Targeted treatment for acute stroke: development of prognostic models and decision support tools

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Stroke, the world‘s second biggest killer, is most often due to a blockage of a brain blood vessel by blood clot. Drugs that dissolve clots or prevent them from forming improve the chance of recovery after stroke, but they also increase the chance of bleeding; when bleeds occur into the brain they are particularly severe. Most stroke patients need to take drugs that affect blood clotting at some point in their illness, ranging from potent ‘clot busting‘ drugs to less risky drugs like aspirin.If a doctor could reliably select which patients were helped rather than harmed by these drugs, this would be a big step forward. I hope to develop a method to predict which stroke patients are more likely to be harmed (by bleeding) than benefit (by avoiding forming blood clots) from medicines that affect clotting. I will use statistical methods to make predictions, and then explore the best way for doctors and patients to use them. This will lead to better decisions for each individual patient, and better treatment policies. This study will use the best information from existing methods as well as from newer technologies (e.g. blood tests and brain imaging) to ensure effective and personalised decisions for each stroke patient