2022
DOI: 10.3389/fcvm.2022.895291
|View full text |Cite
|
Sign up to set email alerts
|

Framework for patient-specific simulation of hemodynamics in heart failure with counterpulsation support

Abstract: Despite being responsible for half of heart failure-related hospitalizations, heart failure with preserved ejection fraction (HFpEF) has limited evidence-based treatment options. Currently, a substantial clinical issue is that the disease etiology is very heterogenous with no patient-specific treatment options. Modeling can provide a framework for evaluating alternative treatment strategies. Counterpulsation strategies have the capacity to improve left ventricular diastolic filling by reducing systolic blood p… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 45 publications
0
1
0
Order By: Relevance
“…These models can be used to predict tumor progression, optimize treatment strategies, and identify potential therapeutic targets [11][12][13][14] . Digital twins simulating the function of the human heart and its response to various interventions, such as pacemaker settings or drug therapies, and patient-specific digital twins of vascular systems simulating blood flow, pressure, and other hemodynamic parameters can be used to optimize treatment strategies and predict patient outcomes in heart failure, arrhythmias, and other cardiovascular conditions [15][16][17] . Digital twins have also been developed for patients with type 1 and type 2 diabetes to simulate glucose metabolism, insulin sensitivity, and the effects of various interventions, such as insulin administration and lifestyle modifications, thus helping optimize glucose control and personalize diabetes management strategies [18][19][20][21] .…”
mentioning
confidence: 99%
“…These models can be used to predict tumor progression, optimize treatment strategies, and identify potential therapeutic targets [11][12][13][14] . Digital twins simulating the function of the human heart and its response to various interventions, such as pacemaker settings or drug therapies, and patient-specific digital twins of vascular systems simulating blood flow, pressure, and other hemodynamic parameters can be used to optimize treatment strategies and predict patient outcomes in heart failure, arrhythmias, and other cardiovascular conditions [15][16][17] . Digital twins have also been developed for patients with type 1 and type 2 diabetes to simulate glucose metabolism, insulin sensitivity, and the effects of various interventions, such as insulin administration and lifestyle modifications, thus helping optimize glucose control and personalize diabetes management strategies [18][19][20][21] .…”
mentioning
confidence: 99%