The Utility of Tissue Doppler Imaging for the Noninvasive Determination of Left Ventricular Filling Pressures in Patients With Septic Shock
Bibliographic record
Abstract
BACKGROUND: Pulmonary artery wedge pressure (PAWP) is an important indicator of volume status in septic patients. Although it requires invasive pulmonary artery catheterization (PAC), a noninvasive method to assess PAWP would be clinically useful in this select patient population. Diastolic indices using transthoracic echocardiography (TTE) may provide an accurate estimate of PAWP. OBJECTIVE: To determine whether echocardiographic Doppler assessment is accurate in estimating PAWP in patients with septic shock. METHODS: A retrospective chart review was performed of 320 patients admitted with a diagnosis of septic shock from 2007-2008. Of the total patient population, 40 patients fulfilled the inclusion criteria, having undergone both TTE and PAC within 4 hours. Spectral Doppler indices including peak early (E) and late (A) transmitral velocities, E/A ratio, and E-wave deceleration time were measured. Tissue Doppler indices including S', E' and A' velocities were determined. Pulmonary artery wedge pressure values measured invasively were compared to the dimensionless index of E/E' in each patient. RESULTS: The mean age was 68 +/- 12 years with 28 males (70%). On echo assessment, 28% of patients had evidence of mild left ventricular diastolic dysfunction while 17% of patients had moderate diastolic dysfunction. Pulmonary artery wedge pressures ranged from 7 to 31 mm Hg with a mean of 18 +/- 5 mm Hg. The mean E/E' was 11 +/- 8. Linear regression analysis between PAWP and E/E7apos; demonstrated a strong correlation (r = .84, P < .05). CONCLUSION: Tissue Doppler indices using TTE is a feasible and strong predictor of PAWP in patients with septic shock.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".