Model-based quantification of left ventricular diastolic function in critically ill patients with atrial fibrillation from routine data: a feasibility study
type: Item
instance of: scholarly article
title: Model-based quantification of left ventricular diastolic function in critically ill patients with atrial fibrillation from routine data: a feasibility study
zbMATH Open document ID: 1423.92034
DOI: 10.1155/2019/9682138
author: Maximilian J. Oremek
author: Andreas Hoeft
author: Sven Zenker
author: Nicholas M. Kiefer
published in: Computational \& Mathematical Methods in Medicine
publication date: 2019-07-09T00:00:00Z
review text: Summary: \textit{Introduction}. Left ventricular diastolic dysfunction (LVDD) and atrial fibrillation (AF) are connected by pathophysiology and prevalence. LVDD remains underdiagnosed in critically ill patients despite potentially significant therapeutic implications since direct measurement cannot be performed in routine care at the bedside, and echocardiographic assessment of LVDD in AF is impaired. We propose a novel approach that allows us to infer the diastolic stiffness, \(\beta\), a key quantitative parameter of diastolic function, from standard monitoring data by solving the nonlinear, ill-posed inverse problem of parameter estimation for a previously described mechanistic, physiological model of diastolic filling. The beat-to-beat variability in AF offers an advantageous setting for this. \textit{Methods}. By employing a global optimization algorithm, \(\beta\) is inferred from a simple six parameter and an expanded seven parameter model of left ventricular filling. Optimization of all parameters was limited to the interval \(]0, 400[\) and initialized randomly on large intervals encompassing the support of the likelihood function. Routine ECG and arterial pressure recordings of 17 AF and 3 sinus rhythm (SR) patients from the PhysioNet MGH/MF Database were used as inputs. \textit{Results}. Estimation was successful in 15 of 17 AF patients, while in the 3 SR patients, no reliable estimation was possible. For both models, the inferred \(\beta\) (\(0.065 \pm 0.044\) ml vs. \(0.038\pm 0.033\) ml (\(p = 0.02\)) simple vs. expanded) was compatible with the previously described (patho) physiological range. Aortic compliance, \(\alpha\), inferred from the expanded model (\(1.46\pm 1.50\) ml/mmHg) also compared well with literature values. \textit{Conclusion}. The proposed approach successfully inferred \(\beta\) within the physiological range. This is the first report of an approach quantifying LVDF from routine monitoring data in critically ill AF patients. Provid…
Mathematics Subject Classification ID: 92C30
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 7077779
zbMATH Keywords: left ventricular diastolic dysfunction
zbMATH Keywords: atrial fibrillation
zbMATH Keywords: model-based quantification
Wikidata QID: Q64912471
describes a project that uses: Boost C++ Libraries
describes a project that uses: Armadillo
describes a project that uses: SciPy
describes a project that uses: Boost
describes a project that uses: ADOL-C
describes a project that uses: trng
describes a project that uses: PhysioToolkit
describes a project that uses: Matplotlib
describes a project that uses: NumPy
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/9682138
OpenAlex ID: W2944844106
cites work: The inverse problem in mathematical biology
cites work: Algorithm 755: ADOL-C
cites work: On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
cites work: Q2753022
DBLP publication ID: journals/cmmm/KieferOHZ19
description: scientific article; zbMATH DE number 7077779