Turbulent-like blood flow in neo-aorta in post-norwood patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Surgical reconstruction of the aortic arch in children born with hypoplastic left heart syndrome (HLHS) can induce disturbed or turbulent-like blood flow patterns characterized by high-frequency velocity fluctuations (turbulent-like flow). However, previous studies have primarily focused on laminar flows as a surrogate marker of aorto-ventricular inefficiencies. In this study, we utilized high-resolution computational fluid dynamics (CFD) simulations and frequency-based analysis to demonstrate that abnormal neo-aortic geometries resulting from post-surgical anatomies can induce turbulent-like blood flow patterns. MATERIALS AND METHODS: Four patients with various HLHS conditions after the Norwood surgery were included: i) two with healthy, unobstructed neo-aortic reconstructions; and ii) two with neo-aortic abnormalities, including stenosis and arch dilations. Two controls included were variants of single ventricle condition with a normal aorta. A novel geometric parameter, Diameter Deviation, was developed that quantified diameter irregularities while accounting for natural tapering of the aorta. High-resolution CFD simulations were performed and hemodynamic parameters, including wall shear stresses (WSS), oscillatory shear index (OSI) and spectral power index (SPI) were assessed; the latter quantified turbulent intensities. Power spectral density (PSD) of the velocity-time signal in the descending aorta was analysed. RESULTS: SPI in diseased cases (Stenotic and Aneurysmal) was four to six-fold higher compared to the Control and Healthy cases, highlighting the presence of turbulent-like blood flow. Diameter Deviation was one-to three-fold higher in the diseased cases compared to the Control and Healthy cases. PSD analysis showed notable energy content even at frequencies as high as 1000 Hz in the diseased cases that were indictive of turbulent-like flow phenomenon. CONCLUSION: Abnormal diameter irregularities may induce turbulent-like flow patterns that could be detrimental to the healthy growth of the neo-aorta. Post-surgical outcomes could potentially be improved through optimal neo-aortic reconstructions that minimize diameter irregularities and turbulent-like blood flow conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".