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Artificial intelligence-based assessment of central aortic haemodynamics using non-invasive pulse wave analysis in

Mathieu N Suleiman1, Oliver Dewald2, Helena Dreher3

  • 1Department of Cardiac Surgery, University Hospital Erlangen , Erlangen, Bayern, Germany mathieu.suleiman@uk-erlangen.de.

Open Heart
|December 31, 2025
PubMed

Insights

Artificial intelligence-based non-invasive pulse wave analysis (AI-PWA) effectively assesses central hemodynamics and arterial stiffness in constrictive pericarditis (CP). This non-invasive method shows promise for improving CP diagnosis and management.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Constrictive pericarditis (CP) is a rare condition causing heart failure due to impaired ventricular filling, often post-cardiac surgery.
  • Diagnosis of CP is challenging due to its presentation mimicking other heart failure causes.
  • Artificial intelligence (AI)-based non-invasive pulse wave analysis (AI-PWA) offers potential for enhanced hemodynamic assessment.

Purpose of the Study:

  • To evaluate the clinical utility of AI-PWA in assessing central aortic blood pressure (CABP), arterial stiffness, and cardiac function in CP patients.
  • To compare hemodynamic parameters between CP patients and healthy controls using AI-PWA.

Main Methods:

  • Prospective case-control study involving 12 adult CP patients and 12 matched healthy controls.
  • Measurement of central aortic blood pressure (CABP) and peripheral blood pressure (PBP) using VascAssist2.
  • Assessment of hemodynamic parameters including pulse wave velocity (PWV), augmentation index@75 (AIx@75), cardiac index, stroke volume, and dP/dtmax.

Main Results:

  • CP patients exhibited lower mean CABP compared to systolic PBP.
  • Elevated PWV (>9 m/s) and higher AIx@75 in CP patients indicated increased arterial stiffness and wave reflection.
  • Impaired cardiac performance in CP patients shown by reduced stroke volume and dP/dtmax, and a significantly higher heart failure index.

Conclusions:

  • AI-PWA provides clinically relevant insights into central hemodynamics and arterial stiffness in CP.
  • This non-invasive approach shows potential to enhance the diagnosis and management of CP.
  • Integration of AI-PWA into routine cardiologic evaluation protocols is recommended.
Abstract

Keywords:
Cardiac Surgical ProceduresHeart Failure, DiastolicPericarditis, Constrictive

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