These models may have clinical value in assisting decision-making, the authors wrote. Further research is must be conducted on their predictive value and diagnostic accuracy while taking into account invasive adjuvant strategies.

In addition, blood pressure (BP), heart rate, glucose level, consciousness level, National Institutes of Health Stroke Scale (NIHSS) score and a history of diabetes mellitus (DM) were all determined to be associated with MNI. Age, glucose level, BP, hemoglobin A1c, history of DM, stroke subtype and NIHSS score were all significant factors affecting long-term stroke outcomes.

BP was found to be especially crucial in both prediction models, with both low and high BP appearing to impact patients short-term and long-term outcomes after thrombolysis.

Patients with lower BP during the acute phase of AIS are associated with brain injury and poor outcome they wrote. A higher initial BP may imply a later reduction in BP in response to thrombolysis and a better neurological improvement. The dynamic changes in BP during AIS are associated with impaired cerebral autoregulation, reperfusion injury, edema, and hemorrhagic transformation.

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AI predicts outcomes for stroke patients following thrombolysis - AI in Healthcare