Application of Mechanical Restitution

Meguro, Tomomi; Amaki, Makoto; Suzuki, Yoshiyuki; Matsubara, Takashi; Yokozuka, Hitoshi; Ogawa, Satoshi

doi:10.1253/jcj.62.829

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…After fitting the SM resulting from equation (4) to patient data, we observed a strong systematic dependence of residuals on the RRI preceding the RRI directly determining the filling duration. Such a dependence has previously been attributed to variations of the inotropic state of the heart, among other things [42, 43]. The model was therefore expanded to include a simple linear correction to compensate these effects.…”

Section: Methodsmentioning

confidence: 99%

Model-Based Quantification of Left Ventricular Diastolic Function in Critically Ill Patients with Atrial Fibrillation from Routine Data: A Feasibility Study

Kiefer

Oremek

Hoeft

et al. 2019

Computational and Mathematical Methods in Medicine

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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, β, 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. Methods. By employing a global optimization algorithm, β 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. 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 β (0.065 ± 0.044 ml−1 vs. 0.038 ± 0.033 ml−1 (p=0.02) simple vs. expanded) was compatible with the previously described (patho) physiological range. Aortic compliance, α, inferred from the expanded model (1.46 ± 1.50 ml/mmHg) also compared well with literature values. Conclusion. The proposed approach successfully inferred β within the physiological range. This is the first report of an approach quantifying LVDF from routine monitoring data in critically ill AF patients. Provided future successful external validation, this approach may offer a tool for minimally invasive online monitoring of this crucial parameter.

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Section: Methodsmentioning

confidence: 99%