The Role of Tissue <scp>D</scp>oppler Imaging in Predicting Left Ventricular Filling Pressures in Patients Undergoing Cardiac Surgery: An Intraoperative Study
Bibliographic record
Abstract
INTRODUCTION: The perioperative management of patients undergoing cardiac surgery usually requires the accurate assessment of left ventricular filling pressures (LVFP). The gold standard for determining LVFP involves the use of pulmonary artery catheters (PAC). Using tissue Doppler indices (TDI) obtained by transthoracic echocardiography, the ratio of early transmitral filling velocity to the corresponding early mitral annular velocity (E/E') has a strong correlation with pulmonary capillary wedge pressure (PCWP). Little is known, however, on whether this relationship between E/E' and PCWP is valid intraoperatively using transesophageal echocardiography (TEE) during cardiac surgery. OBJECTIVE: The objective of our study was to determine whether TDI obtained by intraoperative TEE during cardiac surgery can accurately estimate PCWP using PAC as the gold standard. METHODS AND RESULTS: A total of 34 patients (26 males, mean age 64 ± 9 years) undergoing cardiac surgery were prospectively enrolled between 2010 and 2011 at a single tertiary care center. Conventional diastolic and tissue Doppler parameters were evaluated using intraoperative TEE with concurrent PAC monitoring before and after cardiopulmonary bypass (CPB) surgery. At both pre- and post-CPB, there was no significant correlation between lateral, septal, and mean E/E' obtained by TEE and PCWP. CONCLUSION: Intraoperative TEE was unable to accurately predict LVFP in patients undergoing cardiac surgery. PAC may continue to be the gold standard in the assessment of LVFP for this patient population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".