Dataset Information

Lumped-parameter model as a non-invasive tool to assess coronary blood flow in AAOCA patients.

ABSTRACT: Anomalous aortic origin of the coronary artery (AAOCA) is a rare disease associated with sudden cardiac death, usually related to physical effort in young people. Clinical routine tests fail to assess the ischemic risk, calling for novel diagnostic approaches. To this aim, some recent studies propose to assess the coronary blood flow (CBF) in AAOCA by computational simulations but they are limited by the use of data from literature retrieved from normal subjects. To overcome this limitation and obtain a reliable assessment of CBF, we developed a fully patient-specific lumped parameter model based on clinical imaging and in-vivo data retrieved during invasive coronary functional assessment of subjects with AAOCA. In such a way, we can estimate the CBF replicating the two hemodynamic conditions in-vivo analyzed. The model can mimic the effective coronary behavior with high accuracy and could be a valuable tool to quantify CBF in AAOCA. It represents the first step required to move toward a future clinical application with the aim of improving patient care. The study was registered at Clinicaltrial.gov with (ID: NCT05159791, date 2021-12-16).

SUBMITTER: Ceserani V

PROVIDER: S-EPMC10576762 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Lumped-parameter model as a non-invasive tool to assess coronary blood flow in AAOCA patients.

Ceserani Valentina V Lo Rito Mauro M Agnifili Mauro Luca ML Pascaner Ariel F AF Rosato Antonio A Anglese Serena S Deamici Miriam M Negri Jessica J Corrado Chiara C Bedogni Francesco F Secchi Francesco F Lombardi Massimo M Auricchio Ferdinando F Frigiola Alessandro A Conti Michele M

Scientific reports 20231014 1

Anomalous aortic origin of the coronary artery (AAOCA) is a rare disease associated with sudden cardiac death, usually related to physical effort in young people. Clinical routine tests fail to assess the ischemic risk, calling for novel diagnostic approaches. To this aim, some recent studies propose to assess the coronary blood flow (CBF) in AAOCA by computational simulations but they are limited by the use of data from literature retrieved from normal subjects. To overcome this limitation and ...[more]

PMID: 37838795

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Project description:Although temperature can significantly affect the stability and degradation of drug nanosuspensions, temperature evolution during the production of drug nanoparticles via wet stirred media milling, also known as nanomilling, has not been studied extensively. This study aims to establish both descriptive and predictive capabilities of a semi-theoretical lumped parameter model (LPM) for temperature evolution. In the experiments, the mill was operated at various stirrer speeds, bead loadings, and bead sizes, while the temperature evolution at the mill outlet was recorded. The LPM was formulated and fitted to the experimental temperature profiles in the training runs, and its parameters, i.e., the apparent heat generation rate Qgen and the apparent overall heat transfer coefficient times surface area UA, were estimated. For the test runs, these parameters were predicted as a function of the process parameters via a power law (PL) model and machine learning (ML) model. The LPM augmented with the PL and ML models was used to predict the temperature evolution in the test runs. The LPM predictions were also compared with those of an enthalpy balance model (EBM) developed recently. The LPM had a fitting capability with a root-mean-squared error (RMSE) lower than 0.9 °C, and a prediction capability, when augmented with the PL and ML models, with an RMSE lower than 4.1 and 2.1 °C, respectively. Overall, the LPM augmented with the PL model had both good descriptive and predictive capability, whereas the one with the ML model had a comparable predictive capability. Despite being simple, with two parameters and obviating the need for sophisticated numerical techniques for its solution, the semi-theoretical LPM generally predicts the temperature evolution similarly or slightly better than the EBM. Hence, this study has provided a validated, simple model for pharmaceutical engineers to simulate the temperature evolution during the nanomilling process, which will help to set proper process controls for thermally labile drugs.

Dataset Information

Lumped-parameter model as a non-invasive tool to assess coronary blood flow in AAOCA patients.

Publications

Lumped-parameter model as a non-invasive tool to assess coronary blood flow in AAOCA patients.

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