October 15, 2020Scientific ReportsOpen Access

Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

Q: What are the key findings of this study?

The multi-level computational plaque growth model achieved 80% accuracy in predicting disease progression in coronary arteries over 6.1 years.

Q: What is the clinical evidence from this study?

Study design: Observational. Population: Atherosclerosis (n=94). Intervention: Multi-level computational plaque growth model vs. Real follow-up arterial geometries based on CTCA measurements. Primary outcome: Disease progression, evaluated by simulated plaque area and lumen area change (p=<0.0001).

Q: What does this research mean for the field?

The multi-level computational plaque growth model achieved 80% accuracy in predicting disease progression in coronary arteries over 6.1 years. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

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Key result

Multi-level computational plaque model predicts coronary disease progression with ~80% accuracy.

P<0.0001
n=94

Why the study?

Atherosclerosis is a major cause of mortality worldwide, urging the need for prevention strategies through computational modeling of plaque growth.

Does a computational biomechanics model accurately simulate and predict atherosclerotic plaque growth in human coronary arteries compared to serial CTCA imaging?

Population

94 realistic 3D reconstructed coronary arteries

Comparison

Simulated geometries vs real follow-up arteries on serial CTCA

Design

Computational simulation and validation study

Authors

DPDimitrios S. PleourasUniversity of Ioannina ASAntonis I. SakellariosUniversity of Patras PTPanagiota TsompouCardiac Imaging

Discussion

Member takes

Implication

Hypothesis-generating for computational plaque prediction; leaves open clinical utility pending prospective validation.

Study Design

Type

Observational (n=94)

Multicenter

Yes

Structured PICO

Does a computational biomechanics model accurately simulate and predict atherosclerotic plaque growth in human coronary arteries compared to serial CTCA imaging?

Population

94 patients (900 3 mm sub-segments of coronary arteries) from the SMARTool clinical study with baseline and follow-up CTCA imaging, mean age 60.30, 60.64% male, multinational (Europe).

Intervention

Multi-level computational plaque growth model simulating blood flow dynamics, species transport (LDL, HDL, monocytes), inflammation, and wall thickening over the interscan period.

Comparator

Real follow-up arterial geometries and plaque burden assessed via serial CTCA imaging.

Outcome

Correlation between simulated and real follow-up arterial lumen area, wall area, and plaque burden, and accuracy of predicting disease progression.surrogate

Main Result

p-value: p=<0.0001

A novel computational biomechanics model successfully simulated and predicted atherosclerotic plaque growth and disease progression with 80% accuracy when compared to serial CTCA imaging.

Limitations

Small sample size limits generalizability.
Different stiffness of plaque regions was neglected.
Did not consider plaque composition and its relation with specific plaque types.

Cite This Study

Pleouras et al. (2020) conducted an observational in Atherosclerosis (n=94). Multi-level computational plaque growth model vs. Real follow-up arterial geometries based on CTCA measurements was evaluated on Disease progression, evaluated by simulated plaque area and lumen area change (p=<0.0001). The multi-level computational plaque growth model achieved 80% accuracy in predicting disease progression in coronary arteries over 6.1 years.

synapsesocial.com/papers/696032b1402a67f9580e6428 https://doi.org/10.1038/s41598-020-74583-y

Topics

Coronary artery disease Coronary CT angiography Artificial intelligence in cardiology

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October 15, 2020Scientific ReportsOpen Access

Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

View Full Paper

Ask AI

Bookmark

Key result

Multi-level computational plaque model predicts coronary disease progression with ~80% accuracy.

P<0.0001
n=94

Why the study?

Atherosclerosis is a major cause of mortality worldwide, urging the need for prevention strategies through computational modeling of plaque growth.

Does a computational biomechanics model accurately simulate and predict atherosclerotic plaque growth in human coronary arteries compared to serial CTCA imaging?

Population

94 realistic 3D reconstructed coronary arteries

Comparison

Simulated geometries vs real follow-up arteries on serial CTCA

Design

Computational simulation and validation study

Authors

DPDimitrios S. PleourasUniversity of Ioannina ASAntonis I. SakellariosUniversity of Patras PTPanagiota TsompouCardiac Imaging

Discussion

Member takes

Implication

Hypothesis-generating for computational plaque prediction; leaves open clinical utility pending prospective validation.

Study Design

Type

Observational (n=94)

Multicenter

Yes

Structured PICO

Does a computational biomechanics model accurately simulate and predict atherosclerotic plaque growth in human coronary arteries compared to serial CTCA imaging?

Population

94 patients (900 3 mm sub-segments of coronary arteries) from the SMARTool clinical study with baseline and follow-up CTCA imaging, mean age 60.30, 60.64% male, multinational (Europe).

Intervention

Multi-level computational plaque growth model simulating blood flow dynamics, species transport (LDL, HDL, monocytes), inflammation, and wall thickening over the interscan period.

Comparator

Real follow-up arterial geometries and plaque burden assessed via serial CTCA imaging.

Outcome

Correlation between simulated and real follow-up arterial lumen area, wall area, and plaque burden, and accuracy of predicting disease progression.surrogate

Main Result

p-value: p=<0.0001

A novel computational biomechanics model successfully simulated and predicted atherosclerotic plaque growth and disease progression with 80% accuracy when compared to serial CTCA imaging.

Limitations

Small sample size limits generalizability.
Different stiffness of plaque regions was neglected.
Did not consider plaque composition and its relation with specific plaque types.

Cite This Study

synapsesocial.com/papers/696032b1402a67f9580e6428 https://doi.org/10.1038/s41598-020-74583-y

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Topics

Coronary artery disease Coronary CT angiography Artificial intelligence in cardiology

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