Precision Risk Intelligence
for Aortic Disease

"When I asked three different cardiac surgeons what my risk was if I didn't have surgery, I got three different answers. That's when I knew something was broken."

Post-surgery, ICU

Dan Brodsky knows aortic disease firsthand — as a patient who underwent open heart surgery to replace his Ascending Aorta. Patients facing one of the most consequential decisions of their lives were receiving inconsistent risk estimates with no clear, individualized picture of what their risk looked like over time. For most patients, that ambiguity became a reason to wait. And in aortic disease, waiting can be fatal.

Coming from finance, with a background building analytical platforms including having a former startup in precision oncology data sharing, Brodsky recognized this as a data problem with a solvable answer. He went deep into the same studies that leading cardiac surgeons rely on — the foundational dissection rate work of Elefteriades & Davies, the IRAD registry spanning over 7,300 cases, Coady, Hagan, Pape, the ACC/AHA 2022 Guidelines, and genetic subgroup studies on Marfan, Loeys-Dietz, and bicuspid aortic valve cohorts. Taken together, these studies told a coherent story that existing clinical tools were failing to communicate: risk doesn't stand still — it compounds, year over year, as the aorta grows.

The Aortic Sciences Adverse Event Risk Tool is the platform Dan Brodsky wished had existed when he was the patient sitting across from the surgeon. It applies a transparent, evidence-based risk multiplier methodology to produce a personalized year-by-year projection that a physician can walk through with a patient in the room — turning abstract percentages into a conversation that drives commitment to timely intervention. Thousands of dissections occur each year with fatalities that could have been prevented. Aortic Sciences exists to change that.