Fast and robust parameter estimation with uncertainty quantification for the cardiac function

Salvador, Matteo; Regazzoni, Francesco; Dedè, Luca; Quarteroni, Alfio

doi:10.1016/j.cmpb.2023.107402

Cited by 16 publications

(9 citation statements)

References 38 publications

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“…More sophisticated strategies that account for a selective choice of the new parameter configuration starting from the previous ones (e.g. Markov chain Monte Carlo methods 27 ), which nevertheless entail a larger computational cost, could be considered in further developments of this work.…”

Section: Discussionmentioning

confidence: 99%

“…resistances). To properly select such values for a specific compartment and patient, a calibration procedure was needed 27 , 28 .…”

Section: Methodsmentioning

confidence: 99%

“…For the remaining parameters, the calibration of the model relied on the method we presented in Ref. 27 , that is aimed to reduce the sum of squared relative errors between the model outputs MO1 and clinical data, modifying the parameters of the model in suitable bounded intervals , for , where is the number of parameters, independent of the patient, built starting from the reference values of parameters mentioned before (Supplementary Table S3 ). Specifically, we chose to calibrate those parameters among the latter according to a sensitivity analysis estimating the absolute correlation coefficients between parameters and model outputs (Supplementary Table S4 ).…”

Section: Methodsmentioning

confidence: 99%

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A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

Tonini,

Vergara,

Regazzoni

et al. 2024

Sci Rep

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Impaired cardiac function has been described as a frequent complication of COVID-19-related pneumonia. To investigate possible underlying mechanisms, we represented the cardiovascular system by means of a lumped-parameter 0D mathematical model. The model was calibrated using clinical data, recorded in 58 patients hospitalized for COVID-19-related pneumonia, to make it patient-specific and to compute model outputs of clinical interest related to the cardiocirculatory system. We assessed, for each patient with a successful calibration, the statistical reliability of model outputs estimating the uncertainty intervals. Then, we performed a statistical analysis to compare healthy ranges and mean values (over patients) of reliable model outputs to determine which were significantly altered in COVID-19-related pneumonia. Our results showed significant increases in right ventricular systolic pressure, diastolic and mean pulmonary arterial pressure, and capillary wedge pressure. Instead, physical quantities related to the systemic circulation were not significantly altered. Remarkably, statistical analyses made on raw clinical data, without the support of a mathematical model, were unable to detect the effects of COVID-19-related pneumonia in pulmonary circulation, thus suggesting that the use of a calibrated 0D mathematical model to describe the cardiocirculatory system is an effective tool to investigate the impairments of the cardiocirculatory system associated with COVID-19.

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Section: Discussionmentioning

confidence: 99%

“…resistances). To properly select such values for a specific compartment and patient, a calibration procedure was needed 27 , 28 .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

Tonini,

Vergara,

Regazzoni

et al. 2024

Sci Rep

Self Cite

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“…Moreover, HMC reaches convergence using a reduced amount of samples with respect to vanilla MCMC [21]. For further details about the mathematical derivation of HMC and its application to cardiac simulations we refer to [54].…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

“…In the foreseeable future, a continuous, bi-directional interaction between patient-specific data and Artificial Intelligence-enriched computer models incorporating biophysically detailed and anatomically accurate knowledge would enable the vision of precision medicine [34,38,44]. Personalized treatment and surgical planning may be delivered by leveraging different mathematical methods, such as sensitivity analysis, parameter inference and uncertainty quantification [19,25,49,54].…”

Section: Introductionmentioning

confidence: 99%

Digital twinning of cardiac electrophysiology for congenital heart disease

Salvador,

Kong,

Peirlinck

et al. 2023

Preprint

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In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in pediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and utilizing rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in pediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

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Uncertainty quantification of the pressure waveform using a Windkessel model

Flores‐Gerónimo,

Keramat,

Alastruey

et al. 2024

Numer Methods Biomed Eng

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The Windkessel (WK) model is a simplified mathematical model used to represent the systemic arterial circulation. While the WK model is useful for studying blood flow dynamics, it suffers from inaccuracies or uncertainties that should be considered when using it to make physiological predictions. This paper aims to develop an efficient and easy‐to‐implement uncertainty quantification method based on a local gradient‐based formulation to quantify the uncertainty of the pressure waveform resulting from aleatory uncertainties of the WK parameters and flow waveform. The proposed methodology, tested against Monte Carlo simulations, demonstrates good agreement in estimating blood pressure uncertainties due to uncertain Windkessel parameters, but less agreement considering uncertain blood‐flow waveforms. To illustrate our methodology's applicability, we assessed the aortic pressure uncertainty generated by Windkessel parameters‐sets from an available in silico database representing healthy adults. The results from the proposed formulation align qualitatively with those in the database and in vivo data. Furthermore, we investigated how changes in the uncertainty of the Windkessel parameters affect the uncertainty of systolic, diastolic, and pulse pressures. We found that peripheral resistance uncertainty produces the most significant change in the systolic and diastolic blood pressure uncertainties. On the other hand, compliance uncertainty considerably modifies the pulse pressure standard deviation. The presented expansion‐based method is a tool for efficiently propagating the Windkessel parameters' uncertainty to the pressure waveform. The Windkessel model's clinical use depends on the reliability of the pressure in the presence of input uncertainties, which can be efficiently investigated with the proposed methodology. For instance, in wearable technology that uses sensor data and the Windkessel model to estimate systolic and diastolic blood pressures, it is important to check the confidence level in these calculations to ensure that the pressures accurately reflect the patient's cardiovascular condition.

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Fast and robust parameter estimation with uncertainty quantification for the cardiac function

Cited by 16 publications

References 38 publications

A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

Digital twinning of cardiac electrophysiology for congenital heart disease

Uncertainty quantification of the pressure waveform using a Windkessel model

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