Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patients.

The use of thrombolysis in acute ischemic stroke is restricted to a small proportion of patients because of the rigid 4·5-h window. With advanced imaging-based patient selection strategy, rescuing penumbra is critical to improving clinical outcomes. In this study, we included 155 acute ischemic stroke patients (84 patients in training dataset, age from 43 to 80, 59 males; 71 patients in validation dataset, age from 36 to 80, 45 males) who underwent MR scan within the first 9-h after onset, from 7 independent centers. Based on the mismatch concept, penumbra and core area were identified and quantitatively analyzed. Moreover, predictive models were developed and validated to provide an approach for identifying patients who may benefit from thrombolytic therapy. Predictive models were constructed, and corresponding areas under the curve (AUC) were calculated to explore their performances in predicting clinical outcomes. Additionally, the models were validated using an independent dataset both on Day-7 and Day-90. Significant correlations were detected between the mismatch ratio and clinical assessments in both the training and validation datasets. Treatment option, baseline systolic blood pressure, National Institutes of Health Stroke Scale score, mismatch ratio, and three regional radiological parameters were selected as biomarkers in the combined model to predict clinical outcomes of acute ischemic stroke patients. With the external validation, this predictive model reached AUCs of 0·863 as short-term validation and 0·778 as long-term validation. This model has the potential to provide quantitative biomarkers that aid patient selection for thrombolysis either within or beyond the current time window.