| Summary: | <p>Abstract</p> <p>Background</p> <p>Despite the diffusion into practice of percutaneous closure of a patent foramen ovale (PFO) in patients with cryptogenic stroke (CS), the benefits have not been demonstrated, and remain unclear. For any individual presenting with a PFO in the setting of CS, it is not clear whether the PFO is pathogenically-related to the index event or an incidental finding. Further, the overall rate of stroke recurrence is low in patients with CS and PFO. How patient-specific factors affect the likelihood that a discovered PFO is related to an index stroke or affect the risk of recurrence is not well understood. These probabilities are likely to be important determinants of the benefits of PFO closure in CS.</p> <p>Design/Methods</p> <p>The goal of the Risk of Paradoxical Embolism (RoPE) Study is to develop and test a set of predictive models that can identify those patients most likely to benefit from preventive treatments for PFO-related stroke recurrence, such as PFO closure. To do this, we will construct a database of patients with CS, both with and without PFO, by combining existing cohort studies. We will use this pooled database to identify patient characteristics associated with the presence (versus the absence) of a PFO, and to use this "PFO propensity" to estimate the patient-specific probability that a PFO was pathogenically related to the index stroke (Model #1). We will also develop, among patients with both a CS and a PFO, a predictive model to estimate patient-specific stroke recurrence risk based on clinical, radiographic and echocardiographic characteristics. (Model #2). We will then combine Models #1 and #2 into a composite index that can rank patients with CS and PFO by their conditional probability that their PFO was pathogenically related to the index stroke <it>and </it>the risk of stroke recurrence. Finally, we will apply this composite index to completed clinical trials (currently on-going) testing endovascular PFO closure against medical therapy, to stratify patients from low-expected-benefit to high-expected-benefit.</p>
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