Estimating central blood pressure from aortic flow: development and assessment of algorithms

Author:

Mariscal-Harana Jorge1ORCID,Charlton Peter H.1ORCID,Vennin Samuel12ORCID,Aramburu Jorge3ORCID,Florkow Mateusz Cezary14ORCID,van Engelen Arna1,Schneider Torben5,de Bliek Hubrecht6,Ruijsink Bram17ORCID,Valverde Israel18ORCID,Beerbaum Philipp9,Grotenhuis Heynric10,Charakida Marietta1,Chowienczyk Phil2,Sherwin Spencer J.11,Alastruey Jordi112ORCID

Affiliation:

1. Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom

2. Department of Clinical Pharmacology, King’s College London, King’s Health Partners, London , United Kingdom

3. TECNUN Escuela de Ingenieros, Universidad de Navarra, Donostia-San Sebastián, Spain

4. Philips Research, Cambridge, United Kingdom

5. Philips Healthcare UK, Philips Centre, Guildford Business Park, Guildford, Surrey, United Kingdom

6. HSDP Clinical Platforms, Philips Healthcare, Eindhoven, The Netherlands

7. Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands

8. Cardiovascular Pathophysiology, Institute of Biomedicine of Seville, University Hospital of Virgen del Rocío, University of Seville, CIBERCV, CSIC, Seville, Spain

9. Department of Pediatric Cardiology and Intensive Care, Hannover Medical School, Hannover, Germany

10. Department of Pediatric Cardiology, University Medical Center Utrecht/Wilhelmina Children’s Hospital, Utrecht, The Netherlands

11. Department of Aeronautics, South Kensington Campus, Imperial College London, London, United Kingdom

12. Institute of Personalized Medicine, Sechenov University, Moscow, Russia

Abstract

First, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available.

Funder

British Heart Foundation

Engineering and Physical Sciences Research Council

King's College London Medical Engineering Centre

DH | National Institute for Health Research

Publisher

American Physiological Society

Subject

Physiology (medical),Cardiology and Cardiovascular Medicine,Physiology

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