An Observational Stroke Model for Decision Support

An Observational Stroke Model for Decision Support

Annually, it is estimated that more than 795,000 Americans experience a stroke. The severity of neurological damage due to an acute stroke is mitigated by the early restoration of blood flow to the affected area; and more people are now surviving strokes through earlier intervention with thrombolytic agents and interventional clot retrieval devices. Unfortunately, the rapid development of new drugs and devices in this area has made it difficult to provide treatment guidance for a given patient, and metrics for comparing outcomes between treatment groups are lacking. This project focuses on the creation of an observational database for acute stroke treatment. From this database, an influence diagram is established, using information on patient presentation, medical history, imaging, and available treatment drugs/devices to compute an optimal treatment decision maximizing outcomes and other considerations (e.g., quality of life). This project aims to provide new insights and informatics-based tools to better guide and individually tailor acute stroke treatment , optimizing patients' long-term health outcomes.

The specific aims of this project are:

  1. To develop an observational data model of acute stroke capturing disease variable, cost information, evolving interventional therapies, and clinical outcomes.
  2. To translate the observational model developed in aim 1 into a queryable influence diagram to differentiate and predict outcomes in acute stroke patients.