Cardiosense, a medical AI company transforming cardiovascular disease management, has launched SEISMIC-HF II, a nationwide study to validate its machine learning algorithm for assessing intracardiac filling pressure, an early indicator of acute heart failure decompensation. This milestone represents a critical step forward in the company's mission to enable access to smarter, personalized cardiac care for all heart failure patients.
Building on Breakthrough Device Success
The SEISMIC-HF II study follows the success of the 2024 SEISMIC-HF I study, which demonstrated the ability of Cardiosense's machine learning algorithm—an FDA Breakthrough Device—to non-invasively estimate pulmonary capillary wedge pressure (PCWP) in patients with heart failure with reduced ejection fraction (HFrEF). The findings were presented as late-breaking science at the American Heart Association's (AHA) 2024 Scientific Sessions, where the algorithm achieved "accuracy similar to existing FDA-approved implantable pressure sensors," according to AHA scientific reviewers.
Addressing Critical Healthcare Burden
Heart failure affects nearly 6.7 million adults in the U.S., resulting in over $30 billion in annual healthcare costs and nearly 50% of patients being readmitted within six months. The current standard of care, focused on weight and symptom monitoring, has proven largely ineffective at providing actionable insights to guide proactive management of cardiovascular disease.
"Although there are clear physiological patterns that precede acute heart failure events, identifying them has traditionally required invasive methods only available in acute care settings," said Amit Gupta, CEO and co-founder of Cardiosense. "SEISMIC-HF II represents a critical step in validating our non-invasive solution to identify early warning signs, which has the potential to measurably improve the lives of heart failure patients at scale."
Study Design and Methodology
The SEISMIC-HF II study is designed as a prospective, multi-center, blinded trial that will collect data from patients undergoing a right heart catheterization (RHC) across multiple geographically diverse sites. Cardiosense's non-invasive PCWP estimate will be compared to the RHC measurements to validate the performance of the AI algorithm. Results from this study will be used to support regulatory filings.
"The results from the SEISMIC-HF I study hold tremendous potential to greatly expand patient access to hemodynamic-guided remote care management through a non-invasive solution," said Allman Rollins, M.D. of Inova Fairfax Medical Campus. "I was excited to participate in that study and am thrilled Inova was the first site to enroll patients in SEISMIC-HF II."
Clinical Impact and Future Implications
The validation of this non-invasive technology could transform heart failure management by enabling early detection of decompensation events outside of acute care settings. By providing clinicians with actionable insights comparable to invasive monitoring methods, the algorithm has the potential to reduce hospital readmissions and improve patient outcomes while lowering healthcare costs associated with heart failure management.
