Related Experiment Video
Updated: May 19, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Assessment of cardiovascular function by combining clinical data with a computational model of the cardiovascular
Koichi Sughimoto1, Fuyou Liang, Yoshiharu Takahara
1Department of Cardiovascular Surgery, The Heart Institute of Japan, Tokyo Women's Medical University, Tokyo, Japan. ksughimoto@gmail.com
Insights
This study shows that combining patient clinical data with a computational model can quantitatively assess cardiovascular function. This approach aids in personalized treatment decisions for cardiovascular disease.
Area of Science:
- Cardiovascular Physiology
- Computational Biology
- Medical Informatics
Background:
- Accurate assessment of cardiovascular status is crucial for effective cardiovascular disease treatment.
- Clinical measurements alone often provide insufficient data for comprehensive patient assessment.
- Limitations in current clinical measurements necessitate advanced methods for understanding cardiovascular function.
Purpose of the Study:
- To determine if cardiovascular function can be quantitatively assessed for individual patients.
- To integrate clinical data with a computational model of the cardiovascular system.
- To enable patient-specific quantitative analysis of cardiovascular status.
Main Methods:
- Enrolled seven patients undergoing off-pump coronary artery bypass grafting.
- Collected clinical data preoperatively and intraoperatively.
- Utilized sensitivity analysis and data-fitting to estimate key cardiovascular model parameters for patient-specific assessment.
Main Results:
- Identified left ventricular diastolic dysfunction in all patients, with significant interpatient variability.
- Quantified left ventricular passive elastance, showing a range from 194% to 540% of reference values.
- Found impaired left ventricular systolic function in 4 out of 7 patients.
Conclusions:
- Demonstrated the feasibility of quantitatively assessing cardiovascular function using a combined clinical data and computational model approach.
- The method leverages existing clinical measurements, enhancing its practical applicability.
- This technique offers a promising tool for personalized cardiovascular medicine.
Objective:
A sufficient understanding of patients' cardiovascular status is necessary for doctors to make the best decisions with regard to the treatment of cardiovascular disease; however, it is often not available because of the limitation of clinical measurements. The objective of this study was to examine whether cardiovascular function can be assessed quantitatively and for specific patients by combining clinical data with a computational model of the cardiovascular system.
Methods:
Seven consecutive patients undergoing off-pump coronary artery bypass grafting were enrolled in this study. The clinical data were collected both during the preoperative diagnosis and during the operation. Sensitivity analysis was performed to select the major model parameters most relevant to the measured data. The major model parameters were then estimated through a data-fitting procedure, enabling a patient-specific quantitative assessment of various aspects of cardiovascular function.
Results:
The results revealed the prevalence of left ventricular diastolic dysfunction in the patients, although the severity of dysfunction exhibits significant interpatient variability (the estimated left ventricular passive elastance varies from 194% to 540% of its reference value). Moreover, 4 of the 7 patients studied had impaired left ventricular systolic function.
Conclusions:
The current study demonstrates the feasibility of assessing cardiovascular function quantitatively by combining clinical data with a cardiovascular model. In particular, the assessment utilizes the measurements already in use or available in clinical settings, enhancing the clinical potential of the proposed method.
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