What is the clinical evidence from this study?

Intervention: Computational fluid dynamics modeling. Primary outcome: Flow patterns, velocity vectors, and pressure drops.

What question did this study set out to answer?

This investigation aims to extend a cardiac model to include both systolic and diastolic phases and examine the effect of one cardiac cycle on the next.

April 1, 1995Biorheology

Flow patterns in three-dimensional left ventricular systolic and diastolic flows determined from computational fluid dynamics

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Key result

Computational modeling of the left ventricle demonstrated that maximum velocity could vary by over 30% between the first and second cardiac cycles, with complex vortex formation during diastole.

Key Points

This investigation aims to extend a cardiac model to include both systolic and diastolic phases and examine the effect of one cardiac cycle on the next.
Constructed a realistic left ventricle model using a dog heart cast in diastole.
Simulated the volume dynamics with a 40% reduction in 0.25 seconds, followed by a recovery to original volume.
Analyzed flow patterns across multiple cardiac cycles, assessing velocity vectors and pressures.
Ejection fraction of 60% showed minimal variation in flow after three cardiac cycles.
Maximum velocity variation exceeded 30% between the first and second cycles.
Diastolic phase exhibited complex vortex formation linked to turbulent blood flow in the aorta.

Structured PICO

Population

Computational fluid dynamics model of a dog heart left ventricle simulating both systole and diastole.

Exposure

Simulation of both systolic and diastolic phases across multiple cardiac cycles

Outcome

Flow patterns, velocity vectors, pressure drop, and vortex formationsurrogate

Computational fluid dynamics modeling of the left ventricle demonstrates that flow patterns stabilize after three cardiac cycles and reveals complex diastolic vortex formation.

Discussion

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Implication

LV flow models require multiple cycles for stability; leaves open clinical relevance of diastolic vortices in humans.

Cite This Study

Taylor et al. (1995) studied this question. Computational fluid dynamics modeling was evaluated on Flow patterns, velocity vectors, and pressure drops. Computational modeling of the left ventricle demonstrated that maximum velocity could vary by over 30% between the first and second cardiac cycles, with complex vortex formation during diastole.

synapsesocial.com/papers/6a20209954ef0cdb79a1aea9 https://doi.org/10.3233/bir-1995-32105

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Flow patterns in three-dimensional left ventricular systolic and diastolic flows determined from computational fluid dynamics

Key Points

Structured PICO

Discussion

Implication

Cite This Study

Flow patterns in three-dimensional left ventricular systolic and diastolic flows determined from computational fluid dynamics

Key Points

Structured PICO

Discussion

Implication

Cite This Study

Also Consider

Also Consider