Predicting ineffective thrombolysis in acute ischemic stroke with clinical and biochemical markers

Abstract

Abstract **Ischemic stroke remains a leading cause of morbidity and mortality globally. Despite the advances in thrombolytic therapy, notably recombinant tissue plasminogen activator (rtPA), patient outcomes are highly variable. This study aims to introduce a novel predictive model, the Acute Stroke Thrombolysis Non-Responder Prediction Model (ASTN-RPM), to identify patients unlikely to benefit from rtPA within the critical early recovery window. We conducted a retrospective cohort study at Baoding No.1 Central Hospital including 709 adult patients diagnosed with acute ischemic stroke and treated with intravenous alteplase within the therapeutic time window. The ASTN-RPM was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression technique, incorporating a wide range of biomarkers and clinical parameters. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Decision Curve Analysis (DCA). ASTN-RPM effectively identified patients at high risk of poor response to thrombolysis, with an AUC of 0.909 in the training set and 0.872 in the validation set, indicating high sensitivity and specificity. Key predictors included posterior circulation stroke, high admission NIHSS scores, extended door to needle time, and certain laboratory parameters like homocysteine levels. The ASTN-RPM stands as a potential tool for refining clinical decision-making in ischemic stroke management. By anticipating thrombolytic non-response, clinicians can personalize treatment strategies, possibly improving patient outcomes and reducing the burden of ineffective interventions. Future studies are needed for external validation and to explore the incorporation of emerging biomarkers and imaging data.

Keywords

• Ischemic stroke
• Thrombolysis
• recombinant tissue plasminogen activator (rtPA)
• Predictive model
• LASSO regression
• Personalized medicine