Non-Invasive Hemodynamic Monitoring in Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Background/Objectives: Aortic valve stenosis (AS) is a prevalent cardiovascular condition among elderly patients frequently treated with Transcatheter Aortic Valve Implantation (TAVI). Traditional hemodynamic monitoring during TAVI relies on invasive methods. The ClearSight® Finger Cuff system offers a non-invasive alternative for continuous hemodynamic monitoring. To compare the reliability and feasibility of non-invasive hemodynamic monitoring with traditional invasive hemodynamic monitoring during TAVI procedures. Methods: In this prospective observational study, patients undergoing elective TAVI were recruited from two tertiary hospitals between March and August 2023. Invasive hemodynamic measurements were obtained using arterial and pigtail catheters, with a subset undergoing right heart catheterization. Non-invasive measurements were captured using the ClearSight® system. Data on baseline characteristics, procedural details, and 30-day follow-up outcomes were collected. Results: The study cohort comprised 50 patients (median age 82 years (IQR 78.0, 85.8), 50% female). Non-invasive measurements of cardiac output (CO), cardiac index (CI), and stroke volume (SV) were consistently lower than invasive measurements (CO: 4.1 vs. 4.8 L/min, p = 0.03; CI: 2.2 vs. 2.7 L/min/m2, p = 0.01, SV: 66 vs. 77 mL, p = 0.25). Non-invasive blood pressure readings were lower than invasive radial and aortic measurements before and after TAVI. Correlation of non- and invasive measurements was low but similar before and after TAVI (Mean percentage error of 52%). Conclusions: The ClearSight® system provided lower absolute values for all evaluated hemodynamic parameters as well as low correlation compared to traditional methods pre- as well as post-interventional.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".