Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Parameter inference in a computational model of haemodynamics in pulmonary hypertension

Title: Parameter inference in a computational model of haemodynamics in pulmonary hypertension
Authors: Colunga, Amanda L; Colebank, Mitchel J; Program, REU; Olufsen, Mette S
Source: Journal of The Royal Society Interface, vol 20, iss 200
Publisher Information: eScholarship, University of California
Publication Year: 2023
Collection: University of California: eScholarship
Subject Terms: Biomedical and Clinical Sciences; Cardiovascular Medicine and Haematology; Lung; Cardiovascular; Heart Disease; 4.2 Evaluation of markers and technologies; Humans; Hypertension; Pulmonary; Hemodynamics; Arteries; Computer Simulation; Models; pulmonary hypertension; computational model; parameter inference; cardiovascular modelling; REU Program; General Science & Technology
Time: 20220735
Description: Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) greater than 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary arterial compliance. There are few measurable biomarkers of PH progression, but a conclusive diagnosis of the disease requires invasive right heart catheterization (RHC). Patient-specific cardiovascular systems-level computational models provide a potential non-invasive tool for determining additional indicators of disease severity. Using computational modelling, this study quantifies physiological parameters indicative of disease severity in nine PH patients. The model includes all four heart chambers, the pulmonary and systemic circulations. We consider two sets of calibration data: static (systolic and diastolic values) RHC data and a combination of static and continuous, time-series waveform data. We determine a subset of identifiable parameters for model calibration using sensitivity analyses and multi-start inference and perform posterior uncertainty quantification. Results show that additional waveform data enables accurate calibration of the right atrial reservoir and pump function across the PH cohort. Model outcomes, including stroke work and pulmonary resistance-compliance relations, reflect typical right heart dynamics in PH phenotypes. Lastly, we show that estimated parameters agree with previous, non-modelling studies, supporting this type of analysis in translational PH research.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: unknown
Relation: qt3v25g863; https://escholarship.org/uc/item/3v25g863; https://escholarship.org/content/qt3v25g863/qt3v25g863.pdf
DOI: 10.1098/rsif.2022.0735
Availability: https://escholarship.org/uc/item/3v25g863; https://escholarship.org/content/qt3v25g863/qt3v25g863.pdf; https://doi.org/10.1098/rsif.2022.0735
Rights: CC-BY
Accession Number: edsbas.3C3ACDCD
Database: BASE