Choose your Region

Are you sure you want to proceed?

You will be leaving the Cook Medical website that you were viewing and going to a Cook Medical website for another region or country. Not all products are approved in all regulatory jurisdictions. The product information on these websites is intended only for licensed physicians and healthcare professionals.

Thank you for joining us at the
CIRSE 2020 Summit

Help us to improve your future online congress experiences by taking our 2-3 minute feedback survey about the event and Cook Medical booth areas. Click here to take the survey. Thank you in advance for your time.

Thank you for joining us at the CIRSE 2020 Summit. Cook has a long history with CIRSE, and we were excited to be a part of this unprecedented virtual summit.

I hope that through the videos from our Virtual Studio, opportunities to connect with our representatives at the virtual booth, and our engaging and informative symposia and sessions you were able to make the most out of your experience this year.

Although this year looked different, I know participants were able to receive the quality education they would have received if we were able to come together in-person.

We encourage you to take a look at the Zilver® PTX® predictability model from the FIRST@CIRSE presentation, browse the on-demand content available on the CIRSE website, and visit Cook’s industry spotlight page for additional product and services information.

Thank you for being a part of this virtual experience with us.

Andreas Förster
Global Business Director – Vascular Division
Cook Medical

Zilver PTX Predictability Model

View our data using our

Zilver® PTX® Predictability Model

During the 2020 First@CIRSE session, Dr. Michael Dake presented a new prediction model for patients treated with a Zilver PTX drug-eluting stent. The model, which includes data generated from more than 2,200 patients in five global studies, provides physicians with an opportunity to input 15 different patient and lesion characteristics to predict their probability of freedom from TLR. Read more.

View Prediction Model