Modeling Prehospital Transportation for Patients With Suspected Acute Ischemic Stroke due to Large Vessel Occlusion:...

Area of Science:

• Neurology
• Health Services Research
• Biostatistics

Background:

• A conditional probability model was developed to optimize transport decisions for acute ischemic stroke patients.
• The model compares 'drip and ship' (thrombolysis then endovascular therapy) versus 'mothership' (direct to endovascular therapy) transport.
• Validation was performed against the RACECAT trial (NCT02795962) for suspected large vessel occlusion in acute stroke.

Purpose of the Study:

• To compare and validate a conditional probability model for stroke patient transport.
• To assess if the model accurately predicts outcomes based on transport method.
• To evaluate transport method effectiveness for acute ischemic and hemorrhagic stroke patients.

Main Methods:

• Applied a conditional probability model to predict optimal transport for stroke patients.
• Compared model predictions with actual transport methods and 90-day modified Rankin Scale (mRS) outcomes.
• Utilized ordinal logistic regression and performed subanalysis for hemorrhagic stroke cases.

Main Results:

• The conditional probability model showed high consistency with RACECAT trial results; outcomes were similar overall.
• No significant difference in 90-day mRS outcomes was found between matching and mismatched transport methods (OR 1.13).
• Hemorrhagic stroke patients randomized to mothership had worse outcomes than those randomized to drip and ship (OR 3.53).

Conclusions:

• The conditional probability model successfully predicted RACECAT trial outcomes.
• No significant difference in outcomes between drip and ship and mothership transport methods for stroke.
• Model provides a tool for optimizing stroke transport logistics and patient outcomes.