Speckle Strain Echocardiography Predicts Outcome in Patients with Heart Failure with both Depressed and Preserved Left Ventricular Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: While speckle imaging has been shown to predict outcome in patients with heart failure (HF), it remains unclear whether speckle strain predicts outcome in patients with HF with preserved ejection fraction (HFPEF). METHODS: Four hundred twenty patients with HF by Framingham criteria and either: left ventricular (LV) EF <50%, or elevated LV filling pressure by comprehensive echo Doppler study in the setting of left ventricular ejection fraction (LVEF) ≥50%, were enrolled. Speckle tracking was used to measure strain and strain rate in multiple vectors. The primary endpoint was HF hospitalization or cardiovascular death. RESULTS: Follow-up was completed in 380/420 patients (90%). The mean age was 55.7 ± 0.8 years, 191/380 (50%) were male, 319/380 (84%) were hypertensive, 183/380 (48%) were diabetic, and 152/380 (40%) had known coronary artery disease. At a mean follow-up of 369 ± 30 days, 107/380 patients (28%) reached the primary endpoint: 97 HF rehospitalizations and 10 cardiac deaths. The best univariate predictors of outcome were global longitudinal peak strain (GLPS) (χ(2) = 25.6, P < 0.001), mitral DT (χ(2) = 16.8, P < 0.001), LVEF (χ(2) = 16.7, P < 0.0001), longitudinal early diastolic strain (χ(2) = 8.7, P = 0.003), and circumferential peak strain (χ(2) = 7.9, P = 0.005). On multivariate analysis, GLPS (P < 0.0001), LVEF (P = 0.0002), and mitral DT (P = 0.005) were independent predictors of outcome. In the 100 HF patients with preserved LVEF, there were 17 events. Patients with GPLS ≤-15 had significantly better event-free survival than patients with GPLS >-15 (χ(2) = 4.1, P = 0.04), whereas LVEF did not predict event-free survival. CONCLUSION: Speckle strain echocardiography is an important predictor of outcome in HF patients with both depressed and preserved LVEF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".