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Machine learning

Cardiosense's Noninvasive Sensor and AI Algorithm Succeeds in Estimating Cardiac Filling Pressure in Heart Failure Patients

Strategic Insights

— 1 min read

A landmark study published in the Journal of the American College of Cardiology: Heart Failure demonstrates the efficacy of Cardiosense's novel machine learning algorithm in accurately estimating pulmonary capillary wedge pressure (PCWP) in patients with heart failure with reduced ejection fraction (HFrEF) using a noninvasive sensor. The prospective, multi-center study included 310 patients with diverse demographics and clinical characteristics, indicating the potential for the algorithm to generalize across various patient populations. The results show that the device estimated absolute PCWP values with accuracy comparable to implantable hemodynamic sensors, suggesting a promising alternative to invasive procedures.

Key Takeaways:

  • Cardiosense's machine learning algorithm achieved comparable performance to implanted sensors in estimating cardiac filling pressure in heart failure patients.
  • The algorithm demonstrated consistent performance across sex, race, ethnicity, and body mass index.
  • The study population consisted of 310 patients with heart failure with reduced ejection fraction, reflecting a diverse set of participants.
  • The device estimated absolute PCWP values with accuracy on par with implantable hemodynamic sensors.
  • Additional analysis is available in the full manuscript, titled "Noninvasive Pulmonary Capillary Wedge Pressure Estimation in Heart Failure Patients With the Use of Wearable Sensing and AI."
  • The study's findings have significant implications for heart failure management, suggesting the potential to increase access to hemodynamic-guided care.
  • Initial results from the study were presented as Late-Breaking Science at the American Heart Association's 2024 Scientific Sessions.

Statistics:

  • 310 patients with HFrEF were enrolled in the study.
  • The study was conducted across 15 sites.
  • The algorithm demonstrated consistent performance across diverse patient demographics and clinical characteristics.
  • The estimated absolute PCWP values achieved accuracy on par with implantable hemodynamic sensors.
  • The results have significant implications for the management of heart failure, with potential to increase access to hemodynamic-guided care.

Sources:

  • Cardiosense. (2025, June 24). Cardiosense Announces Publication of Results from SEISMIC-HF I Study in JACC: Heart Failure. Business Wire.
  • Cardiosense. (2024). Noninvasive Pulmonary Capillary Wedge Pressure Estimation in Heart Failure Patients With the Use of Wearable Sensing and AI. JACC: Heart Failure.

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