorrhagic transformation patients exhibited significantly higher PIV and NAR than non-HT patients, with the highest levels in PH1 and PH2. Multivariable analyses identified both PIV and NAR as independent predictors of overall HT, PH1, and PH2. Joint high PIV and NAR conferred a markedly increased risk (OR = 6.63, 95% CI: 4.78–9.17). Subgroup analyses confirmed the consistency of associations across age, sex, baseline NIHSS, and comorbidities. RCS modeling demonstrated a logarithmic non-linear relationship between these inflammatory markers and HT risk (non-linearity p 0.05). ROC analysis showed that combining PIV and NAR substantially improved predictive accuracy (AUC = 0.783, 95% CI: 0.761–0.804, p 0.001) compared to individual markers. Conclusion Elevated PIV and NAR are independently associated with increased risk of HT, particularly PH1 and PH2, after IVT in AIS patients. Integrating these biomarkers enhances predictive performance, supporting their potential utility in multidimensional risk stratification before thrombolysis.